{"as_of":"2026-08-16T19:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9a06c5bb537d12f36359397f5a84269779a5eca4a339cb28f143a1bcb6b39742","coverage":[{"denominator":77,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":77,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T12:54:40.171315Z","state":"measured"},{"denominator":77,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":77,"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/1908.06316/citation-record","integrity":"/paper/1908.06316/integrity","json":"/paper/1908.06316/citation-record.json","paper":"/paper/1908.06316"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:54:41.316692Z","title":"Exploiting semantic information and deep matching for op- tical flow","venue":null,"work_id":"d2d3a854-86ad-4347-aaa0-c4543bc5bdd0","year":2016},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.845288Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:765e44f0912243f7988d42ba36706b09d2c8a03cbbab785ec1164be5f0049246","observation_id":"f7189bfe-ca2b-4fc1-a81e-f8ab09d5ae67","resolution":{"observed_at":"2026-08-14T12:54:41.320272Z","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":"2026-08-14T12:54:41.304998Z","title":"Driven to distraction: Self-supervised distractor learning for robust monocular visual odometry in urban en- vironments","venue":null,"work_id":"9cde006a-8417-4013-98f9-59d9483a1deb","year":1900},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.849597Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:ebf1184f5569519a6182216e49cfad6298449f348645e03067da8eee5606ca78","observation_id":"8b551095-b50a-439b-9a92-1265ab262507","resolution":{"observed_at":"2026-08-14T12:54:41.309100Z","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":"2026-08-14T12:54:41.291761Z","title":"Multi-view scene flow estimation: A view centered variational ap- proach","venue":null,"work_id":"af813639-7009-4785-b072-5a33598ffd34","year":2013},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.853637Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:30066a44e5ccb7a2517363bf7fa250c8727e6956080847d84e81e44b40484ba3","observation_id":"b5467fab-2ddc-47ba-964a-beec279ff844","resolution":{"observed_at":"2026-08-14T12:54:41.296079Z","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":"2026-08-14T12:54:41.280347Z","title":"Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios? In Proc","venue":null,"work_id":"95b64321-489b-4f0c-b78c-2d71ba4f4b83","year":2017},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.857782Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:c2d9fb2bc1079c36c5537a4855c59c9b11d776f30306e45f7e17becc4b241412","observation_id":"18e799fc-ae6c-4db6-87d1-df981b67a2e6","resolution":{"observed_at":"2026-08-14T12:54:41.284377Z","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":"2026-08-14T12:54:41.267301Z","title":"Exploiting Single Image Depth Prediction for Mono-Stixel Estimation","venue":null,"work_id":"0ef2c74b-60e1-4837-9007-1c36c9912234","year":2018},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.862245Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:0ce0f2084a1a3f01f2a8e2fe3da8979f3ee8cba752cd8d36a6f514838655caa9","observation_id":"aa1dc4ca-7534-46c1-8eba-c360882d162d","resolution":{"observed_at":"2026-08-14T12:54:41.273012Z","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":"2026-08-14T12:54:41.254335Z","title":"Mono-Stixels: Monocular Depth Reconstruction of Dy- namic Street Scenes","venue":null,"work_id":"476f634b-4e62-4969-ac08-434d1dd0d415","year":null},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.866235Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:80a5e82c11065ed5b77d4d20642d029566081c8e16b9a3b7db63ba4b23d6c00e","observation_id":"95ba1435-41bf-4113-a272-47cf6f5a65b2","resolution":{"observed_at":"2026-08-14T12:54:41.258854Z","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":"2026-08-14T12:54:41.242512Z","title":"Depth and scene flow from a single moving camera","venue":null,"work_id":"7864b18d-0d03-4896-85af-2b02cf68fe55","year":2010},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.869736Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:eaa96345d3708d06dbe53e9b8f3e81a79ad616affb604f4345b51b056bd2c193","observation_id":"5ba268e0-f9ea-47dd-87b5-5315371eb0ec","resolution":{"observed_at":"2026-08-14T12:54:41.246430Z","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":"2026-08-14T12:54:41.230453Z","title":"3D Vehicle Trajectory Re- construction in Monocular Video Data Using Environment Structure Constraints","venue":null,"work_id":"4da87089-8b4c-40ae-992b-f3635f6927b2","year":2018},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.874022Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:b6b14c491db549679489b5433e266715760c13a3f1ced021f64aeab7737a144d","observation_id":"034cd478-d42f-4c40-8bcf-f4b03fba984a","resolution":{"observed_at":"2026-08-14T12:54:41.234817Z","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":"2026-08-14T12:54:41.216872Z","title":"The cityscapes dataset for semantic urban scene understanding","venue":null,"work_id":"09c08472-2e2d-465d-ae4e-e020ee7a5e45","year":2016},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.877818Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:ced919d231a3b0ceed7142299e2c6e9ed11937c8e449bdf96adafd7035feb16d","observation_id":"8c7fadca-fe3a-4108-869a-5304164a838a","resolution":{"observed_at":"2026-08-14T12:54:41.221884Z","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":"2026-08-14T12:54:41.202186Z","title":"Depth map prediction from a single image using a multi-scale deep net- work","venue":null,"work_id":"71067bfb-f180-4383-8219-d51d88a2445c","year":2014},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.881293Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:2c09c20a74b13dc2a5b9c66a24cc2cb3309a2d95e5917cad574f9f030a2530a2","observation_id":"ceaba9ef-cc99-4252-ae54-8802e47a4fa0","resolution":{"observed_at":"2026-08-14T12:54:41.207092Z","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":"2026-08-14T12:54:41.179409Z","title":"LSD- SLAM: Large-scale direct monocular SLAM","venue":null,"work_id":"e5bec5f6-951f-400a-adc4-4fc1f6247d24","year":2014},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.885071Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:bd3a747947f9d6d543439c672938145583914624ec13ebb904d88eec2ffd8e52","observation_id":"9437707a-241d-422a-befd-3aee74e9e6bd","resolution":{"observed_at":"2026-08-14T12:54:41.188378Z","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":"2026-08-14T12:54:41.160623Z","title":"Single-View and Multi-View Depth Fusion","venue":null,"work_id":"a79f3b72-37a9-4e8b-80f7-5f82973c7cb5","year":1994},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.889072Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:f716aac416d3728980c09e92e54a8252f241c62c438d9303fd099b27a1b0601a","observation_id":"df214aab-84a3-4b73-bf76-5da40bbdbfd6","resolution":{"observed_at":"2026-08-14T12:54:41.165234Z","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":"2026-08-14T12:54:41.146926Z","title":"Predictive monocular odometry (PMO): What is possible without RANSAC and multiframe bundle adjustment? Image and Vision Computing, 2017","venue":null,"work_id":"473da37f-0a59-4308-a481-f4ba895053b9","year":2017},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.893064Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:5b486999314875bf5919e69d9e04308d88739cdc61d65f87ab21408279d3c315","observation_id":"659ee092-b61c-462e-96ba-edb2d6bca8dd","resolution":{"observed_at":"2026-08-14T12:54:41.152163Z","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":"2026-08-14T12:54:41.133923Z","title":"Deep Ordinal Regression Network for Monocular Depth Estimation","venue":null,"work_id":"e76433a9-203a-4c5b-abc4-2ab53b59971a","year":2002},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.896874Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:3782697877ca8c6d03c27c325bed5ee609e9bf8b8583f4a5aa2e21c4fd6f9eb2","observation_id":"910ce654-8076-4742-a2f4-8c7b1255f3ad","resolution":{"observed_at":"2026-08-14T12:54:41.138485Z","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":"2026-08-14T12:54:41.119733Z","title":"Unsupervised CNN for single view depth estimation: Geometry to the res- cue","venue":null,"work_id":"30840726-cb6b-46af-a95e-fb3ca0f4fe96","year":2016},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.901150Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:f3a32ac22f40162587576346adee8a091664eb1204296eeb4291d3960e5edde3","observation_id":"55819404-f4da-4caf-a592-3b7570bfb91a","resolution":{"observed_at":"2026-08-14T12:54:41.124766Z","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":"2026-08-14T12:54:41.103025Z","title":"Dense variational reconstruction of non-rigid surfaces from monoc- ular video","venue":null,"work_id":"e87d483e-b6e0-43f8-ab54-0d95ec0e0cc3","year":2013},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.905989Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:87c0b08881e15d02a1c168f1419cee9c47a9b32d44d92ef9b8bc640c942eafcb","observation_id":"062e9e47-99b8-4997-a494-ec0bab3a96f8","resolution":{"observed_at":"2026-08-14T12:54:41.108516Z","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":"2026-08-14T12:54:41.084011Z","title":"Lightweight Probabilistic Deep Networks","venue":null,"work_id":"d17d6212-c0d7-45d1-b9f5-20209b7df2ba","year":null},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.909917Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:a2f27f9c7b70a4fa57cab126587050e5a3bfb385faf354eccf780658a5c22805","observation_id":"6546d4c0-184b-41eb-af65-054c6c9ac971","resolution":{"observed_at":"2026-08-14T12:54:41.088275Z","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":"2026-08-14T12:54:41.070932Z","title":"Stere- oscan: Dense 3D reconstruction in real-time","venue":null,"work_id":"ccda3f94-ad90-43a1-883b-5178b08f7031","year":null},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.913819Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:9291ced3a10d74dd2397d8764ea71cdc1ec83cf64b3195bf5b21e434b538a076","observation_id":"b34ee35d-c2de-4423-a1c2-831f9d13a478","resolution":{"observed_at":"2026-08-14T12:54:41.074926Z","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":"2026-08-14T12:54:41.058313Z","title":null,"venue":null,"work_id":"3383a340-ecd9-4b1e-9bd7-dc9630cd398d","year":2017},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.918209Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:cd35f78cd0ceb2a24d660177f9d92e50f276c4dce03f2378fbaaf3bed51000f0","observation_id":"bbdc8010-19bb-4f90-a26b-2cefa0a4f146","resolution":{"observed_at":"2026-08-14T12:54:41.062493Z","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":"2026-08-14T12:54:41.045450Z","title":"NRSfM-Flow: Recovering Non-Rigid Scene Flow from Monocular Image Sequences","venue":null,"work_id":"652347d3-267e-4927-8a8f-aa76c5069b0f","year":2016},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.921846Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:6b674b31e7da037c1252c7d65355c43e2ed653f4d019fdc1b0e00d3dd8e017e4","observation_id":"864fdf8f-6653-4bb6-8099-041ea2aadd00","resolution":{"observed_at":"2026-08-14T12:54:41.050108Z","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":"2026-08-14T12:54:41.030481Z","title":"On Calibration of Modern Neural Networks","venue":null,"work_id":"b7da59c0-db65-4316-b937-8e03fd2d0b03","year":2017},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.925936Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:2c18417867234fc8004b213bed17f60e298ad405bf66a564ef389a1af312d444","observation_id":"41b2edac-4efd-4c5c-952b-abbc2365b981","resolution":{"observed_at":"2026-08-14T12:54:41.036683Z","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":"2026-08-14T12:54:39.929996Z","title":"Multiple view ge- ometry in computer vision","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.929996Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:83f3d1b9da9011d308057bef3eff474553f008a7ecffd8c7e5364119f35f371d","observation_id":"a6e74b38-e6d7-430e-ae58-c9840169313b","resolution":{"observed_at":"2026-08-14T12:54:39.929996Z","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":"2026-08-14T12:54:41.007029Z","title":"Mask R-CNN","venue":null,"work_id":"39493457-f69e-4ee6-8b57-e9a598db81c3","year":null},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.934455Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:4618670b5cf87f465df6d430b3f903d86864bc0e431f55f5bc2dbc19247944c1","observation_id":"6fedaab3-21e6-42cb-9b84-ade26b9c845c","resolution":{"observed_at":"2026-08-14T12:54:41.011181Z","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":"2026-08-14T12:54:40.993230Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"7b1384d9-0df4-4b03-aed6-4e85278714b6","year":2016},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.938659Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:5b6f218fc5c996e7c3b431adee39f9c65736f94995e278384474786038897557","observation_id":"004028b1-8b78-45ca-a6ad-021d45ff91cc","resolution":{"observed_at":"2026-08-14T12:54:40.997704Z","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":"2026-08-14T12:54:40.981508Z","title":"RGB-D flow: Dense 3-D motion estimation using color and depth","venue":null,"work_id":"26821b60-4d5b-4050-96be-1465c9b63cfc","year":2013},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.942371Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:3c97c21d3f8b3d4860e20645249c7caccb4ed9d23e9d4323e3fb7b23828f9244","observation_id":"54d2af98-7e73-43d0-8555-2f0ce9a578ef","resolution":{"observed_at":"2026-08-14T12:54:40.985519Z","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":"2026-08-14T12:54:40.969738Z","title":"Accurate and efficient stereo processing by semi-global matching and mutual information","venue":null,"work_id":"f1978b84-365c-4c7a-85df-39e2d0745081","year":2005},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.946104Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:e635398ae23904adafa45041d5bae2e1fc3b5eced647e6d704ed087f784a2a53","observation_id":"4c4a18f5-b5e2-426a-9c7b-bdb2bba77bf0","resolution":{"observed_at":"2026-08-14T12:54:40.973870Z","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":"2026-08-14T12:54:40.957579Z","title":"Auto- matic photo pop-up","venue":null,"work_id":"d6fb6e4b-b433-47dc-9933-d8baae339168","year":2005},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.950146Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:f7e4ccdb0240705ffac9cdc56794289c3163f6723d0a42cd5c98ae62135d2bfb","observation_id":"feabe885-baa1-4e68-9436-c0e8baa56ab3","resolution":{"observed_at":"2026-08-14T12:54:40.961424Z","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":"2026-08-14T12:54:40.945555Z","title":"SphereFlow: 6 DoF scene flow from RGB-D pairs","venue":null,"work_id":"a44b3d3d-93de-4a5d-be1a-7f6449db6108","year":2014},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.953983Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:39466045ba43069fba6f8ee975c0fd00dedffee8cb16c743788dc98c56961595","observation_id":"e90bc42a-7d51-4336-8e74-4cf93118990c","resolution":{"observed_at":"2026-08-14T12:54:40.949982Z","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":"2026-08-14T12:54:40.931738Z","title":"A variational method for scene flow estimation from stereo sequences","venue":null,"work_id":"f3610d37-e513-4b40-a750-2d6769253037","year":2007},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.958375Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:f4ada128c5151e43ea6d4fbdcd817a5b9f9e5f4fed315719e581ded88f668508","observation_id":"04f29e75-7d17-4ba2-bd50-7afc2fb316f5","resolution":{"observed_at":"2026-08-14T12:54:40.936153Z","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":"2026-08-14T12:54:40.918428Z","title":"MirrorFlow: Exploiting symmetries in joint optical flow and occlusion estimation","venue":null,"work_id":"90715193-03dc-41e0-a9dc-dc6aac583692","year":2017},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.962314Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:a0d5dedc9affe4f5b8a465519a63fda3f35e5c4ea92f777aa2a51f05cf728dc1","observation_id":"40fec886-07de-4445-b085-d21e433c484c","resolution":{"observed_at":"2026-08-14T12:54:40.922556Z","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":"2026-08-14T12:54:40.903787Z","title":"Uncertainty Esti- mates and Multi-Hypotheses Networks for Optical Flow","venue":null,"work_id":"e4194624-252f-46f5-8bdf-50b7da20b23d","year":null},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.965902Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:d874af68d06cb5bc70d1556567e408314549269d78a9b1b1307876c37bcaafe0","observation_id":"45a94192-fc5f-4f86-8083-fa360e5ebd21","resolution":{"observed_at":"2026-08-14T12:54:40.909131Z","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":"2026-08-14T12:54:40.892079Z","title":"What uncertainties do we need in bayesian deep learning for computer vision? In Proc","venue":null,"work_id":"85c8ee39-d59c-47dc-a736-611ad2175926","year":2017},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.970107Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:bcf73dc85b7eb34f2358b8d5b2153eb1c78fe2ee44ef554b4d5c222f13d8ae4d","observation_id":"fd6f0de8-cb81-47f6-93c9-fffeb16d3636","resolution":{"observed_at":"2026-08-14T12:54:40.896107Z","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":"2026-08-14T12:54:40.880771Z","title":"Adam: Amethod for stochastic optimization","venue":null,"work_id":"1a2cbb35-57ad-40d9-a448-0ba125d7db50","year":2014},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.973712Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:5efa31e7606a26edcc54eeaf9557d8fe682e744b3c75bf38df9c9a3a3c4886cc","observation_id":"6b2094ad-9e70-46e1-bf83-b0a931f63f65","resolution":{"observed_at":"2026-08-14T12:54:40.884697Z","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":"2026-08-14T12:54:40.869837Z","title":"Supervising the new with the old: learning SFM from SFM","venue":null,"work_id":"c54c4c92-e903-4d3d-aa6f-53e3d5a29728","year":2018},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.977724Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:7ef02e791d0ec3a8372d664a4a60bc795554e68e77687792036b519ee97fb3e1","observation_id":"5aba2187-c409-4a56-b6d2-bbcb3b095386","resolution":{"observed_at":"2026-08-14T12:54:40.874138Z","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":"2026-08-14T12:54:40.858540Z","title":"Accurate Uncertainties for Deep Learning Using Calibrated Regression","venue":null,"work_id":"41f14a9d-4f22-499a-8ed3-6d1c0e6fcf6d","year":2018},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.981593Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:456ebc72b7084990a738ca319a9d921425dbcf54e0165665adc0d16fa8343b75","observation_id":"457e306c-ef42-47b7-a2ed-386b4102db81","resolution":{"observed_at":"2026-08-14T12:54:40.862475Z","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":"2026-08-14T12:54:40.847595Z","title":"Monoc- ular dense 3D reconstruction of a complex dynamic scene from two perspective frames","venue":null,"work_id":"4c2ecc07-46e2-4746-be27-6a0d2cdbc676","year":null},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.985448Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:d3414ec6a7f5e9d1198654768ff5e5ff3d8f23e0c709a1094671ef1a5f9c417a","observation_id":"d3377645-5634-48d2-9002-edc52f7b2477","resolution":{"observed_at":"2026-08-14T12:54:40.851406Z","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":"1902.03791","last_updated":"2019-03-23T10:15:21Z","snapshot_observed_at":"2026-08-14T17:18:14.834753Z","submitted_at":"2019-02-11T09:45:52Z","title":"Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation","version":2},"cited_work":{"arxiv_id":"1902.03791","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.03791","snapshot_observed_at":"2026-08-14T12:54:40.215441Z","title":"Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation","venue":"cs.CV","work_id":"3308763b-ceb8-4f44-9360-0b95cfa3bc75","year":2019},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.991538Z"},"links":{"cited_paper":"/paper/1902.03791","citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:ba030aadbaa2fadfcc833ba983c73aa4efb648ff846309037252f1375909b485","observation_id":"2c07e08b-e628-4861-bfcc-7f1f5dabde3c","resolution":{"observed_at":"2026-08-14T12:54:40.222380Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-08-14T12:54:40.836234Z","title":"g 2 o: A general framework for graph optimization","venue":null,"work_id":"28253811-20c2-4f09-8a2d-2ee75505623d","year":null},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.996006Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:5cd08f938914d645445dc0231b106f3a3e0911f29c4b78e877b10d7d508bc8b0","observation_id":"42d2a73a-8607-4171-8a52-46b0575df5a3","resolution":{"observed_at":"2026-08-14T12:54:40.840298Z","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":"2026-08-14T12:54:40.825488Z","title":"Semi- supervised deep learning for monocular depth map predic- tion","venue":null,"work_id":"bc0c105b-377b-4949-8a1a-f2220600436c","year":2017},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:39.999839Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:26989c322a60538131a86a741978c13358749a3cb00cbb91469e6caa017f9e88","observation_id":"08e6344c-057d-46d5-a214-4db7395204c8","resolution":{"observed_at":"2026-08-14T12:54:40.829242Z","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":"2026-08-14T12:54:40.814413Z","title":"Pulling things out of perspective","venue":null,"work_id":"0374d22f-0597-4bc2-8f3c-4b127b122f06","year":2014},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.004493Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:018d33b2a107535bb3b77483802fc004587cb2615671465267a5f0558e2dee12","observation_id":"ae2b7451-9fe7-43ec-828f-c5e3eef4674c","resolution":{"observed_at":"2026-08-14T12:54:40.818239Z","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":"2026-08-14T12:54:40.803898Z","title":"Deep rigid instance scene flow","venue":null,"work_id":"10abb3c4-788f-47de-9a3c-c583d5b62d5c","year":2019},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.009280Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:bc20d07e6f55b4484e3aed59abe99ece14ad9521d151fb603a20e064c510749b","observation_id":"2fff3c18-f10a-45c1-ab64-934b9d50ab7c","resolution":{"observed_at":"2026-08-14T12:54:40.807633Z","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":"2026-08-14T12:54:40.792516Z","title":"Un- supervised Learning of Depth and Ego-Motion from Monoc- ular Video Using 3D Geometric Constraints","venue":null,"work_id":"3070a33d-641a-4e01-b512-240bd445c174","year":2018},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.012827Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:0cf0fd9603dc0684865a45b85a28e31e878c3d35ea935f63e011360ac2afb650","observation_id":"fdb7e466-c374-4179-a8ef-62b6bee46aba","resolution":{"observed_at":"2026-08-14T12:54:40.797106Z","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":"2026-08-14T12:54:40.780207Z","title":"Predictive uncertainty esti- mation via prior networks","venue":null,"work_id":"ba8a2019-a48e-4596-a1c9-d00614edeb1a","year":null},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.016996Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:41d0098a8f1c9c8863aa1ab255dd65b6ec0fb6af71ed28432d482ad7f0b8f485","observation_id":"13d6d8d2-70b0-4515-b2c7-fb01dd05856f","resolution":{"observed_at":"2026-08-14T12:54:40.784921Z","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":"2026-08-14T12:54:40.769151Z","title":"Object Scene Flow for Autonomous Vehicles","venue":null,"work_id":"666aee51-411c-47b5-926a-6f7da8ff981b","year":2015},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.028543Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:dc21130b0b2e2bdad51c2b2d4da5e575c99a5864415af8d417629c3cb03f58da","observation_id":"2eb2b4fa-2fc7-4411-bbd4-dfcd80337890","resolution":{"observed_at":"2026-08-14T12:54:40.773225Z","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":"2026-08-14T12:54:40.758696Z","title":"Ob- ject Scene Flow","venue":null,"work_id":"a8b0a4c2-8f48-4db1-ab8d-df69a0ac9f7b","year":2018},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.032612Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:7985facf2653d94fe43641cd9496823a1bbb2ecbf188f72342d3b380f658e6f7","observation_id":"01436d9e-f1a5-45a0-ada2-af5e41d71465","resolution":{"observed_at":"2026-08-14T12:54:40.762395Z","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":"2026-08-14T12:54:40.746834Z","title":"Monocular Concurrent Recovery of Structure and Motion Scene Flow","venue":null,"work_id":"81b0959e-2a09-497b-a14d-a18256f8c511","year":2015},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.036140Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:2225393dfb01b7eb518664790909fcfb604f82b9642f7a0b9aa9c36eed1d9335","observation_id":"932a1a2f-a734-40f8-aa00-d6af0c7c1873","resolution":{"observed_at":"2026-08-14T12:54:40.751368Z","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":"2026-08-14T12:54:40.735668Z","title":"ORB-SLAM: a versatile and accurate monocular SLAM system","venue":null,"work_id":"1076f219-a0e4-44ad-9bd7-8cc6859c7094","year":2015},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.040227Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:288a1f9eee438b150073625772001a011008d805ce13f72e1cb56b36fb134480","observation_id":"cf553353-023e-4408-8ff2-b803105616d6","resolution":{"observed_at":"2026-08-14T12:54:40.739574Z","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":"2026-08-14T12:54:40.724385Z","title":"Monocular Visual Odometry with Cyclic Estimation","venue":null,"work_id":"0f1b9915-386f-4088-bdc4-3f39b9d6a40e","year":2017},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.044733Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:9dfdb6c5903a478fa2ac770c825acff8fef799d51df886a096cf4f289c4ecac7","observation_id":"53b4c0c4-9365-44ed-a464-3794caa8bbbc","resolution":{"observed_at":"2026-08-14T12:54:40.728648Z","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":"2026-08-14T12:54:40.711531Z","title":"Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods","venue":null,"work_id":"8dd8df89-5faf-4812-a420-25cf0b2e60e6","year":1999},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.048109Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:7412277f5a4582e3e8821549be66e9a2902c88f7bf13690d7773f342aefbb0bc","observation_id":"750aa15a-03de-4a54-9644-41227f6268df","resolution":{"observed_at":"2026-08-14T12:54:40.715799Z","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":"2026-08-14T12:54:40.698941Z","title":"Multi-view stereo reconstruction and scene flow estimation with a global image-based matching score","venue":null,"work_id":"37248648-211e-418f-a8b2-d7b040f09653","year":2007},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.051800Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:031251b42be445104340188d4644670a3743505eaf7dd49c751042b678a9784d","observation_id":"c4705646-6151-4033-81d0-2bfc37c6b2a6","resolution":{"observed_at":"2026-08-14T12:54:40.703716Z","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":"2026-08-14T12:54:40.685107Z","title":"Dense monocular depth estimation in complex dy- namic scenes","venue":null,"work_id":"f0fdf509-394f-46ae-b5ed-4dbaa1f548f3","year":null},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.054924Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:9d5811ecab036653c694e3480f8cc81841d5294374ffcb61c0ea1c561f3466ef","observation_id":"3566cbd6-4f49-42f0-861f-72efd8001649","resolution":{"observed_at":"2026-08-14T12:54:40.689606Z","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":"2026-08-14T12:54:40.673969Z","title":"Learn- ing depth from single monocular images","venue":null,"work_id":"f0c36ecc-dfd4-4e23-b0d9-c0755a6861b4","year":2005},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.058075Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:5f6954d6731117d0b9ad039b0bbecfb610f8dcd58566357d4f357432df11637c","observation_id":"c3556374-0d9d-40e6-841b-acdbaed21a1f","resolution":{"observed_at":"2026-08-14T12:54:40.677818Z","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":"2026-08-14T12:54:40.662807Z","title":"Make3D: Learning 3D Scene Structure from a Single Still Image","venue":null,"work_id":"65e414d5-3cc6-4d8b-8553-2e124f7cd4e7","year":2009},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.061457Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:33a41f8c9947f169c2e91dae4aaa9bcd6a6570c0a291cb7261278712bc50973b","observation_id":"891defad-b491-4b67-b4c3-533b7373178f","resolution":{"observed_at":"2026-08-14T12:54:40.666560Z","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":"2026-08-14T12:54:40.651208Z","title":"CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction","venue":null,"work_id":"f4457399-cbf6-45d8-aa27-fa753be59da9","year":2017},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.064422Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:79affbfbf00289d64e820cdc4967d9862e1729bd4c16cb78c8c3b780934d2371","observation_id":"a00ead11-3d8c-4780-b6c8-cb7bf4bd4c0c","resolution":{"observed_at":"2026-08-14T12:54:40.655983Z","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":"2026-08-14T12:54:40.640483Z","title":"Occlusion- Aware Unsupervised Learning of Monocular Depth, Optical Flow and Camera Pose with Geometric Constraints","venue":null,"work_id":"ae8b1044-2eb9-455c-a905-beca94911955","year":2018},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.067611Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:76de635fd17e92060bfe8f2a107a1361a1a0b8ecdc1ebc69905f08504e058d35","observation_id":"480014f8-5961-43d0-951c-bf1cf958556f","resolution":{"observed_at":"2026-08-14T12:54:40.644559Z","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":"2026-08-14T12:54:40.628231Z","title":"Sparsity Invariant CNNs","venue":null,"work_id":"fef88cb6-7152-4d59-bc4e-7f75f568cfa1","year":2017},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.071152Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:2f428ef58aabf10bd426c4feff5fbf8acbe554623f70860dd506d34a65e548e0","observation_id":"53e29bc6-902a-454b-9536-d5752762ec19","resolution":{"observed_at":"2026-08-14T12:54:40.633158Z","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":"2026-08-14T12:54:40.616715Z","title":"DeMoN: Depth and Motion Network for Learning Monocular Stereo","venue":null,"work_id":"6921d976-fc32-4085-8ef9-f66cc7e84e9e","year":2017},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.075114Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:9307d681650bdbf9d33805ddc08782fdd89d471151c3c64e36fbafe76640f43c","observation_id":"7a662149-1148-4833-b241-872a829a0b3c","resolution":{"observed_at":"2026-08-14T12:54:40.620861Z","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":"2026-08-14T12:54:40.606029Z","title":"Joint es- timation of motion, structure and geometry from stereo se- quences","venue":null,"work_id":"759a30ba-2a22-494e-9ba1-19d2bc51abaa","year":2010},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.085085Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:a888ce169f56d95c2c66b918351922c8f7dc8c14e7cc38e37e5b28c5320c6b08","observation_id":"73c8ce3b-0b0f-46b1-b292-8aa18f2f4030","resolution":{"observed_at":"2026-08-14T12:54:40.610102Z","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":"2026-08-14T12:54:40.594350Z","title":"Three-dimensional scene flow","venue":null,"work_id":"bddf3910-e596-42a0-a896-40c94f072a1a","year":1999},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.090475Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:5c283e7f19797e272c3f0ac84ebb06e96f9cd37b050ad12b33ef513ac44c7407","observation_id":"d32c3530-921c-489d-b986-6f56d0db6a05","resolution":{"observed_at":"2026-08-14T12:54:40.598580Z","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":"2026-08-14T12:54:40.582193Z","title":"Three-dimensional scene flow","venue":null,"work_id":"8dd980e6-d03a-4ea9-bc99-013b388b8989","year":2005},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.095981Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:7c647b3e18352f5e8413de2d848fbd7b6efd95b7e2819992b01e725d0d4768b5","observation_id":"4ea17cdf-111f-4d96-8fe0-d2900cb03f5d","resolution":{"observed_at":"2026-08-14T12:54:40.586139Z","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":"2026-08-14T12:54:40.569974Z","title":"Piece- wise rigid scene flow","venue":null,"work_id":"edfa5c9e-875d-46d3-94f3-ca23365d5623","year":null},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.101890Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:78e973f540b0d79ec7f3a64ec97b953f0c3f98902b3467c5bbc5685f32968310","observation_id":"d4ec7a7a-8f3f-4611-8c82-e7d4671913c2","resolution":{"observed_at":"2026-08-14T12:54:40.574145Z","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":"2026-08-14T12:54:40.558128Z","title":"Learning Depth from Monocular Videos using Direct Methods","venue":null,"work_id":"e0a57842-b223-4521-8006-2cf965ae0481","year":2022},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.108385Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:cee7cbe5210fe9d4a2b3e528ded27cb6d6f4459001cbe312c71dafe591c5c4ec","observation_id":"63afd2da-88a3-4f99-8eb8-4952184f662d","resolution":{"observed_at":"2026-08-14T12:54:40.561911Z","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":"2026-08-14T12:54:40.546790Z","title":"An MXNet implementation of Mask R-CNN","venue":null,"work_id":"f044fd90-980c-415a-8ea7-73d68a4edb1d","year":2018},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.114193Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:7bd46a455a666447d043ebba8d3604f495bd085abb2698a55b7bedf747df24cc","observation_id":"eb017dfa-3296-44fd-85b1-39b061dbfe0a","resolution":{"observed_at":"2026-08-14T12:54:40.551102Z","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":"2026-08-14T12:54:40.535510Z","title":"Stereoscopic scene flow computation for 3D motion understanding","venue":null,"work_id":"7ab8f127-7de5-4b90-a21b-348c15515f3b","year":2011},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.118366Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:f18ee673da425e1d778051715ec642536544e2f4f7a4cb05391c60b7451f1827","observation_id":"4bf3bdfe-d3e6-41fb-a1c4-0cfe29659575","resolution":{"observed_at":"2026-08-14T12:54:40.539540Z","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":"2026-08-14T12:54:40.523573Z","title":"Efficient dense scene flow from sparse or dense stereo data","venue":null,"work_id":"ebe14092-983a-47af-ab32-e462be4068d8","year":null},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.122211Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:10f84188e2d2849156d72e1e2f9ff28a3b3d45dd19d032884a3c2c5f7d09e22a","observation_id":"ed2a2d2b-9921-48aa-9335-7d16a436ed93","resolution":{"observed_at":"2026-08-14T12:54:40.527873Z","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":"2026-08-14T12:54:40.509038Z","title":"Monoc- ular scene flow estimation via variational method","venue":null,"work_id":"86315c9f-3be8-4a82-9d15-444a2d946e6b","year":2017},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.125382Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:1e59b943f0d145afeaa7d34232d35a07e4c36015e14129d90364b014b1eb94ef","observation_id":"b91b2b37-53d1-4490-b513-59f012f7f459","resolution":{"observed_at":"2026-08-14T12:54:40.513829Z","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":"2026-08-14T12:54:40.371570Z","title":"Robust monocular epipolar flow estimation","venue":null,"work_id":"f1c0bda4-af4b-4ea7-b39b-e14c70283245","year":2013},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.129220Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:111c239e817cbfe16a4e70beb8f73ca54e6e685e802a996216052cc9d4f38d2c","observation_id":"af61f439-b54d-46f0-8347-0e428804ec04","resolution":{"observed_at":"2026-08-14T12:54:40.375923Z","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":"2026-08-14T12:54:40.355345Z","title":"Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation","venue":null,"work_id":"42eab3b2-4855-4864-a55f-385ac97c9c33","year":2014},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.132607Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:ecc825dbabed4cc5a981e93ffcad236bf2904aa32c9ec14e908ce9b099d7f7aa","observation_id":"d56ddaa1-76a5-46ff-8ba0-43afd699fb82","resolution":{"observed_at":"2026-08-14T12:54:40.359795Z","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":"2026-08-14T12:54:40.342469Z","title":"Deep virtual stereo odometry: Leveraging deep depth pre- diction for monocular direct sparse odometry","venue":null,"work_id":"06bf1d7f-ea72-46f8-9346-f0f0cfa85eaf","year":2018},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.136316Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:1e06b46b68d41931b2e3724499d3a96f5c0e4d46feaa9579bb94bf9cb7343597","observation_id":"007d8192-e6b7-4204-aeaa-e3c1cc0d215c","resolution":{"observed_at":"2026-08-14T12:54:40.346683Z","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":"2026-08-14T12:54:40.329552Z","title":"Every Pixel Counts: Unsupervised Geom- etry Learning with Holistic 3D Motion Understanding","venue":null,"work_id":"eb7a00d1-2cdf-457c-810c-6e93afc8a0fb","year":2018},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.139711Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:30b7214709624b09bebaa49e77fc139216b9f9f7ff5efd167735f4e71e647bd5","observation_id":"824bb0e4-1d7b-476c-896c-422016c65c38","resolution":{"observed_at":"2026-08-14T12:54:40.334128Z","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":"2026-08-14T12:54:40.317269Z","title":"Scale recovery for monocular visual odometry using depth estimated with deep convolutional neural fields","venue":null,"work_id":"e1a95818-31a9-43f4-93d5-b84381e0a369","year":2017},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.144462Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:b5d3610e94eb7ad974c6cccf2fae3495de3eaa64c9a4acc4a45cd684a8179e7e","observation_id":"09376317-d567-495e-a95b-daf81fc3ddb8","resolution":{"observed_at":"2026-08-14T12:54:40.321678Z","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":"2026-08-14T12:54:40.304618Z","title":"Hierarchical discrete distribution decomposition for match density esti- mation","venue":null,"work_id":"06ff8bcd-169a-46d2-8915-d9859a582952","year":2019},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.149050Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:c920467693f053a0a16e747943783ad222be96cb60090b1c4a140a82f72cd0d0","observation_id":"869f9c0e-90ff-49bd-a03e-248cbce0ad34","resolution":{"observed_at":"2026-08-14T12:54:40.309714Z","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":"2026-08-14T12:54:40.290786Z","title":"GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose","venue":null,"work_id":"5973b7fa-9b83-4609-87f2-16500248a8c5","year":2018},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.153244Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:51049a8933a65afb0cf11ead94891edcd17ab82b757a9048176c79ac8005932d","observation_id":"5db9d466-3410-4b1f-a931-179f4fa7c09f","resolution":{"observed_at":"2026-08-14T12:54:40.295485Z","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":"2026-08-14T12:54:40.275787Z","title":"Non-parametric local trans- forms for computing visual correspondence","venue":null,"work_id":"53101eb8-6ee4-4c75-ab94-40274e3a74c8","year":null},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.158444Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:c5c4d43bfa541eca387adcb98db263e411632e0dc2e04549d661f8d0a4787aee","observation_id":"f4bfa4bf-ae16-43b8-bcc5-1a5f4354508d","resolution":{"observed_at":"2026-08-14T12:54:40.280857Z","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":"2026-08-14T12:54:40.262231Z","title":"Unsupervised Learn- ing of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction","venue":null,"work_id":"ec4f8346-cf2e-4c80-9500-20f49649f9a3","year":2018},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.162642Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:5e5df7654b206765e4e9f05ce4400f2c76e77dd53584a8b65f7f4d1cde3d473b","observation_id":"37439b93-c532-477e-bc81-cf785ee90734","resolution":{"observed_at":"2026-08-14T12:54:40.266572Z","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":"2026-08-14T12:54:40.248057Z","title":null,"venue":null,"work_id":"0445b778-2647-4201-a253-a97a4935bb25","year":2017},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.166570Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:ab45edb84f93a8de8e594cc0a175efe0dc5a58aa8ea4739b8ddfd53c5590e2d1","observation_id":"be9f3687-cbd2-4b02-8cf0-bb546dc2a140","resolution":{"observed_at":"2026-08-14T12:54:40.252706Z","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":"2026-08-14T12:54:40.232735Z","title":"DF-Net: Un- supervised Joint Learning of Depth and Flow using Cross- Network Consistency","venue":null,"work_id":"d25cf080-a0fa-497d-9d75-22bdda2acd8f","year":2018},"citing_paper":{"arxiv_id":"1908.06316","last_updated":"2019-08-17T15:44:12Z","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:40.171315Z"},"links":{"citing_paper":"/paper/1908.06316"},"observation_digest":"sha256:3d5bf7a37e2ba6f142972d71bafe2b9e074ef5b689c72189715ecc06dd85067b","observation_id":"8f57ac9a-d5c2-4c9f-882d-3dc5bc71d1b1","resolution":{"observed_at":"2026-08-14T12:54:40.237174Z","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":"1908.06316","last_updated":"2019-08-17T15:44:12Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T15:51:04.182480Z","submitted_at":"2019-08-17T15:44:12Z","title":"Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes"},"reference_resolution":{"displayed":77,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":1,"verified_fuzzy":73},"total_outbound_references":77},"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 77 of 77 outbound references and 0 inbound Pith citation observations for arXiv:1908.06316."}