{"as_of":"2026-08-22T22:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:285606f09705e8c50be2fe32c3424e51829c2e66344e85f9713aba9c10e650e9","coverage":[{"denominator":57,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":57,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T22:37:00.236409Z","state":"measured"},{"denominator":57,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":57,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2606.00386/citation-record","integrity":"/paper/2606.00386/integrity","json":"/paper/2606.00386/citation-record.json","paper":"/paper/2606.00386"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Recammaster: Camera-controlled generative rendering from a single video","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:adbc4ab455774da8de597270d5fc55f1822751d4e51d2244aba33bb86f263385","observation_id":"96b6e20c-b31b-4c82-ba1a-9950eafd47ca","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Depth pro: Sharp monocular metric depth in less than a second","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:4a166cbc1f34359a69b27a909270e510bcbcae35327b2293e1362a025481af78","observation_id":"535096f4-20bd-4608-9a27-63f32843be35","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Video depth anything: Consistent depth estimation for super-long videos","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:f1853073faf727920b6eace0de7addaeacaa2011854f16b1f11620588c86f278","observation_id":"23df0b69-57fd-47d1-9b41-d6ff6859f7ff","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.00367","last_updated":"2024-06-29T08:33:55Z","snapshot_observed_at":"2026-08-16T13:38:33.700438Z","submitted_at":"2024-06-29T08:33:55Z","title":"SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix","version":1},"cited_work":{"arxiv_id":"2407.00367","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.00367","snapshot_observed_at":"2026-06-28T22:42:46.962054Z","title":"Svg: 3d stereoscopic video generation via denoising frame matrix","venue":null,"work_id":"8b46ca5b-c176-4a27-a2cc-d9ab539ce045","year":2024},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"cited_paper":"/paper/2407.00367","citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:a15ec1b1f55bf9c0bfdce5c4707bdf10f682111845f93079220240acb5d49d99","observation_id":"b2385470-06b5-4787-84f1-8fa380d5b5d2","resolution":{"observed_at":"2026-06-28T22:42:46.963567Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Boosting robustness of image matting with context assembling and strong data augmentation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:f654940ecfa9e3d9e7572f1c6e019e01a4e497fcbd2904b5cdc6f1858f001c05","observation_id":"e117046e-8878-49db-a2c1-669dedb3dc60","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Simoncelli","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:baee8107fd83614150140ccc854deee5d59bb93b4498a8ed89caaf00b471798d","observation_id":"00dcee1d-1d96-4fe9-a8af-81d0dd83d023","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Cat3d: Create anything in 3d with multi-view diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:e58fb8683535443cdb8426f28f81eeb2fc321c11fb662fb81ba3918baa9dee66","observation_id":"ba618670-1253-431d-a029-d7a07babafd5","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Generative video matting","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:e532e4eed25692810e2ec7292934ce36ddd12297d905022c4891cc2e85ed96c2","observation_id":"fca11d94-c2c6-4f81-828d-e388e0dc7bdd","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.00135","last_updated":"2025-04-30T19:06:09Z","snapshot_observed_at":"2026-08-18T18:50:02.941365Z","submitted_at":"2025-04-30T19:06:09Z","title":"Eye2Eye: A Simple Approach for Monocular-to-Stereo Video Synthesis","version":1},"cited_work":{"arxiv_id":"2505.00135","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.00135","snapshot_observed_at":"2026-06-28T22:42:46.964662Z","title":"Eye2eye: A simple approach for monocular-to-stereo video synthesis.arXiv preprint arXiv:2505.00135, 2025","venue":null,"work_id":"44784f4d-bf12-40dd-b3be-ec84500e5e05","year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"cited_paper":"/paper/2505.00135","citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:23ac9c547fdffc788327c207092cc450cfe48591d3a582e9daefc0ed60776f1e","observation_id":"5af254f8-7869-4234-85f0-74c90246c7e4","resolution":{"observed_at":"2026-06-28T22:42:46.966087Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Repurposing diffusion-based image generators for monocular depth estimation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:333a27249ea5d14df31e2426e684bfcdfd78d87c806fcf9d024e3b5f7ca8176d","observation_id":"14c4326a-8b71-4697-8837-69d05558dc43","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Modnet: Real-time trimap-free portrait matting via objective decomposition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:02391a806ba57cc8ae50e4d2738cf4b2ee046f0bf261ee464a3f5c6b7eb13d1c","observation_id":"a0f7de32-cd34-4a89-965c-9ae73cc864ff","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Zim: Zero-shot image matting for anything","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:d20ccb3974babe023e925527b99fd66b837d68f22b8c6e300cf0f4bcbb632c0f","observation_id":"11a1af3e-3401-45f4-9590-4c3fe4fd73db","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Matting anything","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:cead1f3d93c1b7a8207ee7772cb1bf59e79b1e119dce50270688d7bd9a312f8f","observation_id":"faa010a7-9073-474f-b9e6-80616dddebc2","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"PhD thesis, University of Sydney, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:280719023c4abaf225c6367cb9d20e4ad47ef96eafc89b1be96fb0eaaee3cf9b","observation_id":"e299fc20-45e9-439c-91bd-d33a0ccb70ca","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Privacy-preserving portrait matting","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:bc9053c339dab99d8e30b08867d7eab92313be4de6babfd53536c41ed0e7c9d0","observation_id":"6f523e75-b1b3-4600-9ecb-ac7b022b1b3d","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Deep automatic natural image matting","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:8366221299409a5147830b5deb9ee229d89891c49da83e3364b9d8b49d390483","observation_id":"f8b945cd-afe5-41cc-8180-2624a39cda6c","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Megadepth: Learning single-view depth prediction from internet photos","venue":null,"work_id":null,"year":2041},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:1b0be6acc4d626712e1872e3570eee1515a4e29156442e2e7b8bf72a90b579f3","observation_id":"143fcd85-6675-4b76-aa3e-35c7a674ebe9","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2511.10647","last_updated":"2025-11-13T18:59:53Z","snapshot_observed_at":"2026-08-15T23:16:19.228206Z","submitted_at":"2025-11-13T18:59:53Z","title":"Depth Anything 3: Recovering the Visual Space from Any Views","version":1},"cited_work":{"arxiv_id":"2511.10647","doi":"10.48550/arxiv.2511.10647","metadata_source":"pith","pith_arxiv_id":"2511.10647","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Depth Anything 3: Recovering the Visual Space from Any Views","venue":"cs.CV","work_id":"0a54b500-1e9d-46c2-85eb-8e16cbac8461","year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"cited_paper":"/paper/2511.10647","citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:f3f0186e023b4d183f78f59eb1b9b8a2dbe17068b8bbc8a5e08510e0884f8aa4","observation_id":"c9a0ac76-71fc-461c-9e8a-eb5ee5104031","resolution":{"observed_at":"2026-06-28T22:42:46.953620Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Robust high-resolution video matting with temporal guidance","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:99b932099a29db1ff755dbadd5e15768bee41591defc1b55e2ee88cfa36f7e90","observation_id":"86664605-a4e1-4b2d-8488-4124465bec41","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:add86985b4c254b64a375e2dc5bbfd0bcd423fc23d59be9751fe9151ef6e2307","observation_id":"5e17d30e-0610-4d3e-90d7-e50a45f9fbcd","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"What is the fractional laplacian? a comparative review with new results.Journal of Computational Physics, 404:109009, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:5f4a0cdc552f1e7fb8bd967be20784473711974a8a08a9079df41b45908521c8","observation_id":"4118b05f-5e40-490f-b9ce-464d41d91613","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Decoupled weight decay regularization","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:abb18c1020810f30cdb2812b25fb61b3eac760ef93de090d0a5e7edab7a22de4","observation_id":"d9658881-273c-40ab-8b8f-8f59fa003a37","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Stereo conversion with disparity- aware warping, compositing and inpainting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:59fd2d8f91d57911916672b1b535c81eead715795b75e56e19d3db46f6a699d8","observation_id":"404621a9-5688-4a8b-8d17-fbc2700264f6","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Elastic3d: Controllable stereo video conversion with guided latent decoding","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:04bcd8ff392e9c1c044b6ff8d30b669da6981fc0048c5d72d55ef46f39ace009","observation_id":"6f35f2af-c217-4a9e-9828-eb449a6f23b0","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Softmax splatting for video frame interpolation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:505117595fec748eb3659c262773683bb3ed659dfb43cfcf8c7f4322d3fc4663","observation_id":"0893577e-a684-49f8-87d0-906fef8f9ab4","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-17T13:03:40.359628Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":"2304.07193","doi":"10.48550/arxiv.2304.07193","metadata_source":"pith","pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv2: Learning Robust Visual Features without Supervision","venue":"cs.CV","work_id":"26b304e5-b54a-4f26-be7e-83299eca52e4","year":2023},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:d75951d1aeca6da409e3d24093b9d375fdcff6577331a5dcf7aaa9a18c4b47a4","observation_id":"68f49480-5e57-425b-8baf-7596d4d0a3fa","resolution":{"observed_at":"2026-06-28T22:42:46.951388Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Matteformer: Transformer- based image matting via prior-tokens","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:d574345fd8e686f3b56beb98d2f71ccd3270c41fcbd408d3fdf85a212c6167e0","observation_id":"0e88c19c-4b8e-4335-a0a3-f95e3ab8c0d3","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.20110","last_updated":"2025-12-18T09:32:11Z","snapshot_observed_at":"2026-08-15T12:55:42.301021Z","submitted_at":"2025-02-27T14:03:15Z","title":"UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler","version":2},"cited_work":{"arxiv_id":"2502.20110","doi":"10.48550/arxiv.2502.20110","metadata_source":"pith","pith_arxiv_id":"2502.20110","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler","venue":"cs.CV","work_id":"6454ebe4-40bc-4417-99b5-30dfb09f7a37","year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"cited_paper":"/paper/2502.20110","citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:a51ed7e0e02ebb0b490530fb69218b54cb5025c5d235517634989ea9c92e0a5a","observation_id":"3a7561e5-88ed-404f-b19e-87b24d0a3680","resolution":{"observed_at":"2026-06-28T22:42:46.958398Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Unidepth: Universal monocular metric depth estimation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:2496346cbbbc10b517106ca98f9015b8e3eff6e86060afa5ce42abb4b494728b","observation_id":"84c766ec-cb59-41bd-8ffd-1b0694bb86c6","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Attention-guided hierarchical structure aggregation for image matting","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:b3a32260f82236a90b8737e636ae767da3242870293eabb9085c5ad84a98cd78","observation_id":"9459994b-4f44-4d65-85d8-d97e4b07fee5","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Vision transformers for dense prediction","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:ce349fa1f00f286326d52702971f190efffd72e5dec136513f574732bc17d84f","observation_id":"ed2ab9a8-e257-420c-9e02-2cdb455a7e2f","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer.PAMI, 44(3):1623–1637, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:d0b8a27393821e7908f7801bf232852cae1ff236da95e4785bf6ddd1cdd4087e","observation_id":"ecbd65e9-3f66-4ce0-b556-bf102bf76064","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:1fd81f7eab9e41aeed5afef53f206156e3a33e78e403228ad414d4a424b00b58","observation_id":"86ec3799-839e-49e4-ab53-5fd43b1ffde8","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"U-net: Convolutional networks for biomedical image segmentation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:24fe289ebd1c523fa467098a480cae6ca23596952fc9c52fae02d3cbd16a8c26","observation_id":"9793ed01-9bc6-454b-9579-075887e87ab5","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2512.16915","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:42:46.947556Z","title":"arXiv preprint arXiv:2512.16915 (2025)","venue":null,"work_id":"d36fe6bb-259f-424d-8f1a-9cec2ce42f3a","year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:ee6bd093a5f22c7781611ede150b96a823904a4ae5f472db65b67b8930815c1c","observation_id":"fc595c06-7692-4cbe-9e7c-c41a3dab4486","resolution":{"observed_at":"2026-06-28T22:42:46.949125Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2505.16565","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:42:46.944671Z","title":"M2SVid: End-to-end inpainting and refinement for monocular-to-stereo video conversion","venue":null,"work_id":"41a8939e-b770-4905-a1bb-33b7d9818acc","year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:d6e121a9d1491fdac253cdc55c1eb5011738994374b8738c521e3af772541087","observation_id":"c2e60aa7-2686-45a5-864f-29bd2786e5b6","resolution":{"observed_at":"2026-06-28T22:42:46.946437Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01717","last_updated":"2019-03-27T16:43:17Z","snapshot_observed_at":"2026-08-22T10:00:20.553295Z","submitted_at":"2018-12-03T03:57:42Z","title":"Towards Accurate Generative Models of Video: A New Metric & Challenges","version":2},"cited_work":{"arxiv_id":"1812.01717","doi":"10.48550/arxiv.1812.01717","metadata_source":"pith","pith_arxiv_id":"1812.01717","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Towards Accurate Generative Models of Video: A New Metric & Challenges","venue":"cs.CV","work_id":"72f42543-17d5-49aa-ba5a-25d67ffbb88a","year":2018},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"cited_paper":"/paper/1812.01717","citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:63adeec1a92f8bbc98c625d5ed013e416b7a6dac934ceb469db2753a598b2aed","observation_id":"7e84499c-7f24-40b8-9936-8a17939a3449","resolution":{"observed_at":"2026-06-28T22:42:46.955892Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Stereodiffusion: Training-free stereo image generation using latent diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:9d9c188b2c0ed0a602879916f47a9ee63d5469777c76f253d2549e14881132ae","observation_id":"f9cffedd-0063-4ef0-812d-b47b06e0f4e4","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Moge: Unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:62acc0f1cc98aae5dd3d6db2232b3cfc3c61b378f3647aa6acc6b7a5a29c78f3","observation_id":"a4bdf93a-0dd8-4dd5-b747-efb188e45003","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Moge-2: Accurate monocular geometry with metric scale and sharp details","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:9d9cd3f4d8d8a250e251c330bb5ebf50847b1bfa93ecec0d8b3c1f7e5499322b","observation_id":"2b4e3ebf-2546-4335-997e-2b96da67ab08","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:cc27e4873718205de98a59d6dab4f2cc20e0ea74d05ba1ed5fbe1610782b410b","observation_id":"eaf4858b-eddb-4eed-9f8d-5602a7f2f7f7","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Pixel-perfect depth with semantics-prompted diffusion transformers","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:6d790381419c37e3718ec00b6965e1ec1650db37d48c22451252e236efb23b20","observation_id":"c7ad5611-856a-482f-a04b-646893f0b7ad","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Deep image matting","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:516ed8c983cc665cec58c4323ae012d3aaba421750f5f69f052d1ff85a6c9645","observation_id":"97292503-f334-4742-9761-d31e6454da64","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Depth anything: Unleashing the power of large-scale unlabeled data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:a9341d2f867f989ae1680ee9963d8bd349395c050c93add15a212ca123e11534","observation_id":"03101b83-8490-4d3a-aef8-8e2e4c492487","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Depth anything v2","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:5ab49af3f25a743e6ebcac440363170e4f7e75fc2143bad2376d17e2cfc1d418","observation_id":"2dbfc090-101a-4fe0-b2eb-396d915cd215","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Matanyone 2: Scaling video matting via a learned quality evaluator","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:a38207265447ec618aa7e228620d7cd58fd38120b6a3a27171a401c426c68248","observation_id":"c7f1adb2-b488-4355-a56d-49bdd56a3fbf","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Matanyone: Stable video matting with consistent memory propagation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:3afecf1a4ae9a5051ef61d9657817d7e0ab1fed3f25fd2f5271cb5f7e4d3204a","observation_id":"b521c6f1-eb6f-4fc4-a10d-1e9ebf137d5c","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Vitmatte: Boosting image matting with pre-trained plain vision transformers.Information Fusion, 103:102091, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:be210fe8cff64fe5974908dbd6559e2df6172ee4e5245413bed3ef2303e3cce1","observation_id":"142c9ede-89e1-4fde-ba95-bff6eb788ca3","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.00569","last_updated":"2020-03-28T08:26:57Z","snapshot_observed_at":"2026-08-20T04:50:08.671987Z","submitted_at":"2020-02-03T05:38:33Z","title":"DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data","version":3},"cited_work":{"arxiv_id":"2002.00569","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.00569","snapshot_observed_at":"2026-06-28T22:42:46.959502Z","title":"Diversedepth: Affine-invariant depth prediction using diverse data","venue":null,"work_id":"dc3a9027-21ce-4860-898b-954f2e701750","year":2002},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"cited_paper":"/paper/2002.00569","citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:4a90e71e5a759101c49dbb5e9b06bf008d945a6463880d8fac6c032c44cddf89","observation_id":"293b7ab9-3bc9-4748-a4ba-62562ff0a876","resolution":{"observed_at":"2026-06-28T22:42:46.960932Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Mono2stereo: A benchmark and empirical study for stereo conversion","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:27ae3cf56d539766b3a4c96d46710be037cef00fd2b7ca1d17519d5bbf001dfa","observation_id":"ecec508b-5d75-4f44-8405-f45b8b244030","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.02048","last_updated":"2024-09-03T16:53:19Z","snapshot_observed_at":"2026-08-14T12:22:44.567374Z","submitted_at":"2024-09-03T16:53:19Z","title":"ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis","version":1},"cited_work":{"arxiv_id":"2409.02048","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.02048","snapshot_observed_at":"2026-07-07T14:03:48.606626Z","title":"ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis","venue":"cs.CV","work_id":"af2e8736-e001-407f-b94e-21e98f89ec55","year":2024},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"cited_paper":"/paper/2409.02048","citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:e5221791e21b40dff76d37deca9ecfddbb0a0f90c7785f323a5b40621e19df6e","observation_id":"01af5929-3c9b-4afc-8010-919f86df74d1","resolution":{"observed_at":"2026-06-28T22:42:46.968501Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"The unreasonable effectiveness of deep features as a perceptual metric","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:120bb9029828ffbea17b0872a366657788e5b93fca0c3143f8e8f329bbd0d82d","observation_id":"a76db914-dd11-4ee3-872b-d1eda94cd4c5","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Betterdepth: Plug-and-play diffusion refiner for zero-shot monocular depth estimation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:d976a4d51b2f1b53bf89d73ec150cce85b0285e4f579c51eb80b20081aa4eff8","observation_id":"1403c33f-e70c-4123-b9a1-6115674c25ff","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"High-fidelity novel view synthesis via splatting-guided diffusion","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:070434789cc932b52f363b43a29637c0ef4fafd6c6b096e6f338194b8893ee26","observation_id":"3dd8869a-496b-4daf-8029-c9246c2e7d20","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Guardians of the hair: Rescuing soft boundaries in depth, stereo, and novel views","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:feec8553286ea6d5a9aeb8cd710bb2dbf626bccc2d84bdab6e30aaa679624986","observation_id":"e9282b2b-9ca8-4fa2-9854-ce8e783826eb","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.07447","last_updated":"2024-09-11T17:52:07Z","snapshot_observed_at":"2026-08-16T13:19:22.517959Z","submitted_at":"2024-09-11T17:52:07Z","title":"StereoCrafter: Diffusion-based Generation of Long and High-fidelity Stereoscopic 3D from Monocular Videos","version":1},"cited_work":{"arxiv_id":"2409.07447","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.07447","snapshot_observed_at":"2026-06-28T22:42:46.969625Z","title":"Stereocrafter: Diffusion-based generation of long and high-fidelity stereoscopic 3d from monocular videos","venue":null,"work_id":"4c90e724-9f5a-44b6-86ac-ef536a1ab294","year":2024},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"cited_paper":"/paper/2409.07447","citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:087d112d44543944a8cf41ed789ada69d98e84ecb614406fdca5ba6f0db0c189","observation_id":"61397251-12f9-498b-b8a0-05d3ad79fbce","resolution":{"observed_at":"2026-06-28T22:42:46.971179Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:37:00.236409Z","title":"Stereo magnification: learning view synthesis using multiplane images.ACM Trans","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:00.236409Z"},"links":{"citing_paper":"/paper/2606.00386"},"observation_digest":"sha256:f0b70e426c44afdfa5344026edd5ff72c1707ff0975d411c28acadbf29b6391f","observation_id":"5f6b1491-b2d4-4aac-9b3d-ca1aba0717eb","resolution":{"observed_at":"2026-06-28T22:37:00.236409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.00386","last_updated":"2026-05-29T22:00:17Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-15T12:56:26.001438Z","submitted_at":"2026-05-29T22:00:17Z","title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion"},"reference_resolution":{"displayed":57,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":46,"verified_exact":11,"verified_fuzzy":0},"total_outbound_references":57},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2606.00386."}