{"as_of":"2026-08-17T01:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:69f61104efc671af49eb70b4fed6521072219e7ce78f9578c36aa8745669cdd4","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T11:37:59.843491Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"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/1908.08704/citation-record","integrity":"/paper/1908.08704/integrity","json":"/paper/1908.08704/citation-record.json","paper":"/paper/1908.08704"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:38:01.390438Z","title":"Generative Adversarial Networks for Unsupervised Monocular Depth Prediction","venue":null,"work_id":"fcf047e0-ec9e-4a9c-897b-13d0e06b8928","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.514338Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:d33adbcb32fab2f858dbc100f320c7d206dcb386a3d0731eb1972b79ad9bd927","observation_id":"97a8cbd5-f60e-4a12-965a-a76fb8508ebe","resolution":{"observed_at":"2026-08-14T11:38:01.398091Z","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":"2026-08-14T11:38:01.355143Z","title":"Wasserstein Generative Adversarial Networks","venue":null,"work_id":"40e03d52-0348-4ae2-95f9-fece92e7048c","year":2017},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.520659Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:712f1824159fc867945ce65427644b999eed614b6646efbabcd39acd966a1152","observation_id":"bcc9a4a2-81c5-47b2-9a47-8c88713e785c","resolution":{"observed_at":"2026-08-14T11:38:01.365745Z","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":"1809.00969","last_updated":"2018-11-27T09:21:17Z","snapshot_observed_at":"2026-08-14T18:36:46.778060Z","submitted_at":"2018-08-27T11:40:58Z","title":"A Deeper Insight into the UnDEMoN: Unsupervised Deep Network for Depth and Ego-Motion Estimation","version":3},"cited_work":{"arxiv_id":"1809.00969","doi":null,"metadata_source":"pith","pith_arxiv_id":"1809.00969","snapshot_observed_at":"2026-08-14T11:37:59.932065Z","title":"A Deeper Insight into the UnDEMoN: Unsupervised Deep Network for Depth and Ego-Motion Estimation","venue":"cs.CV","work_id":"4e1d7a67-154e-43bf-b779-0005d484bbd4","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.527421Z"},"links":{"cited_paper":"/paper/1809.00969","citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:33fe638b13bfe74780ad5a54791b141e08858aaed5e65950e82219c8d3b01439","observation_id":"3d9f0f87-0af3-468e-92ed-eb4be101d6e6","resolution":{"observed_at":"2026-08-14T11:37:59.945525Z","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":"2026-08-14T11:38:01.326049Z","title":"UnDEMoN: Unsupervised Deep Network for Depth and Ego-Motion Estimation","venue":null,"work_id":"2f542d7b-f93f-4a6f-b9ef-84062c5ca322","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.532679Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:bf91011ca8cb35d32f5b3514d72291928052eca1d6280914fff5451e1cae6734","observation_id":"12ca8fef-bbe3-44b6-b1c5-c9d6a325a048","resolution":{"observed_at":"2026-08-14T11:38:01.340922Z","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":"2026-08-14T11:38:01.292617Z","title":"Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments","venue":null,"work_id":"d68d7b89-e078-43de-bb9d-7a39f190f691","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.539779Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:f8a1bc8ab571c274085ba6cb8c7aa12c5cff602f7593fa94f626be771d02c707","observation_id":"9ce19dc2-3f08-4400-bdea-1ea1e750ec23","resolution":{"observed_at":"2026-08-14T11:38:01.301849Z","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":"2026-08-14T11:38:01.259835Z","title":"CodeSLAM: Learn- ing a Compact, Optimisable Representation for Dense Visual SLAM","venue":null,"work_id":"75ca6bfb-1fa1-4d1a-a263-ea337a8cf8dd","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.545328Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:0a948902f9cb1d4ee3bd4f63e2020be6dcc9370e4b74163ed8000da385a3f18f","observation_id":"a83c7926-1b7a-4b5d-b5d2-17509b5a032b","resolution":{"observed_at":"2026-08-14T11:38:01.269202Z","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":"2026-08-14T11:38:01.233100Z","title":"DeepDriving: Learning Affordance for Direct Percep- tion in Autonomous Driving","venue":null,"work_id":"155482c7-3717-4421-b9bf-28490eae8709","year":2015},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.553182Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:6f067b0fbeefb9d6ba2d3c5a4b86d49e9a259613a4efb5809be2a2ad4bb8084c","observation_id":"aa864835-c613-468f-b8d7-4ce0b30f84d0","resolution":{"observed_at":"2026-08-14T11:38:01.238266Z","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":"2026-08-14T11:37:59.561336Z","title":"The Cityscapes Dataset for Semantic Urban Scene Understanding","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.561336Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:292578d4f23aa4a8c8cdebe745c9eed516c5d0fc59555a3ed4c16b6b23e64270","observation_id":"45ba4334-6596-4441-810b-421348eee3c3","resolution":{"observed_at":"2026-08-14T11:37:59.561336Z","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":"2026-08-14T11:38:01.192446Z","title":"FlowNet: Learning Optical Flow with Convolutional Networks","venue":null,"work_id":"be66cc47-768b-45b1-a4c3-1e9a28b10a1f","year":2015},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.568844Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:4db5656b8d6182b0c022c66f2cf5504e6e8f8ef37767cc30ab23aee9480e92f6","observation_id":"63977407-f0e9-4c4d-a8a9-bbe9ce9808b6","resolution":{"observed_at":"2026-08-14T11:38:01.199989Z","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":"2026-08-14T11:38:01.167784Z","title":"Depth Map Prediction from a Single Image Using a Multi-Scale Deep Network","venue":null,"work_id":"801218d7-a4cc-4957-99d1-a7036f4cbfbd","year":2014},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.580808Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:4084c1ea759cc66090e8018fa22b5fcd657b7d35f4e2444f23a6596a07791e2a","observation_id":"96716256-4d8f-468f-ac34-7fbc34c832dc","resolution":{"observed_at":"2026-08-14T11:38:01.176083Z","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":"2026-08-14T11:38:01.128555Z","title":"Direct Sparse Odometry","venue":null,"work_id":"8d1eacf7-d31b-41c8-b85a-e25d8e056304","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.598553Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:c97355581342eff60d5641fe5dbc30e86c0c9feecebbdb9cf231c065bc667faf","observation_id":"4622e19d-a110-4b7d-8dbb-24fcf0070c65","resolution":{"observed_at":"2026-08-14T11:38:01.143937Z","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":"2026-08-14T11:38:01.091956Z","title":"LSD- SLAM: Large-Scale Direct Monocular SLAM","venue":null,"work_id":"a0d1b8ff-406f-46e1-badf-3374337aaba1","year":2014},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.608793Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:3081a4d942d66a9e280bf787f310c6c3e7e707fd2805157efd0001a632155acb","observation_id":"f1873912-cf72-477d-9088-29c126fa51d2","resolution":{"observed_at":"2026-08-14T11:38:01.109922Z","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":"2026-08-14T11:38:01.064893Z","title":"Two-Frame Motion Estimation Based on Polynomial Expansion","venue":null,"work_id":"b3906cc3-1fbb-4314-b783-51842fb1eec1","year":2003},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.616664Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:190b0ef038b6e58a0b0c7742c83b6b6db4336780362da62c799ca00a8498e482","observation_id":"bfc7a4b9-8283-4e4c-bdd8-cc9e1037fe0e","resolution":{"observed_at":"2026-08-14T11:38:01.074057Z","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":"2026-08-14T11:38:01.014849Z","title":"Collaborative Monocular SLAM with Multiple Micro Aerial Vehicles","venue":null,"work_id":"70aeee1f-403b-4a1c-bd3a-45aac4102a59","year":2013},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.625829Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:098671396a4f23082c599325d2102ee3a93281aa40612033442df9048d00a11b","observation_id":"6be5f8dd-ef06-421f-9558-2078dfa379dd","resolution":{"observed_at":"2026-08-14T11:38:01.024106Z","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":"2026-08-14T11:38:00.992519Z","title":"SVO: Fast Semi-Direct Monocular Visual Odometry","venue":null,"work_id":"b5ce41b0-86b9-4404-8337-508768884ac6","year":2014},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.632607Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:4336451e2218235da5604868b9eefe235a1bed1c882410086e25d4ffb28624c1","observation_id":"eef2d020-a1fb-424b-bed7-686f593ea45d","resolution":{"observed_at":"2026-08-14T11:38:01.001049Z","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":"2026-08-14T11:38:00.964208Z","title":"Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue","venue":null,"work_id":"719b7a27-2994-4cdb-92be-4146246d0983","year":2016},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.639555Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:ab16e592976bc0c5c63193e9f6618f840bcf806688921001793df133015b16fb","observation_id":"eef68e31-4807-41f5-ae54-280f313412da","resolution":{"observed_at":"2026-08-14T11:38:00.971733Z","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":"2026-08-14T11:38:00.902410Z","title":"Unsupervised Monocular Depth Estimation with Left-Right Consistency","venue":null,"work_id":"745c05c9-71d9-4f12-a6b4-23fff929c348","year":2017},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.653132Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:13af4f4dfc1c6426a99568258a6f62f0023c7df1400c65219226ba4892ff805a","observation_id":"91caa15d-772a-4239-ba71-d36b8afccecc","resolution":{"observed_at":"2026-08-14T11:38:00.910680Z","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":"2026-08-14T11:38:00.878268Z","title":"Generative Adversarial Nets","venue":null,"work_id":"86c34aae-09fd-4bb2-9de5-d5f1aca9939e","year":2014},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.664725Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:f19065b97a0c0f33501d4ca11e59393044cc0190f4bd363795ed36a7272fb5d9","observation_id":"cabe7e8a-9568-4855-8682-982951742539","resolution":{"observed_at":"2026-08-14T11:38:00.885323Z","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":"2026-08-14T11:38:00.851282Z","title":"MapNet: An Allocen- tric Spatial Memory for Mapping Environments","venue":null,"work_id":"a02f2fd7-ddda-491c-8664-5aae3c370510","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.670462Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:af67be79cebc06b9b978d33e5c326daef0866d2fee926ddad5d2a0c83fc360d3","observation_id":"678a5da1-f7d5-43cc-a82a-98be86c97d96","resolution":{"observed_at":"2026-08-14T11:38:00.859154Z","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":"2026-08-14T11:38:00.826062Z","title":"Long Short-Term Memory","venue":null,"work_id":"0b43a5d5-30a3-478c-b536-23120bcd3912","year":1997},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.676589Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:42313abd47e707989c064760d33d34ae8cb519ebf149ef619b6b1de90ca6fe6d","observation_id":"8df7ad6b-4b50-48b7-9e11-48f31a0d4337","resolution":{"observed_at":"2026-08-14T11:38:00.835493Z","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":"2026-08-14T11:38:00.782463Z","title":"Image-to-Image Translation with Conditional Adversarial Networks","venue":null,"work_id":"b2eaa4db-5e60-4d6c-8d1e-682e023980cd","year":2017},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.682463Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:e4b41c21626bc64fc53a1a6b81d77ab5c210af336177000fe3499275fc2979f3","observation_id":"ced3e66c-07b2-4fc3-9d8d-9545bbd605e6","resolution":{"observed_at":"2026-08-14T11:38:00.802497Z","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":"2026-08-14T11:38:00.757401Z","title":"Geometric Consistency for Self- Supervised End-to-End Visual Odometry","venue":null,"work_id":"07139e28-1070-4010-9dfc-948df8ca8a6e","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.687996Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:36d88ea8e0cff0d6676d7e0ed7734b1bc0908567a8d3056ed51470966a854806","observation_id":"6c3ad2b2-7731-4e45-b808-235afa85066b","resolution":{"observed_at":"2026-08-14T11:38:00.765798Z","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":"2026-08-14T11:38:00.722591Z","title":"Dense visual SLAM for RGB-D cameras","venue":null,"work_id":"92328791-43c0-4631-a0fc-e11ae35822a1","year":2014},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.692509Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:6085e3a6d0f296dce6173a166f22b92d5742ba28e2626191aeff776794c37f0f","observation_id":"f3a3dbb6-707e-42fa-8348-e3ebb41f571d","resolution":{"observed_at":"2026-08-14T11:38:00.740852Z","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":"2026-08-14T11:38:00.676352Z","title":"Adam: A method for Stochastic Optimization","venue":null,"work_id":"7a9c138f-acd9-4597-b78c-1547eabeb8c9","year":2015},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.697473Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:a56c53588f52e39c45c2eb182bd98534c58ac81f89ba20b8fdb202962d17afe6","observation_id":"b8b825d7-f3dd-446f-b363-42c1dbb60190","resolution":{"observed_at":"2026-08-14T11:38:00.684484Z","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":"2026-08-14T11:38:00.648866Z","title":"Parallel Tracking and Map- ping on a Camera Phone","venue":null,"work_id":"a2cd38fc-b728-4bab-b686-e474cc74f1b3","year":2009},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.703019Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:ba3827213764abb9bb7766659f1b3db6d757f85f2163454397d418b67fd7bfb2","observation_id":"ee2b456b-cc37-4453-b861-76e35a1fed16","resolution":{"observed_at":"2026-08-14T11:38:00.656717Z","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":"2026-08-14T11:38:00.629030Z","title":"UndeepVO: Monocular Visual Odometry through Unsuper- vised Deep Learning","venue":null,"work_id":"c83d80e1-1835-4c21-babd-2150d3e4a372","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.708607Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:e9b1bbe941158f7b1870510c9ac3326ae9d7f4c2d0b3cbb252844e99d7c0d3b2","observation_id":"5b1b77f2-e62f-4172-84fc-b970cb99440f","resolution":{"observed_at":"2026-08-14T11:38:00.634690Z","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":"2026-08-14T11:38:00.601597Z","title":"Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields","venue":null,"work_id":"9b72fe03-8a7d-431b-a632-329a8d1a7762","year":2024},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.714677Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:386f1938c4623a39af98e97507b5bbdbaa48ac034d109821285e56f9d2c5177f","observation_id":"047e0a90-49af-4bee-a137-ea508030c86e","resolution":{"observed_at":"2026-08-14T11:38:00.612340Z","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":"2026-08-14T11:38:00.559412Z","title":"Un- supervised Learning of Depth and Ego-Motion from Monoc- ular Video Using 3D Geometric Constraints","venue":null,"work_id":"381e89da-3d76-46fe-9903-ef6f2116870d","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.721380Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:62acdadf583f8fbbec669fa77eaf34e41eed7abda0b840b97e9cfa5b6430764e","observation_id":"4cc811a9-c4ae-4eb1-9620-815e169dd472","resolution":{"observed_at":"2026-08-14T11:38:00.584080Z","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":"2026-08-14T11:38:00.530841Z","title":"ORB-SLAM: A Versatile and Accurate Monoc- ular SLAM System","venue":null,"work_id":"0a72d9aa-95a2-4682-b3cd-3500eb9bb848","year":2015},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.734794Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:608e15955801a3195e9d9773a13ab3e6ab8eba2cbfee3bd515af0064a8e57327","observation_id":"72c2f674-cf2b-470a-8985-bb302a2c2e28","resolution":{"observed_at":"2026-08-14T11:38:00.541863Z","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":"2026-08-14T11:38:00.509017Z","title":"KinectFusion: Real-Time Dense Surface Mapping and Tracking","venue":null,"work_id":"fbb1d1fa-6d33-4d13-9da9-c4a053820e03","year":2011},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.748952Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:233735fe3ee850640a00ffabc52986e7659dac0a5e298cbffba9bb11164e6b60","observation_id":"91c64341-1261-49e2-b686-936cb723ff21","resolution":{"observed_at":"2026-08-14T11:38:00.513698Z","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":"2026-08-14T11:38:00.478977Z","title":"Colored Point Cloud Registration Revisited","venue":null,"work_id":"4b83fe1b-75a8-493f-a2fe-4572f5017f87","year":2017},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.757860Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:83e1da1554d48461ae6b42a9219a7be28532b3be54d5a29345a555c885acf1f9","observation_id":"36dd664c-d477-43b1-8459-3759caacd332","resolution":{"observed_at":"2026-08-14T11:38:00.483996Z","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":"2026-08-14T11:38:00.449276Z","title":null,"venue":null,"work_id":"f0e23ab0-0329-45aa-86b2-bcf06ae1f36b","year":2017},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.767580Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:a217aec8015e45faeae0cd75883f99e37ca80ab52a1ad919dffb71d383d15ddc","observation_id":"5492910d-8a55-476c-9a82-f5f1438eb211","resolution":{"observed_at":"2026-08-14T11:38:00.459960Z","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":"2026-08-14T11:38:00.411595Z","title":"DeMoN: Depth and Motion Network for Learning Monocular Stereo","venue":null,"work_id":"8d80fc91-cf17-4a31-b373-9efdd246c629","year":2017},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.772694Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:5e0a93551a4afa0a5050a3a23cf421bfdc13b2fabc975a021db9f46823a284da","observation_id":"2a424f9d-6f85-453f-ac45-20e98195de25","resolution":{"observed_at":"2026-08-14T11:38:00.427189Z","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":"2026-08-14T11:38:00.384074Z","title":"DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks","venue":null,"work_id":"9b76bed5-b09c-43d3-848e-8308e104a2ac","year":2017},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.778988Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:ba483a5bb0cc19439cb59b9907969ee6bb3370fe40650aabcd39e94669dc2581","observation_id":"9b63f619-a070-4e75-b4ec-18de0d872fe7","resolution":{"observed_at":"2026-08-14T11:38:00.392139Z","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":"2026-08-14T11:38:00.357804Z","title":"Image Quality Assessment: from Error Visibility to Structural Similarity","venue":null,"work_id":"f2fff615-e036-4457-a55b-c7dfaee2888f","year":2004},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.784182Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:e6a3ad488991082db6f5642338338e5adc6bc9294426eab1c165ea08faa4aa6d","observation_id":"a5960a96-8916-4ce6-8008-6a826d4ee351","resolution":{"observed_at":"2026-08-14T11:38:00.366076Z","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":"2026-08-14T11:38:00.339418Z","title":"Guided Feature Selection for Deep Visual Odometry","venue":null,"work_id":"c7358cf0-570b-4310-bbae-e2e297bd593e","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.790623Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:3166662dfd483a10fec278c51cca733ac1240220f988dd1fd3fb67cb9979a050","observation_id":"ecec0813-0b3f-432c-8568-cf1749a4b9da","resolution":{"observed_at":"2026-08-14T11:38:00.346930Z","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":"2026-08-14T11:38:00.286486Z","title":"Beyond Tracking: Selecting Mem- ory and Refining Poses for Deep Visual Odometry","venue":null,"work_id":"2a7c0937-3fec-47ab-b8c6-18a9a2f3d0be","year":2019},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.796560Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:b8b79daaa8873acd335b5ab6e33d3f8002bdb31d7bb83bc9eb4fcd21f6d4b93f","observation_id":"e2e9c145-743e-40a0-b36f-a96554d4ee9d","resolution":{"observed_at":"2026-08-14T11:38:00.313354Z","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":"2026-08-14T11:38:00.243102Z","title":"Deep Virtual Stereo Odometry: Leveraging Deep Depth Pre- diction for Monocular Direct Sparse Odometry","venue":null,"work_id":"d80498d0-ab4e-4039-bca8-ea356a44b7c4","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.802729Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:ba6761a20ec5f30ff3be325aa7f449c360a7752bb023282a9f196db16d7c8417","observation_id":"349babe0-489b-4f2a-a1d5-6a7a707a817d","resolution":{"observed_at":"2026-08-14T11:38:00.257340Z","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":"2026-08-14T11:38:00.185248Z","title":"GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose","venue":null,"work_id":"e746e261-5e8f-4a17-a6b9-835f98a317a9","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.812595Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:b69413c978e23f2920df27bb84efb70eb2b3b3c92b52f9174352f257e6c6757d","observation_id":"1dfe9bba-4a68-4c89-a9ea-f05de79d44c5","resolution":{"observed_at":"2026-08-14T11:38:00.225831Z","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":"2026-08-14T11:38:00.139541Z","title":"Unsupervised Learn- ing of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction","venue":null,"work_id":"75943cb7-27e6-4d06-9790-7ff82ee3c171","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.818828Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:d8325d05b3729a358f911ec7422c4ed85736ff52b00a1550dc7b7f6ff6e4e1e3","observation_id":"5574b6a8-40f2-4607-9f33-06760713c584","resolution":{"observed_at":"2026-08-14T11:38:00.154287Z","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":"2026-08-14T11:38:00.099589Z","title":"DeepTAM: Deep Tracking and Mapping","venue":null,"work_id":"5493d1f1-6f52-49ec-92ec-38a85ba2d49d","year":2018},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.825822Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:5cd173db07987823073832bba2d200bf3b4c018193cc3793729169b440c8ba28","observation_id":"92a7e70f-5227-4ea2-87a2-cb7bda1c0a62","resolution":{"observed_at":"2026-08-14T11:38:00.106918Z","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":"2026-08-14T11:38:00.036937Z","title":"Unsupervised Learning of Depth and Ego-Motion from Video","venue":null,"work_id":"c8064ab1-38ff-41da-ba99-f38e74b6086b","year":2017},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.835041Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:f8cee6568d5ee4889ac4797df5f0b3d28e9ad5f50f2f6c4195372eff7f64f48b","observation_id":"4a53cda1-2351-46c4-8c92-22561734182c","resolution":{"observed_at":"2026-08-14T11:38:00.069057Z","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":"2026-08-14T11:37:59.996301Z","title":null,"venue":null,"work_id":"a95343be-94b5-45d1-ae4a-511c1af131cb","year":2017},"citing_paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T11:37:59.843491Z"},"links":{"citing_paper":"/paper/1908.08704"},"observation_digest":"sha256:8b46fb05fde0aa82cee7a3bfbceeeea31a8f4f7fac3e9094a09660cabbd1460c","observation_id":"a1d84fe1-7bf7-46ac-8c1d-da1c2ca041e7","resolution":{"observed_at":"2026-08-14T11:38:00.015897Z","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"}}],"paper":{"arxiv_id":"1908.08704","last_updated":"2019-08-23T07:53:35Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T11:29:20.986145Z","submitted_at":"2019-08-23T07:53:35Z","title":"Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":1,"verified_fuzzy":39},"total_outbound_references":43},"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 17 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:1908.08704."}