{"as_of":"2026-08-23T10:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d297fb929e5b10357fdcad1ecf26675cbdfeb0f6c18d1cd66a194101a2d7b6cb","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T15:35:19.072729Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+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/2507.15496/citation-record","integrity":"/paper/2507.15496/integrity","json":"/paper/2507.15496/citation-record.json","paper":"/paper/2507.15496"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:35:19.470882Z","title":"Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping,","venue":null,"work_id":"25637c48-3669-4df4-81d6-939480c4955d","year":2020},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.951836Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:48b5e2371de1084d721e448cb350ff49f9e5e6d8b957e83f85f41f0b1f636fa1","observation_id":"7f80d67f-40ea-4081-b86a-fea014065f13","resolution":{"observed_at":"2026-08-06T15:35:19.474405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.461884Z","title":"Visual-lidar odometry and mapping: Low-drift, robust, and fast,","venue":null,"work_id":"42d83252-e345-495b-90ef-757702dc1341","year":2015},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.954994Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:7fd9fa8f4364d343320c9a0143ec71211fd1fa2f29330e24b021b369347353d0","observation_id":"c709f784-18d4-4084-b3b9-df5464808909","resolution":{"observed_at":"2026-08-06T15:35:19.465026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.453500Z","title":"Self-supervised visual- lidar odometry with flip consistency,","venue":null,"work_id":"257cfcd9-9654-4789-801e-026c182f5e99","year":2021},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.957516Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:5b5a21753234c967c2854de7ca1d378e348b6018a08731511b41960e3e00825f","observation_id":"9c7683de-d0f1-48ba-8a2b-abd229bd3931","resolution":{"observed_at":"2026-08-06T15:35:19.456484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.445073Z","title":"Cnn-slam: Real-time dense monocular slam with learned depth prediction,","venue":null,"work_id":"d4ac73a9-8a79-42e5-b302-3de55fa8114c","year":2017},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.960021Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:72304bf03ef44d5e3b5b5ea8cf7ec44923b29ace134d60537a4469e1fd88d892","observation_id":"3a4c546f-50b3-4ffe-a919-625e9d1e9454","resolution":{"observed_at":"2026-08-06T15:35:19.448088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.437088Z","title":"Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume,","venue":null,"work_id":"d590ae3d-a9ca-4a84-93a8-8896be46dfe2","year":2018},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.962976Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:87e0c100ee56441edd38a96eab144b732a74835e54865858e4befc73c74d8f21","observation_id":"951fa2e2-b6db-4888-89ef-84adbf7469c5","resolution":{"observed_at":"2026-08-06T15:35:19.439875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.428899Z","title":"Lidar odometry and mapping based on semantic information for outdoor environment,","venue":null,"work_id":"763996ed-a40a-4c36-b1c8-a142bd26c62b","year":2021},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.965488Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:d1a4f13125947934eb27ea8419424e974ceba25403f1e58b565522e44badc2fb","observation_id":"98ad9503-d6e0-4c45-941b-9599550a2557","resolution":{"observed_at":"2026-08-06T15:35:19.431662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.420810Z","title":"Loam: Lidar odometry and mapping in real- time","venue":null,"work_id":"f7a6ea36-f08e-4d3b-a7b1-594862ac2634","year":2014},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.968304Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:0589f3bdd88bf6eb964506b036ff9570ae824e13cacb705c7a3da2ebd4ec915a","observation_id":"e7ab5c7a-878a-47b0-809d-ec5dcc5d9e25","resolution":{"observed_at":"2026-08-06T15:35:19.423503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.412151Z","title":"Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain,","venue":null,"work_id":"7f60645d-4bc5-43d6-aeed-53295a80813d","year":2018},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.970737Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:9c6da5a8b33e2549f7a890f8f205f0a5334b114ef5ea8babd2e83ec85bc58be4","observation_id":"8c14385f-26f9-4ba3-b05c-6f232417d82d","resolution":{"observed_at":"2026-08-06T15:35:19.415114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:18.973258Z","title":"Vision meets robotics: The kitti dataset,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.973258Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:671891aea1441cca33e29f6128ea53a2ea7fadebb257ad0788786efff999ea71","observation_id":"c6e51ffd-4080-4ae7-a9f2-9531df435948","resolution":{"observed_at":"2026-08-06T15:35:18.973258Z","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":"2026-08-06T15:35:19.399609Z","title":"Depth completion from sparse lidar data with depth-normal constraints,","venue":null,"work_id":"78115088-168f-4c20-b447-bea59cebc160","year":2019},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.975753Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:c292d423fef610c1defbbb481496ed0be30fd4795907463e122e6144e5ece381","observation_id":"51e3ae7c-017a-498d-8741-351f2a9cf0ee","resolution":{"observed_at":"2026-08-06T15:35:19.402195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:18.978087Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.978087Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:c87d0c20baa5e7184b72731954e77b358fe1f82b86198512ba8af688d2bf97de","observation_id":"dc83bd3a-0be2-4521-88f5-b168cf2ff653","resolution":{"observed_at":"2026-08-06T15:35:18.978087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:35:18.980250Z","title":"Are we ready for autonomous driving? the kitti vision benchmark suite,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.980250Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:e39b669f2d5fd6697a75ef6a4731328af465b53ca492d3460a482670af49d119","observation_id":"b62529b3-5357-475b-bda2-5befb83338a3","resolution":{"observed_at":"2026-08-06T15:35:18.980250Z","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":"2026-08-06T15:35:19.380394Z","title":"Orb-slam: a versatile and accurate monocular slam system,","venue":null,"work_id":"667541ad-099a-4504-9386-f05b79173275","year":2015},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.982795Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:d3b37f43989c14c79429debadc1d1ab74059c15d4c811045c271c2391f40b399","observation_id":"5edd0bb2-6e77-457e-bdc0-5c2cd2f7e014","resolution":{"observed_at":"2026-08-06T15:35:19.382971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.372763Z","title":"Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras,","venue":null,"work_id":"7867b648-63bb-4dc4-98c0-20559ef376f6","year":2017},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.985162Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:993459edea4ca3d4c8b9abb5ea25294700652d4cf1bcd468108f6d69deee500c","observation_id":"f15bb8ec-fd24-49ba-877d-4d48b9407588","resolution":{"observed_at":"2026-08-06T15:35:19.375331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:18.987604Z","title":"Direct sparse odometry,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.987604Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:49ca0d8624d287445187b851372490152dc1c16ffed563374e43843da39474b8","observation_id":"4f5e4043-9286-4cb6-95a2-d478f255f7e4","resolution":{"observed_at":"2026-08-06T15:35:18.987604Z","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":"2026-08-06T15:35:19.359406Z","title":"Dtam: Dense tracking and mapping in real-time,","venue":null,"work_id":"75118c9e-3e02-4c13-98f2-27153bcf082b","year":2011},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.990040Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:31aef7ad1633cdcf7221bd44b6ba31d22f6d57a2774bfe9f6c1b71ea519ae761","observation_id":"6d26aab7-20eb-4214-bee1-af8896d09e10","resolution":{"observed_at":"2026-08-06T15:35:19.362018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.351677Z","title":"Lsd-slam: Large-scale direct monocular slam,","venue":null,"work_id":"79136ffb-153b-4074-aec9-47040c296e05","year":2014},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.992416Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:2e4055c7e2e341049b1b500c6738a061985841f5ebfd432209a0214b1b28cc35","observation_id":"c2dc0b9e-bc6f-42e8-aa5b-96aa051fe6f4","resolution":{"observed_at":"2026-08-06T15:35:19.354313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.343522Z","title":"Deepvo: Towards end-to- end visual odometry with deep recurrent convolutional neural networks,","venue":null,"work_id":"4b98e82a-92cb-4af7-9a68-18c293de2010","year":2017},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.994777Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:a7d9ee3ae2c029c2f7c9b9b8cf96fc21aae9e6f8a86fbe159e618644e68d6292","observation_id":"c11a6f8d-58d9-4406-b6e4-3d04b51bddd8","resolution":{"observed_at":"2026-08-06T15:35:19.346320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00933","last_updated":"2021-03-01T11:50:39Z","snapshot_observed_at":"2026-08-16T18:42:13.958443Z","submitted_at":"2021-03-01T11:50:39Z","title":"DF-VO: What Should Be Learnt for Visual Odometry?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.00933","snapshot_observed_at":"2026-08-06T15:35:18.997147Z","title":"Df-vo: What should be learnt for visual odometry?","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.997147Z"},"links":{"cited_paper":"/paper/2103.00933","citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:96b30526ae3b958e723d0e26d0fd1b4294ded04984326286592cb49a80d6a26c","observation_id":"c3902ca7-4235-40df-ba29-81360f7f27b9","resolution":{"observed_at":"2026-08-06T15:35:18.997147Z","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":"2026-08-06T15:35:19.335048Z","title":"Raft: Recurrent all-pairs field transforms for op- tical flow,","venue":null,"work_id":"88346037-36e1-474b-965e-a8c8e239ff7f","year":2020},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:18.999796Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:cd7e7307d6dc1bf7bbe1223a6fa6c68ce209844a94fd67a98875095ab6df5ba4","observation_id":"989f5edd-55d2-4be3-a1c7-175ae6955423","resolution":{"observed_at":"2026-08-06T15:35:19.338110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.327088Z","title":"Unsupervised learning of monocular depth estimation and visual odom- etry with deep feature reconstruction,","venue":null,"work_id":"6ba6f32e-7757-4d46-a305-291256c68ac1","year":2018},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.002501Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:6f87e4bedd0887a639aa1350781582d6db3440902011a357819b47093192c29a","observation_id":"65dad3a1-5f76-4510-9f36-ae56accf8d63","resolution":{"observed_at":"2026-08-06T15:35:19.329975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.004882Z","title":"D3vo: Deep depth, deep pose and deep uncertainty for monocular visual odometry,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.004882Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:b8071d67c36ac24e91b023d673705b6aac2445c1611c5a7d45c13b3f272d62b6","observation_id":"589bdf03-f996-46b4-9bac-2ae7f06ecfb2","resolution":{"observed_at":"2026-08-06T15:35:19.004882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:35:19.007589Z","title":"Method for registration of 3-d shapes,","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.007589Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:362ea98e4996deb65719657153fe430e83fa0c89112171c63a18e758f7f4f78b","observation_id":"9bf6c3dd-598c-4944-a88d-73470ae2068b","resolution":{"observed_at":"2026-08-06T15:35:19.007589Z","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":"2026-08-06T15:35:19.308716Z","title":"Tightly coupled 3d lidar inertial odometry and mapping,","venue":null,"work_id":"36bac408-49ba-426d-9916-33e92720db18","year":2019},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.009774Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:77e5cdf805c87f74266aeddf5aa81fb7e965555ea9c7c49c529ede8b7c1f2b68","observation_id":"97d49b7c-5941-467a-8176-d57c443bbf55","resolution":{"observed_at":"2026-08-06T15:35:19.311152Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.301207Z","title":"Lo- net: Deep real-time lidar odometry,","venue":null,"work_id":"c5b394b4-de40-4858-9eb6-d429670ce939","year":2019},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.012117Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:79209f0db48710fc2090ff2fb68ac249bb955bf775ea7c6adb4e798809aa9be2","observation_id":"aeb5c84c-f293-4799-8067-135693592c75","resolution":{"observed_at":"2026-08-06T15:35:19.303858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.10562","last_updated":"2019-02-27T14:48:42Z","snapshot_observed_at":"2026-08-20T16:08:45.710361Z","submitted_at":"2019-02-27T14:48:42Z","title":"DeepLO: Geometry-Aware Deep LiDAR Odometry","version":1},"cited_work":{"arxiv_id":"1902.10562","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.10562","snapshot_observed_at":"2026-08-06T15:35:19.098007Z","title":"DeepLO: Geometry-Aware Deep LiDAR Odometry","venue":"cs.RO","work_id":"841eed04-25e6-46e8-8ef9-466e7a92403a","year":2019},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.014410Z"},"links":{"cited_paper":"/paper/1902.10562","citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:1692ade4eb56c3151af1b24ca868dd5a607c02334252aef121b0ad93d66335a0","observation_id":"7d3f05f7-3b66-4458-a0c3-be45a8173256","resolution":{"observed_at":"2026-08-06T15:35:19.103120Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.293052Z","title":"Pwclo-net: Deep lidar odometry in 3d point clouds using hierarchical embedding mask optimization,","venue":null,"work_id":"67167077-57e6-4cbb-b351-d07ee50359eb","year":2021},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.017137Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:a1cb502038e5a4ff44c2874bfda8cc745b741cc6cf5c63fa568188802f963c0c","observation_id":"c801dfc7-2467-43d1-abc1-781336aea458","resolution":{"observed_at":"2026-08-06T15:35:19.296180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.284806Z","title":"Lodonet: A deep neural network with 2d keypoint matching for 3d lidar odometry estimation,","venue":null,"work_id":"e8862e63-ead6-4a85-8fd3-3dfe9b7b1a59","year":2020},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.019578Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:b97023adfff70d4d515b0c29fe92c21ef3d87d66be3cfa0ce30669f420c14657","observation_id":"1706b5eb-92ca-4c8d-9c37-501935b7b7b2","resolution":{"observed_at":"2026-08-06T15:35:19.287426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.277272Z","title":"Illumination invariant imaging: Applications in robust vision-based localisation, mapping and classification for autonomous vehicles,","venue":null,"work_id":"6287888b-bf90-460b-a841-5d2293fdc971","year":2014},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.022143Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:0148f586edfe2d8867ca00e33a421d884cc1c1f16c3be294b1196b01152d3b68","observation_id":"95ddcd3e-d110-4e0d-a1ce-47c53c3255ae","resolution":{"observed_at":"2026-08-06T15:35:19.279878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.268593Z","title":"Dvlo: Deep visual-lidar odometry with local-to-global feature fusion and bi- directional structure alignment,","venue":null,"work_id":"7f5b1148-25e9-4655-965a-d480b1436aa0","year":2024},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.024506Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:cebe020d019e6925ce27692becf0772ff59de7fa12d0f2627217afe4d6acccfc","observation_id":"70da1640-23f5-4c9d-b083-cc4bfddf0695","resolution":{"observed_at":"2026-08-06T15:35:19.272096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.260397Z","title":"Visual-lidar slam based on unsu- pervised multi-channel deep neural networks,","venue":null,"work_id":"bc779478-bf23-4fe2-9853-894dc6bbf99b","year":2022},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.026763Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:bb148971d835ff4bc8c509ef927ebc85cc9388dfa211df310c6538ac95b571e5","observation_id":"4e554308-7c4d-4991-b2d5-08b3a6d6d1f5","resolution":{"observed_at":"2026-08-06T15:35:19.263158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.252262Z","title":"Penet: Towards precise and efficient image guided depth completion,","venue":null,"work_id":"6dda26e0-0f39-45a1-8b9e-123f799143cf","year":2021},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.029115Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:a92d1ff2c65a6746cb17fa74c66241bd6f77bbab644ee93a4207b5f69ea81615","observation_id":"c529d5f5-0a30-4be1-8ba8-951cc4ae6368","resolution":{"observed_at":"2026-08-06T15:35:19.254788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.032112Z","title":"Squeeze-and-excitation networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.032112Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:702d11a14ec7c0bcd7802b9deedc1bd03d1f85b3f6cd7c70b356e998a4fba47b","observation_id":"c9239060-1458-432f-9f67-ed248afd8e07","resolution":{"observed_at":"2026-08-06T15:35:19.032112Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:35:19.034521Z","title":"Cbam: Convolutional block attention module,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.034521Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:edd7f1ef71f61417ca22c5a74e1c12b0ff5efb8b197a4aa4ae7510305c324a77","observation_id":"dea49cde-2123-4713-bd43-0018d071324b","resolution":{"observed_at":"2026-08-06T15:35:19.034521Z","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":"2026-08-06T15:35:19.234195Z","title":"Liteflownet: A lightweight convo- lutional neural network for optical flow estimation,","venue":null,"work_id":"962476c4-4dee-42d8-9bb2-9d56ee72e4f9","year":2018},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.036961Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:57173f7c21e9927d8da12a7b0e823c6212deaaba7b879d7d3b26e698408d50bb","observation_id":"75adfa30-2f66-4dba-b247-5f9f79fab562","resolution":{"observed_at":"2026-08-06T15:35:19.237246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.226467Z","title":"Maskflownet: Asymmetric feature matching with learnable occlusion mask,","venue":null,"work_id":"3f42792a-bdf6-4f61-95fc-b25e29d6fa84","year":2020},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.039498Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:4a579534359aba3e965ffeeae70d65f7d1e4de75ad987a96d260cfe32b920e47","observation_id":"9792c978-bfc7-4f18-8381-b42cefa50e0c","resolution":{"observed_at":"2026-08-06T15:35:19.229132Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.041856Z","title":"Just go with the flow: Self-supervised scene flow estimation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.041856Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:6a010dcafe599c1da2a76d5be364b6207ac0edbaad58c9a33401fcba7d2aa527","observation_id":"46ab984e-98ab-425e-91e2-9f24a0a09fac","resolution":{"observed_at":"2026-08-06T15:35:19.041856Z","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":"2026-08-06T15:35:19.213602Z","title":"Rethinking optical flow from geometric matching consistent perspective,","venue":null,"work_id":"78b7c8c6-39ea-44eb-9e9b-6861044a5f63","year":2023},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.044385Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:ce810ed37455f3efaaa36145fbe9a387600224b4203523affc239c4e9019dfee","observation_id":"8d867eb8-fcd9-45f2-ae06-e13bcbeec217","resolution":{"observed_at":"2026-08-06T15:35:19.216431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.206328Z","title":"Depth- aware video frame interpolation,","venue":null,"work_id":"4794c571-c657-4b4f-a885-64319ae13478","year":2019},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.046841Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:a530cf7ddb91556d7ecd0776e5cffc48ea6dd21ac4b17e39904dbc44cf08330a","observation_id":"6f56942c-0c0a-4e96-b358-e03935ef8b71","resolution":{"observed_at":"2026-08-06T15:35:19.208881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.198526Z","title":"Unsupervised learning of depth and ego-motion from video,","venue":null,"work_id":"ef703d82-1018-4f8b-8128-d27169dc3395","year":2017},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.049089Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:36b167452effcd23300a7188a89b22fc7f62b72863c16fb5ca89560efdadf227","observation_id":"e6a88bb9-8196-4e0c-a089-5d1b19c80860","resolution":{"observed_at":"2026-08-06T15:35:19.201302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.190576Z","title":"Generalizing to the open world: Deep visual odometry with online adaptation,","venue":null,"work_id":"9d09d918-9b50-433b-ac9f-aa908c2c0c7a","year":2021},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.051335Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:32791f838800b6ded64685af651f0980ca9a514208a43b733f898812afaa02a9","observation_id":"a4736c0e-09d2-4024-a2cf-991bfe8f84a2","resolution":{"observed_at":"2026-08-06T15:35:19.193395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.182555Z","title":"H-vlo: hybrid lidar-camera fusion for self-supervised odometry,","venue":null,"work_id":"de0f98a9-5949-4820-a684-91c3547fb1c3","year":2022},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.053665Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:df6d8061d363f6cb53babc94129eca2038dfa2f7348f7a324debaced517ab607","observation_id":"bc921045-f3ce-4ee5-8cfd-e5fb1e535cf1","resolution":{"observed_at":"2026-08-06T15:35:19.185236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.174500Z","title":"Dvl-slam: Sparse depth enhanced direct visual-lidar slam,","venue":null,"work_id":"ab8f463e-2cb9-495e-ab35-665fdaec40ec","year":2020},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.055992Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:0e49298fbfce258e159ec4cdc3b1aacee29f268fd80cc9c2afb957523b457dee","observation_id":"3edab751-962a-4018-976e-6cac49115902","resolution":{"observed_at":"2026-08-06T15:35:19.177219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.166715Z","title":"Lidar- monocular visual odometry using point and line features,","venue":null,"work_id":"33754a02-dfe6-4ba0-8320-543f6d127adc","year":2020},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.058425Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:752d265d1def1e9c252f320f23eb80cde79af1fa466d502a44e4b826058cbb30","observation_id":"0efeaedd-b9a6-41f0-a1b9-97fe8e966a0a","resolution":{"observed_at":"2026-08-06T15:35:19.169309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.158565Z","title":"Efficient 3d deep lidar odometry,","venue":null,"work_id":"1b7abb42-ce87-48b9-909f-00d2e628ed04","year":2022},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.060829Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:5f5e1646ac603b602dd51cfc9d0be443ffd73eb5238946e61fb47d5dbab1e914","observation_id":"b7e4f13a-87e1-45a1-8800-97c357860be4","resolution":{"observed_at":"2026-08-06T15:35:19.161373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.149916Z","title":"Selfvio: Self-supervised deep monocular visual–inertial odometry and depth estimation,","venue":null,"work_id":"9e74bbec-5e48-4615-8596-0bf808a8d18c","year":2022},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.063453Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:38ccd9bb2aad1c9e004c5d56d8664878a3fb584bd89e449bc37796958a4d7cdc","observation_id":"baa1b184-d2d9-4281-a77b-5298aff1cfe0","resolution":{"observed_at":"2026-08-06T15:35:19.152964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.142156Z","title":"Un- supervised deep visual-inertial odometry with online error correction for rgb-d imagery,","venue":null,"work_id":"98e569f3-acfe-484b-9292-64aaf043ecbf","year":2019},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.065714Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:bf288d674b34fd43284fb46a94f65aa73a9f090e6c63efb54266e257597dfdd9","observation_id":"8d4fc6c4-2ef7-42fe-abcb-eff92a6e5da4","resolution":{"observed_at":"2026-08-06T15:35:19.144912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.133740Z","title":"Self-supervised depth comple- tion from direct visual-lidar odometry in autonomous driving,","venue":null,"work_id":"578bf60e-082b-49d2-ac3d-6d9fa0476739","year":2021},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.068047Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:c3ac0e599a6d12bc955c9b9689043a261cc1f0841a0d00c8a50e38fbdf845d32","observation_id":"67c97013-ed81-4b3a-b526-8aed8d6cb542","resolution":{"observed_at":"2026-08-06T15:35:19.136903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.125726Z","title":"Recent advances in conventional and deep learning-based depth completion: A survey,","venue":null,"work_id":"7b1f97c8-231f-448b-9925-cf2fe49ec63f","year":2022},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.070367Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:d379fc52dd93fb012b1dd447cda38991ee0450d484d6e79ddaf479dd1dce9751","observation_id":"542e1920-7271-48c0-a863-484d8f2cf69f","resolution":{"observed_at":"2026-08-06T15:35:19.128577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T15:35:19.117404Z","title":"Multi-sensor fusion self-supervised deep odometry and depth estimation,","venue":null,"work_id":"65ff1e0d-9dd9-4158-a4d4-426cbcd53a1c","year":2022},"citing_paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T15:35:19.072729Z"},"links":{"citing_paper":"/paper/2507.15496"},"observation_digest":"sha256:6d83960384646d36857f2171232a8d9b64a8b7da5843dbfd521ef89625defaa7","observation_id":"a3eea151-2abf-4474-ab5c-d3831e786af0","resolution":{"observed_at":"2026-08-06T15:35:19.119995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.15496","last_updated":"2025-07-21T10:58:10Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-08T04:30:31.927867Z","submitted_at":"2025-07-21T10:58:10Z","title":"Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":1,"verified_fuzzy":39},"total_outbound_references":50},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2507.15496."}