{"as_of":"2026-08-21T01:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1f95cf35dd93db0512c11c309c21c94a1ce5fc78e38a5e6f11a7f84f6f4df583","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T23:47:44.970772Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2412.19964/citation-record","integrity":"/paper/2412.19964/integrity","json":"/paper/2412.19964/citation-record.json","paper":"/paper/2412.19964"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:47:45.353178Z","title":"Multiview depth estimation by fusing single-view depth probability with multi-view geometry","venue":null,"work_id":"284e0421-9cd0-43e5-864e-8544362bb661","year":2022},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.861255Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:5f5ac862c26aa029e7cfe7064bfba25b9872eeedc49744fbac27e9a73cac131a","observation_id":"3e4426b8-2732-4a30-9e3f-eecd8241368d","resolution":{"observed_at":"2026-08-10T23:47:45.357489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.340553Z","title":"Adabins: Depth estimation using adaptive bins","venue":null,"work_id":"5ed8833d-82e9-4480-9892-401a8438276d","year":2021},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.866737Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:b2e76ade29821e6dea3b169f69b588b80045976f160dd54404573def3c263118","observation_id":"cf78b815-215f-47df-83f3-a4818191fc61","resolution":{"observed_at":"2026-08-10T23:47:45.344919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.379643Z","title":null,"venue":null,"work_id":"db0f9022-d61a-4d5d-95f2-50e1de740a75","year":null},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.850481Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:ec5455dbd112cdf9d792d24104431310037afcd89089d24059db032461ec02d4","observation_id":"6b398b9e-a6ab-4d90-a907-4286eee1aeeb","resolution":{"observed_at":"2026-08-10T23:47:45.384065Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.366092Z","title":"Two prominent datasets in this field are the KITTI and DDAD datasets, each providing valuable resources for developing and benchmarking stereo vision systems","venue":null,"work_id":"abcf2c4e-2d09-49b7-8822-9af8800b3288","year":1936},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.856025Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:41747f5af952f7dd6c1f604809f30597dc5f8bc8e4ac5dbe6e4bb5612b1408b3","observation_id":"bd56929b-ef1a-4500-a1d2-7a1a093b65fd","resolution":{"observed_at":"2026-08-10T23:47:45.370778Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.327329Z","title":"Point -based multi-view stereo network","venue":null,"work_id":"8c02e54b-60c6-4aeb-95e5-92a969419dcb","year":2019},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.871175Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:6ebd960929e48feb875b9631e85b4a6ce444f53bfdda1e257ef0106d045d2bec","observation_id":"88fef307-e64d-4735-9823-ef8f6bb3f9aa","resolution":{"observed_at":"2026-08-10T23:47:45.331674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.314324Z","title":"Depth anything: Unleashing the power of large-scale unlabeled data","venue":null,"work_id":"8ac17c5e-07fc-4471-8bbc-7eb781ca06ee","year":2024},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.875336Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:97d46e01f7cd2a0e1512737f5e7ad4e34f904b67b1bbb06b7b3fdcf9f9aa711a","observation_id":"f488926c-9a2e-401d-9117-a4ab69db968c","resolution":{"observed_at":"2026-08-10T23:47:45.318878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.301330Z","title":"Repurposing diffusion-based image generators for monocular depth estimation","venue":null,"work_id":"8fbc9c9f-751c-4ef2-969f-ba5b5fc0d840","year":2024},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.879722Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:51aea9ce72c58793a086e83dcaf6dcf76f70b35fce504c4673c112307aa6dffd","observation_id":"0f94819a-c216-4c15-9dbe-ab140876cb3f","resolution":{"observed_at":"2026-08-10T23:47:45.305767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.287916Z","title":"Depth map prediction from a single image using a multi-scale deep network","venue":null,"work_id":"39d33691-f131-4e88-a81c-a95f87385a41","year":2014},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.884335Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:c3d427096aee32529cf2a37573ab050b4e3ede59c3126416cd0bee79173bbc2e","observation_id":"47d8ca43-c5e6-47ad-bcf3-4539be7ed389","resolution":{"observed_at":"2026-08-10T23:47:45.292481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.274224Z","title":"Single-view and multi-view depth fusion","venue":null,"work_id":"2c080f25-532b-4a3c-9e27-42785bce5994","year":2017},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.888692Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:c59efe2ced3c42d4f0de9db58b1e6d1cb5d00dda8f3ffe2264e1473d117fb9d0","observation_id":"5cc87d26-f75b-4dda-85eb-a5cd816d6473","resolution":{"observed_at":"2026-08-10T23:47:45.278656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.260566Z","title":"Deep ordinal regression network for monocular depth estimation","venue":null,"work_id":"74038dfc-dcf4-4621-b358-37996b2e7b41","year":2018},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.893034Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:5d603f712a6810ba2b6acaea0a0de614309dd4aa10f3e600648546ceee3ec729","observation_id":"d105399d-2638-4e71-b510-419fe8cfe762","resolution":{"observed_at":"2026-08-10T23:47:45.265059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.247262Z","title":"Vision meets robotics: The kitti dataset","venue":null,"work_id":"bd24656d-d413-447e-ab12-b7205e3a8191","year":2013},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.897170Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:80b12ed47f0ee419de06d8e09afa5d4bf149ddc4351e74b783093cfa2cc1b283","observation_id":"532a70dc-3a13-43f6-8ed2-e392310b4f9e","resolution":{"observed_at":"2026-08-10T23:47:45.251694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.233827Z","title":"Digging into self-supervised monocular depth estimation","venue":null,"work_id":"6a00f754-cda7-4dc7-97b8-a05c12f0a1ef","year":2019},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.901404Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:09350b98c7a038e9cfda367b627ea4110cb5f30b96faa20aeb80939a5a5fc606","observation_id":"707308fb-730e-4807-96c3-e18b3a553031","resolution":{"observed_at":"2026-08-10T23:47:45.238293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.220227Z","title":"Cascade cost volume for high-resolution multi-view stereo and stereo matching","venue":null,"work_id":"cb54d78e-81f2-4ee3-a1dc-53f6c9788af7","year":null},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.905379Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:b2aec47d5d30003871d6be275c70853025861edf867a7a31f525914d0ac74948","observation_id":"5a4fb547-663c-4d08-a488-8918fb0acf84","resolution":{"observed_at":"2026-08-10T23:47:45.224546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.10326","last_updated":"2021-09-23T10:23:51Z","snapshot_observed_at":"2026-08-19T15:38:00.383979Z","submitted_at":"2019-07-24T09:31:24Z","title":"From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.10326","snapshot_observed_at":"2026-08-10T23:47:44.909539Z","title":"From big to small: Multi-scale local planar guidance for monocular depth estimation","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.909539Z"},"links":{"cited_paper":"/paper/1907.10326","citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:9eb4371e2a5a19beff705d9a70afec1ed79d6ffcea293aab279a8b59256d0ccf","observation_id":"8358f799-b291-4539-af25-cb3e8da9bec5","resolution":{"observed_at":"2026-08-10T23:47:44.909539Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:47:45.207436Z","title":"Multi- view depth estimation using epipolar spatio-temporal networks","venue":null,"work_id":"210810f9-3999-4cbf-817f-b3066074bf9d","year":2021},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.914361Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:41ada93d6438cdc6f63d7a2a3ef6e64dde8c12e3c94a0a9d512dbd1fb5a744ef","observation_id":"a17aa15c-c216-4cea-bd4a-b45ad6a94de9","resolution":{"observed_at":"2026-08-10T23:47:45.211685Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-14T20:13:52.872565Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-10T23:47:44.918859Z","title":"Decoupled weight decay regularization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.918859Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:c77f8f956233a2af795a326ef89671a8e82917d5b712f74f0a8923a85afa123b","observation_id":"91b4b9df-5e53-4b7d-bc81-1bf7bc7d1954","resolution":{"observed_at":"2026-08-10T23:47:44.918859Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:47:45.192997Z","title":"Multi -level context ultra-aggregation for stereo matching","venue":null,"work_id":"74d2aab8-b5f6-4669-b51a-45c05c7bbd1e","year":2019},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.923683Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:2aa38ed1664eb8ea95339932ef46d9d82c82a634b0bf4e61fd04576bb58a4677","observation_id":"16d39c9a-5037-4c3b-bbda-e7621544d390","resolution":{"observed_at":"2026-08-10T23:47:45.197849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.179077Z","title":"Pytorch: An imperative style, high-performance deep learning library","venue":null,"work_id":"4caf15d1-1e79-4498-a6cc-cebfa46ce42b","year":2019},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.927669Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:329eac0d16dd4272e6b4f855cb50092f73c38c1509cd4c9759a544000c8f11d1","observation_id":"aa5d9013-ecc4-4f7c-82f7-7c07fc1fcb57","resolution":{"observed_at":"2026-08-10T23:47:45.183548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.165371Z","title":"Feature-metric loss for self- supervised learning of depth and egomotion","venue":null,"work_id":"fb388289-edf7-4f85-bf8b-5355e541bf24","year":2020},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.931795Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:d9aa88ea2e9e3d183455938826d9c29355fafc59c87ecd6e14199716e0064c33","observation_id":"d1ab6520-6fb8-42ad-b2e5-8f30b281ca26","resolution":{"observed_at":"2026-08-10T23:47:45.169925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.04605","last_updated":"2020-04-27T19:17:43Z","snapshot_observed_at":"2026-08-20T05:03:05.291993Z","submitted_at":"2018-12-11T18:47:12Z","title":"DeepV2D: Video to Depth with Differentiable Structure from Motion","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.04605","snapshot_observed_at":"2026-08-10T23:47:44.935918Z","title":"Deepv2d: Video to depth with differentiable structure from motion","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.935918Z"},"links":{"cited_paper":"/paper/1812.04605","citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:aa1db0aa882be0323f192eb682d2e862e1a7ee46cd8d0df387b6ed3a099eccd3","observation_id":"e0892eaf-37bd-4979-87b6-7f4adfe3e29c","resolution":{"observed_at":"2026-08-10T23:47:44.935918Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:47:45.151182Z","title":"Patchmatchnet: Learned multi-view patchmatch stereo","venue":null,"work_id":"68a32f20-fccb-4845-84ff-c5c33b8ebbea","year":2021},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.941189Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:51cf395c793dbf392e9f06d4052ba584448541032bb3bfecd4a22696dbbb2936","observation_id":"a199258d-3f89-40b5-ad8b-495670b3189a","resolution":{"observed_at":"2026-08-10T23:47:45.155894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.136595Z","title":"Itermvs: Iterative probability estimation for efficient multi-view stereo","venue":null,"work_id":"bc4f66e5-e997-40b2-b7ae-0117cbd1f8ef","year":2022},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.945177Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:e6a3d36ff1e71d40d4de9ea1b082b6edd8b39d87d3cf9ff50df74c9d8085df53","observation_id":"4a2abece-2187-42fa-ab41-ba8b662e9778","resolution":{"observed_at":"2026-08-10T23:47:45.141396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.121586Z","title":"Mvs2d: Efficient multi-view stereo via attention-driven 2d convolutions","venue":null,"work_id":"9065a2d1-8130-4fe1-8df0-6ffa75e362ae","year":2022},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.949186Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:1d032b83c304625d9306ebd0fdd2ead4d233374b0cb8163bdc7a815a05fff2dc","observation_id":"defffe85-b47d-4848-b518-0b9dac2be11a","resolution":{"observed_at":"2026-08-10T23:47:45.126827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.106074Z","title":"Metric3d: Towards zero-shot metric 3d prediction from a single image","venue":null,"work_id":"c25000cc-e775-46cf-9293-58b49d02c5cd","year":2023},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.953379Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:20855022bfa8f1c23997ff6f3a66dceca0d1c707c090d6360c4f32c96b9b00f4","observation_id":"a251051d-e5ca-4f86-9139-3d8745a3cad2","resolution":{"observed_at":"2026-08-10T23:47:45.110886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.091598Z","title":"Fast -mvsnet: Sparse-todense multi-view stereo with learned propagation and gaussnewton refinement","venue":null,"work_id":"404b5c2f-6366-4b1b-98b9-d4dfbed74231","year":2020},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.957771Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:f6042f5362ce0543a4f9f5e8921a70415276efd1ca01ebf429b780314831a0ab","observation_id":"074c8374-cd52-421a-9f03-275eaf6ca490","resolution":{"observed_at":"2026-08-10T23:47:45.096192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T23:47:45.074506Z","title":"Computing the stereo matching cost with a convolutional neural network","venue":null,"work_id":"66de71fa-a403-4033-aa66-ec4ac4343f0a","year":2015},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.961937Z"},"links":{"citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:aabdc618f5eea874725456ea1b96531332256455d98519fc1122aa921c17985d","observation_id":"32722e12-c81a-4a9a-9521-c9447678e91e","resolution":{"observed_at":"2026-08-10T23:47:45.081318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-08-17T20:47:46.242385Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-10T23:47:44.966144Z","title":"Mamba: Linear -time sequence modeling with selective state spaces","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.966144Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:99c83d182e5ae43ed45128aa1a113771ae5eb313238b3deb633e40bca7baf0a7","observation_id":"bc2de490-5ad5-48a7-8d30-fdfe54ca0564","resolution":{"observed_at":"2026-08-10T23:47:44.966144Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10166","last_updated":"2024-12-29T14:57:13Z","snapshot_observed_at":"2026-08-17T14:56:56.233298Z","submitted_at":"2024-01-18T17:55:39Z","title":"VMamba: Visual State Space Model","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10166","snapshot_observed_at":"2026-08-10T23:47:44.970772Z","title":"VMamba: Visual State Space Model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T23:47:44.970772Z"},"links":{"cited_paper":"/paper/2401.10166","citing_paper":"/paper/2412.19964"},"observation_digest":"sha256:764a7a2aa242be46364e9fc69bd444dfdf4bec24645b069f798639476f7270c7","observation_id":"57888a18-5441-407d-a54b-dd8ed4d56ecb","resolution":{"observed_at":"2026-08-10T23:47:44.970772Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.19964","last_updated":"2024-12-28T01:17:47Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-19T15:39:12.875166Z","submitted_at":"2024-12-28T01:17:47Z","title":"DepthMamba with Adaptive Fusion"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":21},"total_outbound_references":28},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2412.19964."}