{"as_of":"2026-08-16T18:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ed4a041272a342f7474cff95aa75c2d3f89818f39fafd8d1a40de9e77edad3d9","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T15:23:14.132042Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/1908.01301/citation-record","integrity":"/paper/1908.01301/integrity","json":"/paper/1908.01301/citation-record.json","paper":"/paper/1908.01301"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1809.11096","last_updated":"2019-02-25T21:32:06Z","snapshot_observed_at":"2026-07-06T07:04:57.275371Z","submitted_at":"2018-09-28T15:38:49Z","title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.11096","snapshot_observed_at":"2026-08-14T15:23:13.920389Z","title":"Large scale gan training for high fidelity natural image synthesis","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.920389Z"},"links":{"cited_paper":"/paper/1809.11096","citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:b37385d512fd54a99e29f13558a80ed599fd033f836e2966bcb9afc6f5afbb78","observation_id":"0cfb8d53-70b5-4b59-b9f1-06a4cec8eeda","resolution":{"observed_at":"2026-08-14T15:23:13.920389Z","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":"2026-08-14T15:23:14.753541Z","title":"Depth from a single image by harmonizing overcomplete local network predictions","venue":null,"work_id":"534e557f-2375-465d-ae6d-a676951e04b3","year":2016},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.926189Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:7fd60968f29b12e328519be03599b72d06e210a96b8424be127c5cfbc47610f2","observation_id":"bc63d27e-6681-401a-a23c-b8a2f258df04","resolution":{"observed_at":"2026-08-14T15:23:14.757918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.734188Z","title":"Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture","venue":null,"work_id":"dd0fec11-eb18-4e30-b6c9-0cb0b0865e44","year":2015},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.931024Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:0d1207ac61dad93dfa6007bd651d922cfc73a02ee56949b738c3a0799a4a9328","observation_id":"f871e86a-3f36-44d2-8e1a-c211ad7bb538","resolution":{"observed_at":"2026-08-14T15:23:14.738791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.714549Z","title":"Depth map prediction from a single image using a multi-scale deep network","venue":null,"work_id":"54b1913b-1098-4278-8bb1-f7fe377631bb","year":2014},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.935704Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:998c391483b12c0a4e87226aaa509be7edf8a0e141a634ac7cba281158bec47c","observation_id":"f5c81753-ba3c-4613-ab9f-4dfa35f53f76","resolution":{"observed_at":"2026-08-14T15:23:14.720195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:13.940070Z","title":"Deep ordinal regression network for monocular depth estimation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.940070Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:c51223af3b48f4cee1de56304d2abb358a9cd8c37176e94c7eb0e9e598ab149c","observation_id":"d41271e2-c08d-444e-9771-b99f7b96e293","resolution":{"observed_at":"2026-08-14T15:23:13.940070Z","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":"2026-08-14T15:23:14.688415Z","title":"Multi-view stereo: A tutorial","venue":null,"work_id":"4fe0397d-bc55-4b23-919d-ad6d83e5d5d2","year":2015},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.947273Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:4aca4260edd9fcd92b1584450bb20e7b4dd8a50f696e8c665a9d4c2c36b1da54","observation_id":"f749173c-94c9-44ab-8ec5-d67f6ceaee62","resolution":{"observed_at":"2026-08-14T15:23:14.693067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:13.953397Z","title":"Massively parallel multi- view stereopsis by surface normal diffusion","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.953397Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:f5c608a65e647d9aff5f16854a1264b41065aa17773e0c1093a8d9456df80a7e","observation_id":"e22d590f-76a6-47d5-b413-9f077c8e32b4","resolution":{"observed_at":"2026-08-14T15:23:13.953397Z","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":"2026-08-14T15:23:13.958512Z","title":"Generative adversarial nets","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.958512Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:169c0a8ab44f7f40c939cb22442889201cf1648f1e367c2006b277c4b01704d0","observation_id":"afd9d764-a619-423f-ae37-758391bb2935","resolution":{"observed_at":"2026-08-14T15:23:13.958512Z","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":"2026-08-14T15:23:13.968908Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.968908Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:b3139eff2c2db05bb5a6119fc7a3819eb76b648a2a1ef412fcc9266df04b30d7","observation_id":"5735d84f-3cfb-42ac-9202-277b35a2e4aa","resolution":{"observed_at":"2026-08-14T15:23:13.968908Z","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":"2026-08-14T15:23:14.645117Z","title":"Recovering surface layout from an image","venue":null,"work_id":"c445c462-8b3f-4c32-9872-138509f8aa2a","year":2007},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.974141Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:6ed01b33cec62cd4a1f62008a5b5f46476224abeefd92e5c4665d6c84a886143","observation_id":"607bbc67-22ce-4864-9603-f14baa36662b","resolution":{"observed_at":"2026-08-14T15:23:14.649730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.629821Z","title":"Beyond face rotation: Global and local perception gan for photorealistic and identity preserving frontal view synthesis","venue":null,"work_id":"8f1bdc4d-5cf7-4f07-930c-265a01138282","year":2017},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.978621Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:13fed76a9c4e6bfd77c47ab0fe40a0f86da832a0ba33ef3860ca85a992dcb7db","observation_id":"6fd24a5b-d0b5-4acd-9241-382f58f8b417","resolution":{"observed_at":"2026-08-14T15:23:14.634297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.616135Z","title":"Spatial transformer net- works","venue":null,"work_id":"b500851c-f0b2-498b-b520-597bb9e10171","year":2015},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.982812Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:4efec43e15e5e54d0797e1d1fa95a648fb0340aa26f0202a5e31ad33673f9450","observation_id":"1bbdbe2f-07a7-431f-89c7-d04ff1393ee7","resolution":{"observed_at":"2026-08-14T15:23:14.621243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.602229Z","title":"Surfacenet: An end-to-end 3d neural network for multiview stereopsis","venue":null,"work_id":"413973e7-87ac-4277-908d-2df5892d58f1","year":2017},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.986795Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:fbee22dd4603093180e1203184725cbc5116937d4999e2798902b83bb727ac1a","observation_id":"6aeca3db-cff4-4cff-8cbd-36355df27364","resolution":{"observed_at":"2026-08-14T15:23:14.606558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.585581Z","title":"Karsch, C","venue":null,"work_id":"bb090bf7-6360-491e-ba4b-02c97bbae1d8","year":2014},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.991277Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:7592debca91b40cf55083e55ef6d03ebef52957cf960ce92f155dfa847c71b3e","observation_id":"a15aa538-e351-4f75-b8a4-9ab92bca59c0","resolution":{"observed_at":"2026-08-14T15:23:14.592868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.570314Z","title":"What uncertainties do we need in bayesian deep learning for computer vision? In nips","venue":null,"work_id":"41ae3672-d07e-49f6-b485-ee26047ea009","year":2017},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:13.995376Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:3bb9c3b18e9bc8ba355e28eb4db60c0d762e9b316960ea222ca3d42bfe6e0e57","observation_id":"a514219e-5df3-496f-b9e4-3b758e620947","resolution":{"observed_at":"2026-08-14T15:23:14.575694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.556106Z","title":"A theory of shape by space carving","venue":null,"work_id":"a93ccb52-ecba-4dc9-8da2-fd406dc646f8","year":2000},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.000627Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:e982cec3ecaf6c40c9c2691d4cec1ef7802f7a57b0f93a3a8946fe1b3691c7a3","observation_id":"dda81993-1413-472a-9f12-e86ea456380d","resolution":{"observed_at":"2026-08-14T15:23:14.561517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.005136Z","title":"Pulling things out of perspective","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.005136Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:46d684bb63ef8a477061bfe6269922ae203da06eadf9a5566241b7bbec91c177","observation_id":"a9b902ff-f71b-49c7-ad52-1bd29d9d2e75","resolution":{"observed_at":"2026-08-14T15:23:14.005136Z","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":"2026-08-14T15:23:14.533611Z","title":"Deeper depth prediction with fully convolutional residual networks","venue":null,"work_id":"c01c40b5-0f74-470a-90e6-442e629e0cac","year":2016},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.011369Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:97cc5472347a7025ccd7c367912ba4adc59f23f5985aac460d2cf4bb9c00d789","observation_id":"a28e66e8-1888-4a8d-9c55-4b54d1c62540","resolution":{"observed_at":"2026-08-14T15:23:14.538063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.520634Z","title":"Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs","venue":null,"work_id":"7f30b000-25b1-4d0b-9d78-c57a20c6a9db","year":2015},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.016632Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:dbace15406027cce19889b4c64165fb01db1d28ad19ef6c093a332f97085d015","observation_id":"4b9d7df7-6dba-4cb4-abf0-888a1a0e49f0","resolution":{"observed_at":"2026-08-14T15:23:14.525307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.507468Z","title":"A two-streamed network for estimating fine- scaled depth maps from single rgb images","venue":null,"work_id":"f515b175-14a2-48e1-8198-50cc8f7249ef","year":2017},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.028404Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:68f29ecde542afc959fd334f64c7f86c81354b997969ad15bc04fb53e822c4b7","observation_id":"268856fd-dc11-4cf9-b3a6-553e36e61d51","resolution":{"observed_at":"2026-08-14T15:23:14.512274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.493339Z","title":null,"venue":null,"work_id":"a86ba16b-bc11-49a7-aabf-48809626fd0f","year":2010},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.033167Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:86ad75d8b24386db99228319763111586fd58ad4986c732f98b85f53c209e9d8","observation_id":"b8599966-1023-4e5e-9193-9bc6acef82cb","resolution":{"observed_at":"2026-08-14T15:23:14.498777Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.479018Z","title":null,"venue":null,"work_id":"d02b7c46-79b9-4008-8ff6-aa0d428ec1ae","year":2008},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.037229Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:06215ab964b236a53c76ea7e970cfd675f6f8110586b3c06eb23eda3baee6e09","observation_id":"c0c33516-c4eb-429d-850e-2ef8872f9e4a","resolution":{"observed_at":"2026-08-14T15:23:14.484433Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.466004Z","title":null,"venue":null,"work_id":"7d7997f7-9842-4752-93d4-f56cf63d8022","year":2024},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.042206Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:b176e171d3c2c7a6b638c8efe2ede606e3b7c787bc2b157928ca1398e7eb2f2e","observation_id":"fc32dae6-c3a7-445b-8e2d-eeb0d18ca5a4","resolution":{"observed_at":"2026-08-14T15:23:14.470092Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.453008Z","title":"Learning depth from single monocular images using deep convolutional neural fields.IEEE transactions on pattern analysis and machine intelligence, 38(10):2024–2039, 2016","venue":null,"work_id":"cefc679b-a079-4a12-ae05-0695a035240e","year":2024},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.050278Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:15b4b38e2a1122cb11828617f0d698301e14fe428db775268a8bd6b1b8adc8b4","observation_id":"ca2fe135-df29-4541-a7f6-5e60ec7a015f","resolution":{"observed_at":"2026-08-14T15:23:14.457515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.439928Z","title":"Discrete-continuous depth esti- mation from a single image","venue":null,"work_id":"3a81dc0a-bbd2-4198-aa91-0c8f0c9cbe28","year":2014},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.055220Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:19501214d67d75d5638b06906b98ce91f3ec1eee0f2ada5fd6449e389b1f3f9a","observation_id":"de41f80e-14ed-4c50-8f18-c0378648b378","resolution":{"observed_at":"2026-08-14T15:23:14.444156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.427074Z","title":"Deep metric learn- ing with bier: Boosting independent embeddings robustly","venue":null,"work_id":"d5c6ba67-d021-4e5c-bdb9-6c69246de19e","year":2018},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.059984Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:6fdc8946bbd54b2e0c0995c493329c1446125dde52d2d44fac2c63f46fbb49e6","observation_id":"4e5d6a7e-3556-4fc9-94c1-998a9f538bae","resolution":{"observed_at":"2026-08-14T15:23:14.431367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06434","last_updated":"2016-01-07T23:09:39Z","snapshot_observed_at":"2026-08-12T18:43:14.431633Z","submitted_at":"2015-11-19T22:50:32Z","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06434","snapshot_observed_at":"2026-08-14T15:23:14.064151Z","title":"Unsupervised representation learning with deep convolutional generative adversarial networks","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.064151Z"},"links":{"cited_paper":"/paper/1511.06434","citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:d4bdadddca88d9e7019cb9386c88b3411b2c7efb7d871c9ce1ff1884a2f64766","observation_id":"7aca2ddc-7e18-4195-a4ef-3ae165de64e3","resolution":{"observed_at":"2026-08-14T15:23:14.064151Z","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":"2026-08-14T15:23:14.413507Z","title":"Monocular depth estimation using neural regres- sion forest","venue":null,"work_id":"9df151e4-259c-481d-9a82-384f0edca5fe","year":2016},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.069356Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:b69fa68e90125f6b3e3f4bd759bed6eeee7190f8b675a6a49e383d2cca72f65c","observation_id":"53ae1672-7c74-4926-a671-85dd1facc183","resolution":{"observed_at":"2026-08-14T15:23:14.418488Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.399681Z","title":"Saxena, M","venue":null,"work_id":"05c8042a-2752-440f-92ee-679701da2b68","year":2009},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.073529Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:4e93ab0c3e838c2d9e8d130d757babde7d906895eb9402ebf35d384b278e459e","observation_id":"0dfe464d-5a28-4b2f-a906-2e8346a1a837","resolution":{"observed_at":"2026-08-14T15:23:14.404597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.384882Z","title":"Learning depth from single monocular images","venue":null,"work_id":"216dd49f-bf89-4c52-aac7-d8d730de49de","year":2006},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.077788Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:203a02bf0e5d5313c9b9cf225d7fcfa3622433c01bff979c6b4f3e55e6ef14d6","observation_id":"800e8fb8-c16c-489a-aa6d-939ddfc8a648","resolution":{"observed_at":"2026-08-14T15:23:14.389186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.372251Z","title":"Photorealistic scene reconstruction by voxel col- oring","venue":null,"work_id":"00cb6b5b-52f3-4d22-9289-01ddecefb78d","year":1999},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.082532Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:56bbdd65c42b40a707e4b45074cb658eccb9309b447ac6eb961296c79789604a","observation_id":"80eaff04-ca7b-4e0e-81c7-b1f76b32d74b","resolution":{"observed_at":"2026-08-14T15:23:14.376534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.355586Z","title":"Indoor segmenta- tion and support inference from rgbd images","venue":null,"work_id":"42916ead-191d-4b48-880d-aecceac914c6","year":2012},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.086463Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:27742157963329feecf6ca78f3da983ad395909e8f40d7f15d1d7e64e7bc5d05","observation_id":"3756df73-5df3-4b84-b3e0-268a7016bb91","resolution":{"observed_at":"2026-08-14T15:23:14.359796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.340964Z","title":"Efficient large-scale multi-view stereo for ultra high-resolution image sets","venue":null,"work_id":"a05bc88f-292b-4a17-9a5b-689c934cc9cc","year":2012},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.090542Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:1c02c8c2556530a04408b260ebc39faa9e389b1cee063c063936f9dd8850690c","observation_id":"82a861c2-c42e-4e66-86c2-95dd1fe6aace","resolution":{"observed_at":"2026-08-14T15:23:14.346343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.094657Z","title":"Disentangled representation learning gan for pose-invariant face recognition","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.094657Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:a31df927f5c209915747df72ad664957c74ce8950cda126ae1abba56991e70c3","observation_id":"0abf1a12-d2dd-4939-9c45-de99d28eb1e3","resolution":{"observed_at":"2026-08-14T15:23:14.094657Z","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":"2026-08-14T15:23:14.316853Z","title":null,"venue":null,"work_id":"6da6f500-309c-4ab4-89a4-d86802576954","year":2015},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.099423Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:e91211546a8b52d58bc7819387139b80e20334bb4008305222617fac13e366fa","observation_id":"da94cd1a-6069-4e46-889d-d7e837505e1e","resolution":{"observed_at":"2026-08-14T15:23:14.321323Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.301429Z","title":"A-fast-rcnn: Hard positive generation via adversary for object detection","venue":null,"work_id":"fe754a42-2325-4002-a015-bc4265518d63","year":2017},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.103640Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:965548ff3b0afcba28b247724156426a1e9763018111464caa19ee35ea44a276","observation_id":"bd032492-b3b6-40e6-b254-38aa49f01186","resolution":{"observed_at":"2026-08-14T15:23:14.306011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.281904Z","title":"Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks","venue":null,"work_id":"80b8592b-d7d1-4f0b-a181-149d14c9e011","year":2016},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.108459Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:d1e5e1562139ea00b84e32ffbd1a2ba4a7f5f28c7e94982e7e9311cb7163c1a9","observation_id":"f4f7b833-6eb5-4ff4-82ae-d1d9abfb018c","resolution":{"observed_at":"2026-08-14T15:23:14.289629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.265822Z","title":"Multi-scale continuous crfs as sequential deep networks for monocular depth estimation","venue":null,"work_id":"4469b122-b617-40cb-aba4-16638c087ca1","year":2017},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.113004Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:85454891a414514d059c84efd837b1ad1c72dc6537a96f3d61d67366ba6922da","observation_id":"c109977d-60ad-4b55-a82f-2a0047e3a609","resolution":{"observed_at":"2026-08-14T15:23:14.271166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.251468Z","title":"Mvsnet: Depth inference for unstructured multi-view stereo","venue":null,"work_id":"50272429-1365-4a0a-9a54-7d9a84728c42","year":2018},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.117660Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:d91dcaf6e3a0241a4db60417d0bcd07c927631371ca411e699f3d16b8cd5a8e5","observation_id":"b0e3a166-9f2d-40ce-a592-315e9111e794","resolution":{"observed_at":"2026-08-14T15:23:14.256603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.122674Z","title":"Unsupervised learning of depth and ego-motion from video","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.122674Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:bf1b3138bc1f54e37258593015bba353e31512332fc77bc76e37a4d9c3fc2ec0","observation_id":"6cd3e240-1812-4f40-9a1c-c8e4d06cdc24","resolution":{"observed_at":"2026-08-14T15:23:14.122674Z","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":"2026-08-14T15:23:14.225697Z","title":"Generative visual manipulation on the natural image manifold","venue":null,"work_id":"ccb5d381-a7a9-4e57-8909-b8569294f0be","year":2016},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.127417Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:059e698fa532bb171bf79a23842f039e470579b592edaaf646758224a9a8aba5","observation_id":"a23633d5-5171-4894-a4bb-f11a74e55c53","resolution":{"observed_at":"2026-08-14T15:23:14.230839Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:23:14.206610Z","title":"Indoor scene structure analysis for single image depth estimation","venue":null,"work_id":"ca362897-b1a7-4162-b22a-5e95deb5706b","year":2015},"citing_paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T15:23:14.132042Z"},"links":{"citing_paper":"/paper/1908.01301"},"observation_digest":"sha256:20fa927b4ec11d4892b842e757fb1d321f031a597c6d4ad26e14ac64522cf8a0","observation_id":"9514ba2e-0935-4a93-ae15-7be41fbf3465","resolution":{"observed_at":"2026-08-14T15:23:14.214082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.01301","last_updated":"2019-08-04T09:37:24Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T08:39:31.710584Z","submitted_at":"2019-08-04T09:37:24Z","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":0,"verified_fuzzy":29},"total_outbound_references":42},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:1908.01301."}