{"as_of":"2026-08-05T03:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5690188331788f63095b79651ed7537adc950683478c794cedbdc9135f6a4a6b","coverage":[{"denominator":79,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":79,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T16:48:35.201206Z","state":"measured"},{"denominator":79,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":79,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2604.10218/citation-record","integrity":"/paper/2604.10218/integrity","json":"/paper/2604.10218/citation-record.json","paper":"/paper/2604.10218"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Segment anything","venue":null,"work_id":"f6663857-118e-4084-98bf-bc15db36fdd2","year":2023},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:f9e8baa60de7fd9d99beb8a5746ab1e52fa05742634ff874acc96cef7b754354","observation_id":"09faf5f1-1163-4852-82c9-adc102decbc8","resolution":{"observed_at":"2026-05-17T13:29:40.934595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"CFNet: Cascade and fused cost volume for robust stereo matching","venue":null,"work_id":"5ee33857-a428-43d1-9343-5b91cb5c9b3d","year":2021},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:2c0ebc005cf761d3070187b2579c072877c83c41cf7b6214c0f082b06da4d92e","observation_id":"c1b5a3e8-5164-4339-81ab-ee92baf99ad7","resolution":{"observed_at":"2026-05-17T13:29:40.942051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Iterative geometry encoding volume for stereo matching","venue":null,"work_id":"5b2a3054-9867-47e7-a84d-7f1904b800d4","year":2023},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:fe6f7cfc77f6dce53661bb2801bf93bbe10f603497cc62693e1e90f6288b7304","observation_id":"e906f1d0-7a26-4485-944b-e5cc4e5b04a0","resolution":{"observed_at":"2026-05-17T13:29:40.930525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Practical stereo matching via cascaded recurrent network with adaptive correlation","venue":null,"work_id":"330ce7e5-e254-42b4-bf29-2ae0ac22604f","year":2022},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:773a63ab7cafa1c571a00f47a14e9b81a5ad82e09488f06f9325d602907a02c0","observation_id":"068302cd-6714-4248-8247-ba48fd58609f","resolution":{"observed_at":"2026-05-17T13:29:40.917374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"RAFT-Stereo: Multilevel recurrent field transforms for stereo matching","venue":null,"work_id":"ba24b283-c638-4ee3-ae06-70cd3b975151","year":2021},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:66bc21dc1ff3ad0e789c1ff7d6bf2569497163b11c80a38a69a4016d58787d09","observation_id":"fb3d3502-ff0d-4432-be93-0f28cb8bfb95","resolution":{"observed_at":"2026-05-17T13:29:40.921591Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"SPNet: Learning stereo matching with slanted plane aggregation","venue":null,"work_id":"3727dc34-28b7-481d-8247-2ba8853f4474","year":2022},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:ecff65a3894cec61bed924db96db67248a692a34a9bc14977b8a3ded7052df8e","observation_id":"551170e3-d167-421e-8f60-194bb1ed9d1b","resolution":{"observed_at":"2026-05-17T13:29:40.926073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Exploring fine-grained sparsity in convolutional neural networks for efficient inference","venue":null,"work_id":"7cb8a529-2b29-4062-95b4-7d88755b2821","year":2022},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:12a3f48bd891c841db0ccefd3477f4e3a65302f21dd13dbc1ef8197b09cedf7e","observation_id":"62d8f641-3a07-4ebd-a1c0-76a63b7526a9","resolution":{"observed_at":"2026-05-17T13:29:40.913148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Stereo processing by semiglobal matching and mutual information","venue":null,"work_id":"ada7d1a7-055a-4829-afc5-348ac06695fd","year":2007},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:3fcbdb7df0c941d7969929a237e7284839d89acab81accb4b20428b20568e796","observation_id":"21c045ef-cc44-486e-afd9-05f59bc0354b","resolution":{"observed_at":"2026-05-17T13:29:40.938256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Open challenges in deep stereo: the booster dataset","venue":null,"work_id":"5ac71d53-9728-4b46-aa15-cc8a9c00785a","year":2022},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:9a902d5fb554809432f2a30c9b0e1b66c5f8471a38130581633fb3cc34ecaca1","observation_id":"d097ad43-3106-4009-8db1-9eec83e063a1","resolution":{"observed_at":"2026-05-17T13:29:40.873121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Par- allax attention for unsupervised stereo correspondence learning","venue":null,"work_id":"89b8d31e-bc81-499f-8945-e049c6e147be","year":2020},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:a0e72d778008bd07026c4576f441e341dfab94783838a94670629621dbf1dfcb","observation_id":"97da649a-2367-4bf2-b934-774961738b68","resolution":{"observed_at":"2026-05-17T13:29:40.876079Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Flow2stereo: Effective self- supervised learning of optical flow and stereo matching","venue":null,"work_id":"b8db1623-ea1e-4cae-b62b-154ac16d05ac","year":2020},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:f8baff94866013b505f13fdc6d3f620e76a9cdab16e598dc042b00f1e72a1fa5","observation_id":"2a27cdc2-5b4a-434b-b90a-e235eabc90d8","resolution":{"observed_at":"2026-05-17T13:29:40.867042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dispsegnet: Leveraging semantics for end-to-end learning of disparity estimation from stereo imagery","venue":null,"work_id":"2fd8baef-a2c8-423c-a206-28cbda1d11c4","year":2019},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:ed1781d656c8b0d5e92ee93955520d12bf7c1a6cccdeb5e8d4a24935baf75600","observation_id":"1f48b845-278d-4422-a445-0a7b3b64ccc9","resolution":{"observed_at":"2026-05-17T13:29:40.870228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"DINOv2: Learning robust visual features without supervision","venue":null,"work_id":"3c69b03a-c3b2-42e9-b40d-55812917dc53","year":2024},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:823605615e7218edd68c325fdaa3f4b5f442b2477a9db32b97be424b24ff125b","observation_id":"6cfc6dbe-1fc3-4ea7-98ee-da388e1eccc9","resolution":{"observed_at":"2026-05-17T13:29:40.878947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.09414","last_updated":"2024-10-20T11:24:09Z","snapshot_observed_at":"2026-07-06T18:30:32.982860Z","submitted_at":"2024-06-13T17:59:56Z","title":"Depth Anything V2","version":2},"cited_work":{"arxiv_id":"2406.09414","doi":"10.48550/arxiv.2406.09414","metadata_source":"pith","pith_arxiv_id":"2406.09414","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Depth Anything V2","venue":"cs.CV","work_id":"5f5274d6-f7a5-4598-a3f6-d11f44520a2e","year":2024},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"cited_paper":"/paper/2406.09414","citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:0462b0daa8f8e885afb47b905e1dfd29f3c4a2523ebb2b578466b438a5569dcc","observation_id":"81177f96-3b4e-4eac-8bd5-b91cd75516b9","resolution":{"observed_at":"2026-05-13T14:56:34.521820Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.11331","last_updated":"2023-03-22T14:10:37Z","snapshot_observed_at":"2026-08-04T19:06:24.411991Z","submitted_at":"2023-03-20T17:59:59Z","title":"EVA-02: A Visual Representation for Neon Genesis","version":2},"cited_work":{"arxiv_id":"2303.11331","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.11331","snapshot_observed_at":"2026-07-05T11:41:02.797019Z","title":"Eva-02: A visual representation for neon genesis","venue":"cs.CV","work_id":"bf537885-a22b-4675-854f-7f42dfb687a7","year":2023},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"cited_paper":"/paper/2303.11331","citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:7bd8da016896a220ba70c004c9ad38ab3006a86e3bbba109197a03e3acd28a33","observation_id":"bd5d012f-cca4-45c9-b515-fc5d23c8908d","resolution":{"observed_at":"2026-05-11T08:10:59.367242Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dust3r: Geometric 3d vision made easy","venue":null,"work_id":"1871de68-07c5-4862-b2e4-3c0f8f658719","year":2024},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:2a42a3a2349046549fc651ba01000b8511492fcc977aa010b9bd85911883196a","observation_id":"1adbc923-11ac-4852-a724-6041a5e8f107","resolution":{"observed_at":"2026-05-17T13:29:40.881889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.06261","last_updated":"2024-04-09T12:34:28Z","snapshot_observed_at":"2026-08-03T18:35:34.869337Z","submitted_at":"2024-04-09T12:34:28Z","title":"Playing to Vision Foundation Model's Strengths in Stereo Matching","version":1},"cited_work":{"arxiv_id":"2404.06261","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.06261","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Playing to vision foundation model’s strengths in stereo matching","venue":null,"work_id":"615b7b60-6755-48f5-b597-91444b1f7b59","year":2024},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"cited_paper":"/paper/2404.06261","citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:3e68997fce69ee47225198ea90bff790a73ccc2b7f7e476ef0a12fff76aa0bab","observation_id":"92dd82cb-ae41-40ff-831c-0a3b24f6f13c","resolution":{"observed_at":"2026-05-11T08:10:59.355344Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning representa- tions from foundation models for domain generalized stereo matching","venue":null,"work_id":"ca37d61e-453a-4f29-9afe-1fa8d5885aab","year":2025},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:355e32cc24fa1941de483daf32bff8b25d05cb2273e10e861c56eb8c9bbefade","observation_id":"1c83289e-a2ab-4975-831c-e695492a8451","resolution":{"observed_at":"2026-05-17T13:29:40.887808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Finetune like you pretrain: Improved finetuning of zero-shot vision models","venue":null,"work_id":"e84d8077-0325-438c-ba60-9d61d4f2f38a","year":2023},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:946e174cef15d75a840f88cb30355d99a14d248773650f5409ac82d1414cc1b5","observation_id":"9b3cce22-29cc-42ce-8ef1-18a54f10edca","resolution":{"observed_at":"2026-05-17T13:29:40.884765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.08252","last_updated":"2024-06-10T15:11:40Z","snapshot_observed_at":"2026-07-06T15:27:02.376191Z","submitted_at":"2023-05-14T21:18:18Z","title":"Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity","version":4},"cited_work":{"arxiv_id":"2305.08252","doi":"10.48550/arxiv.2305.08252","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.08252","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Parameter-efficient fine-tuning for medical image analysis: The missed opportunity","venue":"arXiv (Cornell University)","work_id":"a14faa6d-8eee-4fc2-b116-7c2752ea49aa","year":2023},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"cited_paper":"/paper/2305.08252","citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:3c9e46aa46cd80e16169b8a8515d4c3970df55c010870f8ee4c0be4e492b9784","observation_id":"44c66160-73ff-47da-92e5-3516aa2ee4ea","resolution":{"observed_at":"2026-05-11T08:10:59.336777Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Croco v2: Improved cross-view completion pre-training for stereo matching and optical flow","venue":null,"work_id":"7a1a21bc-8ac9-4388-a0d3-244dd77df6a6","year":2023},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:bb4750110796bcc6d5b2baf1ecbd453599fe3c2e39c2938c2eacff0a6dbe75bf","observation_id":"848ee70a-633c-4d61-a6e3-c49aad5abc83","resolution":{"observed_at":"2026-05-17T13:29:40.849247Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cost vol- ume aggregation in stereo matching revisited: A disparity classification perspective","venue":null,"work_id":"5fdc462a-a2a5-4b25-872a-6afc10a0c8a7","year":2024},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:29e30cd2d1a3a97b16d7d650c2365f399f4a1fbbb22b7ef5f4c0cc33757c7492","observation_id":"67701be7-4155-428c-9e3a-cb98907f47db","resolution":{"observed_at":"2026-05-17T13:29:40.852167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep stereo matching with hysteresis attention and supervised cost volume construction","venue":null,"work_id":"84182f56-cd2d-48ff-afe4-b0ca9f8e4083","year":2021},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:9cecfacd6b3e300752916d1048593d0f934469fa27135a815607229f44fcd0a8","observation_id":"2ee0db49-68a0-4d5f-bca3-bfdb53c0ad86","resolution":{"observed_at":"2026-05-17T13:29:40.839696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Active disparity sampling for stereo matching with adjoint network","venue":null,"work_id":"1e54e58a-81b4-4938-b800-4fa8b9b5649c","year":2023},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:deb01654cea2250a37102ba4a1a2deec6c9ed22960f600f0614226a7238a705f","observation_id":"965f46e9-295f-40d5-9b45-716589c5cadd","resolution":{"observed_at":"2026-05-17T13:29:40.846057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.00486","last_updated":"2024-03-01T12:13:20Z","snapshot_observed_at":"2026-08-04T20:02:14.623006Z","submitted_at":"2024-03-01T12:13:20Z","title":"Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching","version":1},"cited_work":{"arxiv_id":"2403.00486","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.00486","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Selective-stereo: Adaptive frequency information selection for stereo matching","venue":null,"work_id":"5fcd4e6a-7cf4-4000-a142-e5edc4e1ac9b","year":2024},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"cited_paper":"/paper/2403.00486","citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:d9efa4559268d83531c4b8530262d48ac3b3bbce8288faeaf5a27429e368de6e","observation_id":"a8281baf-5726-43a1-bd74-be3832674898","resolution":{"observed_at":"2026-05-11T08:10:59.285886Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Defom-stereo: Depth foundation model based stereo matching","venue":null,"work_id":"5ddd3553-fa43-4067-ae23-c92324814c06","year":2025},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:48ed86ae364e8decff39a31332b51fb10bb439c9b680e3f79c7624a157a3ee95","observation_id":"773be120-f22b-4f69-86ba-5e0dce8f0713","resolution":{"observed_at":"2026-05-17T13:29:40.833414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Foundationstereo: Zero-shot stereo matching","venue":null,"work_id":"625b5c9a-4eec-41fd-a7b8-cf92d311191f","year":2025},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:c7c7f48052f4504c3044e39b973d702dd2eb950192ea5323a288e6d3dce1959f","observation_id":"4bdc1442-4abb-4a2f-9150-e0d258e070df","resolution":{"observed_at":"2026-05-17T13:29:40.890684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"All-in-one: Transferring vision foundation models into stereo matching","venue":null,"work_id":"08ed5acf-260a-4278-a109-d724c713544f","year":2025},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:be7417bd07a63f6fe1b0b7a910b6d4c8fa39fdc00b952b9e9e423823b5ffa97e","observation_id":"4bbbe57f-3c31-44e4-ae40-ce53a7f3e805","resolution":{"observed_at":"2026-05-17T13:29:40.827723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning robust stereo matching in the wild with selective mixture-of-experts","venue":null,"work_id":"2803dfcb-956c-47b1-912a-4971777e3481","year":2025},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:a5bb4ee0f4bc3fed36641ca617f5d3420b7b9fbc847ede0db6a404a79cc7fd17","observation_id":"7e8f4445-35f7-43c7-90f0-0eafc6be36d6","resolution":{"observed_at":"2026-05-17T13:29:40.830439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1709.00930","last_updated":"2017-09-04T12:56:18Z","snapshot_observed_at":"2026-07-06T05:57:59.144686Z","submitted_at":"2017-09-04T12:56:18Z","title":"Self-Supervised Learning for Stereo Matching with Self-Improving Ability","version":1},"cited_work":{"arxiv_id":"1709.00930","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1709.00930","snapshot_observed_at":"2026-07-04T22:10:43.321337Z","title":"Self-supervised learning for stereo matching with self-improving ability","venue":null,"work_id":"7db54e8b-7dc1-48b0-8cad-00ac849ba25b","year":2017},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"cited_paper":"/paper/1709.00930","citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:53a01ce95a55cd2fdb14565867e1d55cda707c9cece9f09a590818541f9f58e9","observation_id":"5906805f-1455-41b0-8208-818071a420c9","resolution":{"observed_at":"2026-07-04T22:10:43.321337Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Unos: Uni- fied unsupervised optical-flow and stereo-depth estimation by watching videos","venue":null,"work_id":"0bd7ddfe-ceca-4323-90cb-5ada4f07d9d2","year":2019},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:00f504d059ef6f2c8a9c947bb882f71e810191d3663cdad9a74c7034eda76a30","observation_id":"d6f9571b-5fba-4842-9b4a-da0cf4ea7a1c","resolution":{"observed_at":"2026-05-17T13:29:40.836453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Unsupervised occlusion-aware stereo matching with directed disparity smoothing","venue":null,"work_id":"32e58877-4278-420b-b3b4-2207f54b9713","year":2021},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:ba893bf9bfb36c5b570a058490ae9a632263185aa3a0950e62e91cade0a6fe18","observation_id":"26b89606-4b50-47db-bc1c-72ec41e16733","resolution":{"observed_at":"2026-05-17T13:29:40.842892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Revealing the reciprocal relations between self-supervised stereo and monocular depth estimation","venue":null,"work_id":"9f1d10b3-ffef-43f8-9bc5-05f073f6c7bd","year":2021},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:032277291ead177c1d5799f444dc3ae5f9750aacad65a56ae614a953d026afdc","observation_id":"307cd110-3ef9-4f36-b64b-c34821252d30","resolution":{"observed_at":"2026-05-17T13:29:40.857673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Chitransformer: Towards reliable stereo from cues","venue":null,"work_id":"2ad2a48c-8179-4d8e-8e56-55076007ff7a","year":1939},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:f362f33c780a95be7b3e9a1c48a3f0d72e01544a204cbcc937582a5e1c2f6177","observation_id":"b91e5d4e-475c-47e0-832e-a34e78714dc9","resolution":{"observed_at":"2026-05-17T13:29:40.808269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":"2010.11929","doi":"10.1175/jcli-d-22-0357.1","metadata_source":"pith","pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","venue":"cs.CV","work_id":"e96730e3-129b-4db6-b981-15ab7932e297","year":2020},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:cba0ace13440d65e5baca039693612b6ad7e56595e5ecb0d61e59f71ff85acc3","observation_id":"152af64e-e57d-4e82-b950-df682dbd6093","resolution":{"observed_at":"2026-05-11T08:10:59.518347Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Nerf-supervised deep stereo","venue":null,"work_id":"aa11d7de-0a7a-48f5-bc85-4d6a18ba3b6b","year":2023},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:48ace9be9560c0b2a2d521f1b1e619ffcc55d89b15f23af3bbde7abb413213bd","observation_id":"b63a76ae-16ce-4d4e-a4c9-511c789313d2","resolution":{"observed_at":"2026-05-17T13:29:40.824387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Self-supervised multi- view stereo via effective co-segmentation and data-augmentation","venue":null,"work_id":"d660fbdc-4d04-4d91-8496-9390603d3a6a","year":2021},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:9fa4ec042ed4f0f44a6ada36b3ad22a50a9f3b78b3c5076219a456466fc10274","observation_id":"91215a8a-161c-4cb7-b051-c91a4c0c86b9","resolution":{"observed_at":"2026-05-17T13:29:40.797640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Rc-mvsnet: Unsupervised multi-view stereo with neu- 14 ral rendering","venue":null,"work_id":"09f6e3cb-6d72-4214-890b-dfabbd935e62","year":2022},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:c4f40d978da95acdc4f1b0e7837ee2622951405dd91aff64ec802dab756d26d2","observation_id":"831ab215-0e10-4ded-ad19-0a3ae11c1593","resolution":{"observed_at":"2026-05-17T13:29:40.893677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dualnet: Robust self-supervised stereo matching with pseudo-label supervision","venue":null,"work_id":"ebd937e2-6df1-49ef-990e-41329fbb025c","year":2025},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:53a9cdf144657880af7fba499ff0131fc5e314cb6fb4cc289f37b9b429136d3e","observation_id":"875263ee-f261-4038-a2d9-2b6a78dd65ba","resolution":{"observed_at":"2026-05-17T13:29:40.801202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Rose: Robust self-supervised stereo matching under adverse weather condi- tions","venue":null,"work_id":"68c3ebcb-7d7e-43ce-a914-6d094d8c3a2c","year":2025},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:7e248052816368ccde0e3fec67c5bbfc95acb0037790fc837b9906f070ffdd5b","observation_id":"92aae15b-4e8e-42a5-97a2-8f3ced7fdc2d","resolution":{"observed_at":"2026-05-17T13:29:40.815181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Pyramid stereo matching network","venue":null,"work_id":"d0887246-126b-45a1-8401-e2d800d69491","year":2018},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:ace8f970b9ccf29ab4485bbbd231cd4ecf9857675d394d65f4a1c0d392987aeb","observation_id":"aeb3e560-ccc2-4421-8127-f426a67d9922","resolution":{"observed_at":"2026-05-17T13:29:40.819495Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Pcw-net: Pyramid combination and warping cost volume for stereo matching","venue":null,"work_id":"cd3088e7-c63b-4bf0-8ea5-96a366bef827","year":2022},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:82a35674bea1c772df9481541f5c6e1a39800caa7588f811141e03f293f8a07f","observation_id":"2fa3a719-65b7-4406-baa8-32157c44ec4c","resolution":{"observed_at":"2026-05-17T13:29:40.860303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cvcnet: Learning cost volume compression for efficient stereo matching","venue":null,"work_id":"9defcc1f-2bc1-4427-b031-c3da65f24dd8","year":2022},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:de5e87fb6ac79888860be56880697a27b8283595ee0d0eff2421ba417f3c11bf","observation_id":"cf9209fd-b824-4df5-a457-ad8c4e06a1bd","resolution":{"observed_at":"2026-05-17T13:29:40.805077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Adstereo: Efficient stereo matching with adaptive downsampling and disparity alignment","venue":null,"work_id":"6c25c7d3-ef9a-4338-87a2-af3f79bf675b","year":2025},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:162f029eb023e0724411c37487ba87b3a799696910fb2c2d5ab95031f6338001","observation_id":"82f40b6e-c3b2-4e94-b40b-dbe24b83f20e","resolution":{"observed_at":"2026-05-17T13:29:40.901792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"AANet: Adaptive aggregation network for efficient stereo matching","venue":null,"work_id":"27b49042-c717-466c-b0ae-86a747291315","year":1959},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:20513b08967150272fe85756bbc533d1d3c33093da3485048d3ffde61114253b","observation_id":"e7569849-8a69-452b-a52c-e9f64b6ba33b","resolution":{"observed_at":"2026-05-17T13:29:40.784989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hda-net: Horizontal deformable attention network for stereo matching","venue":null,"work_id":"ffa57587-3cd2-4e64-b00c-562f484a030b","year":2021},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:9677dc308e56908cb8273536f8f9caa447b578739179767db6b6b874f4091fc3","observation_id":"1a3beb75-c5ea-4f21-8349-70c75a886950","resolution":{"observed_at":"2026-05-17T13:29:40.897634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"High- frequency stereo matching network","venue":null,"work_id":"0e619179-fa65-4224-b0a5-503160d8632a","year":2023},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:e1fcc81e190f53d3ac7fd3087f470a7b6d44478ce5446afdd029da947bb56c53","observation_id":"c5a702a1-792d-4e49-8514-8f0920253585","resolution":{"observed_at":"2026-05-17T13:29:40.905582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Depth anything: Unleashing the power of large-scale unlabeled data","venue":null,"work_id":"c2178397-6432-4c17-a529-59bb312ccf67","year":2024},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:4b6f1a8176f369e5b2af1b6c5e4ec0f251abaa5882b2f7d4b2acd3a189cac116","observation_id":"2a984078-87c4-40ed-a6e7-ec563ef56c9e","resolution":{"observed_at":"2026-05-17T13:29:40.781917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Momentum contrast for unsupervised visual representation learning","venue":null,"work_id":"dae4202e-f20a-4379-9649-2dc1fa76b4ed","year":2020},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:58d40a6ce0737199a821a5ce01cc8aa0ff5946fa95d5f5f16eafbaf7ca74c88f","observation_id":"34b7531c-4314-4462-b785-31d6a3b9aa76","resolution":{"observed_at":"2026-05-17T13:29:40.788693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Exploring simple siamese representation learning","venue":null,"work_id":"5856f7b2-c4f5-49ff-bf5e-39338462f778","year":2021},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:cd8c73b5db6045e6909fad693b4ba8574a46813387d028986520b6336390fca4","observation_id":"ab7c7ba5-da3e-45b8-9d49-46322d1496a5","resolution":{"observed_at":"2026-05-17T13:29:40.794552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Contrastive learning with stronger augmenta- tions","venue":null,"work_id":"53c8facb-77b1-4b53-b690-7777cc5f639b","year":2022},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:4b7666174d62e096c917383f89455095dec8db395debf839953568ec1cdd8d0b","observation_id":"6bee10ce-f319-44f0-82eb-e3f1679a1eaa","resolution":{"observed_at":"2026-05-17T13:29:40.775781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.04297","last_updated":"2020-03-09T17:56:49Z","snapshot_observed_at":"2026-07-06T09:03:25.467987Z","submitted_at":"2020-03-09T17:56:49Z","title":"Improved Baselines with Momentum Contrastive Learning","version":1},"cited_work":{"arxiv_id":"2003.04297","doi":"10.48550/arxiv.2003.04297","metadata_source":"pith","pith_arxiv_id":"2003.04297","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Improved Baselines with Momentum Contrastive Learning","venue":"cs.CV","work_id":"f275e715-bcdc-487c-bc31-6f98d8a01f5c","year":2020},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"cited_paper":"/paper/2003.04297","citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:6644c33700ffd905b9acb8305e4076d3ede859ef7e44c6b4f4944b7e364fead2","observation_id":"442b2083-6e78-42f9-99da-2eb3ee42146d","resolution":{"observed_at":"2026-05-13T15:39:24.627430Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning","venue":null,"work_id":"9731803e-11f2-406b-9b0c-eda70695b6f0","year":2021},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:4417ece761058cbefa2e7ba1e3bee74342128adbf2c1c400f1efc327ead6cbb2","observation_id":"3adde7a1-9c45-411b-9708-cf217f477f70","resolution":{"observed_at":"2026-05-17T13:29:40.769457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Revisiting domain generalized stereo matching networks from a feature consistency perspective","venue":null,"work_id":"f2cf4e10-2fcf-4a4e-b958-3b3af04d5c60","year":2022},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:44d2bfb9612c5f65f451b5d05dd57325ba308fe7a3cacb2f0bfa8e91d284aa7e","observation_id":"a3f7c6f4-41e6-48e2-aaef-af80cab758da","resolution":{"observed_at":"2026-05-17T13:29:40.772894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Image quality assessment: from error visibility to structural similarity","venue":null,"work_id":"2638ff19-8e90-45e0-a0fd-d6e67faed053","year":2004},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:64dabd00ab1ace46bbed5dd7f3dc78e54e8418c887e75acfd30b2358de07e94d","observation_id":"37d14dd3-5a9a-46f6-867a-b8582de76f11","resolution":{"observed_at":"2026-05-17T13:29:40.863369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Sense: Self-evolving learning for self-supervised monocular depth estimation","venue":null,"work_id":"62807732-69c2-49c0-9ce0-21443ab9b7c8","year":2023},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:9b67faf96280942f29a195067dee2c814ab7c32e05fbe8f3971413442d3461e5","observation_id":"f132caf5-3d0b-4f23-bd8b-97cb981eaf50","resolution":{"observed_at":"2026-05-17T13:29:40.756997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Masked autoencoders are scalable vision learners","venue":null,"work_id":"e9df4f05-1d9a-4f89-a7bc-a32bca52a5b1","year":2022},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:16fa6211a342471dd740df0480433ed8cb5a03c6f22dd66ead58844f2d361125","observation_id":"3ee6c1be-454d-4c60-aa65-b9a9f77ea57e","resolution":{"observed_at":"2026-05-17T13:29:40.763067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Masked representation learning for domain generalized stereo matching","venue":null,"work_id":"81ad7b91-669b-407e-a9a0-462146f0d494","year":2023},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:f9a2cf03b79595f73e26213b3c441322b19e17eb23f409d42c4aefb26380a4ee","observation_id":"f4901a3a-b62d-4b36-8053-98b9d948b890","resolution":{"observed_at":"2026-05-17T13:29:40.713473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Faster r-cnn: Towards real- time object detection with region proposal networks","venue":null,"work_id":"9b51f5b0-9786-4d67-b646-429478095c9b","year":2016},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:f3c38279832f81baabf802ec79875ed6597bb8adf5485f114d20883aca221b55","observation_id":"2248d31c-49a8-4151-b339-95cacf0a94ea","resolution":{"observed_at":"2026-05-17T13:29:40.716854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Kd- mvs: Knowledge distillation based self-supervised learning for multi- view stereo","venue":null,"work_id":"def57a9e-f9b2-460d-ac35-5cd3173657de","year":2022},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:2bc595b7393a1a7a8ff54e5b167f2ec41f61cc10fdff5af1939b8e8cb1ca44de","observation_id":"19ba8ede-9515-4e73-9841-9695c1e13faf","resolution":{"observed_at":"2026-05-17T13:29:40.760241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Flownet: Learning optical flow with convolutional networks","venue":null,"work_id":"2ad72084-da25-49ca-b869-4b7a79708778","year":2015},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:5468efea4326255f8af924eac56d372f395289c9d9b40a26e6f059b25f9c412f","observation_id":"f5924eef-60ce-4272-86e4-59fcb49f8547","resolution":{"observed_at":"2026-05-17T13:29:40.766229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Are we ready for autonomous driving? the KITTI vision benchmark suite","venue":null,"work_id":"a05c2e60-b235-488d-9711-6bf4ba8a2d83","year":2012},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:60674e961e3a9d88c9c0ddb51b0a60d7838e339939ec9fc4c93138de4465670c","observation_id":"fcf82c5c-ba2c-4725-aabc-add2064ff577","resolution":{"observed_at":"2026-05-17T13:29:40.778753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T21:37:45.225198Z","title":"Object scene flow for autonomous vehicles","venue":null,"work_id":"198c357a-c661-4274-8cfe-aab94c094273","year":2015},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:edd5bf382f7ce8080d52536cd29dd7fdceb45f7a47b01a0a876b588fa6db6e6f","observation_id":"c04fcadc-e882-4faa-ab60-19da404024d5","resolution":{"observed_at":"2026-05-17T13:29:40.791666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"End-to-end learning of geometry and context for deep stereo regression","venue":null,"work_id":"740afcf3-7467-471f-a9d0-95c0c1eaad5e","year":2017},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:db2653993ae4b2556d9d0462fedd7a46e5f43eedb6757aa2dd8042c792261893","observation_id":"363020b2-e9ec-44aa-b90e-0da1f27eaca2","resolution":{"observed_at":"2026-05-17T13:29:40.811717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mabnet: a lightweight stereo network based on multibranch adjustable bottleneck module","venue":null,"work_id":"352dbf30-f11a-4b27-bba7-326d1dc16782","year":2020},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:8f731a2601a0f609d568779bf58212c47b8912b5ab9dc708d9c9bb7e07ece825","observation_id":"144ca9b7-5335-4e16-b0fc-dcc31c1d1112","resolution":{"observed_at":"2026-05-17T13:29:40.741523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Sgm-nets: Semi-global matching with neural networks","venue":null,"work_id":"14ce1eea-0113-481b-9727-d5bc3799d035","year":2017},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:5dfabbddba4e5342a5ed7021d6a0ee7502c5db365e51c080ec3ae2e51722cf7a","observation_id":"221b4d69-43c1-4693-aaec-df846091f67e","resolution":{"observed_at":"2026-05-17T13:29:40.744689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Occlusion aware stereo matching via cooperative un- supervised learning","venue":null,"work_id":"09d53520-e956-4562-8b17-c5e3fc48c863","year":2018},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:ae78584256e9f25e5ba92fe4f549dd7382edcea40dc4773d9a3f5453424a5ae8","observation_id":"6df805f0-79f4-44e8-b7f5-174967cb5eb7","resolution":{"observed_at":"2026-05-17T13:29:40.750538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Digging into uncertainty-based pseudo-label for robust stereo matching","venue":null,"work_id":"b2da357a-43c9-4e7b-b858-2cab816f3f4f","year":2023},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:325c49a75fbcfdecc94a826807fbf2f578accf535b1f17bb340e3699efec38f7","observation_id":"3de8e4b0-745a-406d-9d30-79f9905100ea","resolution":{"observed_at":"2026-05-17T13:29:40.747753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Los: Local structure-guided stereo matching","venue":null,"work_id":"7273dca1-9aee-4b42-aab1-3d5849eb9a52","year":2024},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:caa77522a5400671247ad00b202e122e423d1feb9fc51017eaa7beb8e552be47","observation_id":"7d9654d6-b2e9-45b7-a6e4-af8e74affe09","resolution":{"observed_at":"2026-05-17T13:29:40.735268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mocha-stereo: Motif channel attention network for stereo matching","venue":null,"work_id":"4f718026-eff9-4518-a4c8-ea5b6fff7df7","year":2024},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:c156c629da28cc6a2d6eadfcdcce1bc9432bbfab4dece4a3eb7090e1e7946f9b","observation_id":"8dd2af49-c207-436e-a23d-fa0541aefe12","resolution":{"observed_at":"2026-05-17T13:29:40.909099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neural markov random field for stereo matching","venue":null,"work_id":"95a4cfcc-d75e-468b-a500-f30ddfac329e","year":2024},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:01ae2f2d5ee517f4d722e91683efb773c6a0d699d472ef9920a21c31453c95ae","observation_id":"e169f644-1cfa-43b7-8044-18117ed31c01","resolution":{"observed_at":"2026-05-17T13:29:40.725675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"High-resolution stereo datasets with subpixel-accurate ground truth","venue":null,"work_id":"a6540340-132a-407b-bb93-54a53d2b711a","year":2014},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:1b1aaaac87e1e40abd8aa2142f13f2b2bcd3a0893da61c14f08692315793f80e","observation_id":"2eec733f-eddd-41aa-ae56-d6c2135c8787","resolution":{"observed_at":"2026-05-17T13:29:40.729058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A multi-view stereo benchmark with high-resolution images and multi-camera videos","venue":null,"work_id":"bab5891c-6636-4cd5-8ba5-fc4aba2e17d6","year":2017},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:109cc061d43ed5bfffecffbddc92b25de239c1af8fc5e2802b56817d79018275","observation_id":"34bdf184-af95-425e-aed7-5ad51a2b0776","resolution":{"observed_at":"2026-05-17T13:29:40.732118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Attention concatenation volume for accurate and efficient stereo matching","venue":null,"work_id":"3341ea00-2456-491f-bfcd-1beb10439cda","year":2022},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:fe12e9aabcfc0b500bfaaa7846a838c0347fd735630e9d72122528dd701ea42f","observation_id":"1167a627-2fbf-46c1-b6c8-60477e71e499","resolution":{"observed_at":"2026-05-17T13:29:40.722875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Unambiguous pyramid cost volumes fusion for stereo matching","venue":null,"work_id":"409f1208-5933-4d65-a75b-3dd3df4d16e9","year":2023},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:568f67032789b9a36e0d1c50342bbb3aa18f1a86b690ecca35ee59e92ed08047","observation_id":"b9e9822f-9073-415c-9526-2ac4bede4514","resolution":{"observed_at":"2026-05-17T13:29:40.738370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":"16287787-8b58-411d-ac8a-ed7ef32103cd","year":2021},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:5b498e45322a23a84eb074bdd07421007144ead76a51cd0c8d68ce6e090f9882","observation_id":"680c3a2c-05b6-47d2-96bd-69c3e45b9c31","resolution":{"observed_at":"2026-05-17T13:29:40.753893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"DeepDriving: Learning affordance for direct perception in autonomous driving","venue":null,"work_id":"7a5316f1-ac62-4c6d-8c93-7a67d25de44e","year":2015},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:3ebb1f4a19d9a19bbe7312cd67780448c0d062fe29fe0eeaa386f8783c89409c","observation_id":"34b0441f-94df-4781-8016-135bd3c49dba","resolution":{"observed_at":"2026-05-17T13:29:40.719929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Open challenges in deep stereo: the booster dataset","venue":null,"work_id":"b66c8601-b51e-417e-b3c3-1b8e0f51f240","year":2022},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:0b3ac1e6b0e05a2c78c58dde5a4b99a0c5a848a6ce9151e40408ceaa566708d9","observation_id":"18046f4f-c1dd-4c67-9168-0a2bb735d257","resolution":{"observed_at":"2026-05-17T13:29:40.854899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.10773","last_updated":"2020-01-29T12:13:20Z","snapshot_observed_at":"2026-07-06T08:53:28.193420Z","submitted_at":"2020-01-29T12:13:20Z","title":"Virtual KITTI 2","version":1},"cited_work":{"arxiv_id":"2001.10773","doi":null,"metadata_source":"pith","pith_arxiv_id":"2001.10773","snapshot_observed_at":"2026-07-07T22:24:11.153404Z","title":"Virtual KITTI 2","venue":"cs.CV","work_id":"c0d9c030-aa25-44e7-9cc4-72d7403f1447","year":2020},"citing_paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-05-10T16:48:35.201206Z"},"links":{"cited_paper":"/paper/2001.10773","citing_paper":"/paper/2604.10218"},"observation_digest":"sha256:2f735a915034050de7707935aa5bd8fc24231043498b83b97054ee37093cadef","observation_id":"c445b8ae-75ed-4816-85d8-bdf18d39fda7","resolution":{"observed_at":"2026-05-13T16:00:33.810478Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.10218","last_updated":"2026-04-11T13:56:41Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-02T11:47:58.134223Z","submitted_at":"2026-04-11T13:56:41Z","title":"SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation"},"reference_resolution":{"displayed":79,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":9,"verified_fuzzy":70},"total_outbound_references":79},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 0 inbound Pith citation observations for arXiv:2604.10218."}