{"as_of":"2026-08-13T18:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2255f89ad1cc16bed7323f6bf13505096a5824a2537570012c2c7d3de088cf9f","coverage":[{"denominator":27,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T18:16:22.940791Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T10:52:48.648480Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-12T12:16:16.734455Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"cited_work":{"arxiv_id":"2412.08120","doi":"10.48550/arxiv.2412.08120","metadata_source":"pith","pith_arxiv_id":"2412.08120","snapshot_observed_at":"2026-08-12T12:16:16.734455Z","title":"Dense Depth from Event Focal Stack","venue":"cs.CV","work_id":"ed77b826-098f-4214-af13-0ca87a151d60","year":2024},"citing_paper":{"arxiv_id":"2608.11075","last_updated":"2026-08-11T15:37:49Z","snapshot_observed_at":"2026-08-13T17:30:55.840752Z","submitted_at":"2026-08-11T15:37:49Z","title":"Static in Frames, Dynamic in Events: Rethinking Features in Event Cameras as Motion Cues","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-12T10:52:48.648480Z"},"links":{"cited_paper":"/paper/2412.08120","citing_paper":"/paper/2608.11075"},"observation_digest":"sha256:179d3a51a6854c1fba45eeef7cee9309b54ba88c5c80176e87643069495a66f8","observation_id":"dddc2610-0fa2-4680-9af1-57275d177426","resolution":{"observed_at":"2026-08-12T10:53:36.813684Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.08120/citation-record","integrity":"/paper/2412.08120/integrity","json":"/paper/2412.08120/citation-record.json","paper":"/paper/2412.08120"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:16:23.317492Z","title":"End-to-end learning of geometry and context for deep stereo regression","venue":null,"work_id":"a8a7e8e7-024d-4f04-ad27-2f3ab69d7bc7","year":2017},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.839693Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:9d74ae172916188fdaf504c2cec879e920d1935e7254da4934057cde3498faa5","observation_id":"bf811cc9-3949-4254-8e4c-008129f342fd","resolution":{"observed_at":"2026-08-11T18:16:23.321339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.304499Z","title":"A new sense for depth","venue":null,"work_id":"15550073-264b-4f4e-8b30-9a392488ce07","year":1987},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.843823Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:c39071961607407d02e4a57ad830210a1d78c0eaf04b3359084d3eacccf7a116","observation_id":"e6966bd1-fffd-4b99-8039-30a3cd889a24","resolution":{"observed_at":"2026-08-11T18:16:23.308712Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.292266Z","title":"The Blender project - free and open 3d creation software, Accesed: 2023","venue":null,"work_id":"bccc6d2e-19ae-4b53-bd5e-b895ff4ca099","year":2023},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.847837Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:6680600d08b04b90561e37e2b3aafd692f05e6c9a0c5d83443d006084afb0072","observation_id":"7aa92295-a018-4c99-bf97-91811b0fe8ed","resolution":{"observed_at":"2026-08-11T18:16:23.297390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.06944","last_updated":"2024-05-11T07:54:49Z","snapshot_observed_at":"2026-08-13T00:09:49.533379Z","submitted_at":"2024-05-11T07:54:49Z","title":"Learning Monocular Depth from Focus with Event Focal Stack","version":1},"cited_work":{"arxiv_id":"2405.06944","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.06944","snapshot_observed_at":"2026-08-11T18:16:22.972492Z","title":"Learning Monocular Depth from Focus with Event Focal Stack","venue":"cs.CV","work_id":"0d2cab95-a13b-4fed-9e0f-94f444802349","year":2024},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.851497Z"},"links":{"cited_paper":"/paper/2405.06944","citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:a154ca611d4e879e9d6e3e65d6577064e2184af1182f9e9f735332e4f5522b2c","observation_id":"412688d0-2413-4f1a-b1cc-d4667c75cbe5","resolution":{"observed_at":"2026-08-11T18:16:22.979265Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.280473Z","title":"Eigen and R","venue":null,"work_id":"1931d68b-d26f-42a9-b30e-7c5d5fbfef17","year":2015},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.855878Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:9218a3829823365378d5a2d28b6f571bcdd2d43aadd765a117909b627609f3ae","observation_id":"6ea69b47-c434-4635-9365-245c39463bf9","resolution":{"observed_at":"2026-08-11T18:16:23.284120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.268173Z","title":"Kingma and Jimmy Ba","venue":null,"work_id":"656064be-19a9-494b-b817-0f6bd2e36a25","year":2014},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.859764Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:fa043b1b18bae0d63b5a33dbdbcb5ed72b44c1cf742a1c89c4d7ed0dfbcaeb88","observation_id":"db99df53-8648-4d4f-8214-cb8eccaa47ec","resolution":{"observed_at":"2026-08-11T18:16:23.271918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.254437Z","title":"Depthlab: Real-time 3d interaction with depth maps for mobile augmented reality","venue":null,"work_id":"ccbb36d4-ac9b-4bc0-9ad9-487d692a6d50","year":2020},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.863713Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:fa7706523fcd9de0a968a75c02420cf12654cd1a4a817e6183e3eb745eba405e","observation_id":"69100c85-8639-4e79-a6b5-0e98db7312ca","resolution":{"observed_at":"2026-08-11T18:16:23.259348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.243385Z","title":"Deep depth from focus with differential focus volume","venue":null,"work_id":"8f6e2756-b543-4da8-9d6f-305c84715698","year":2022},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.867213Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:1ba177ee4a0a473057db7c513577f011630e247b0e4b5ad416c5d7346fdc213e","observation_id":"eefae682-d082-4695-97e2-c0c00a14799b","resolution":{"observed_at":"2026-08-11T18:16:23.247545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.232558Z","title":"Davison, J¨org Conradt, Kostas Dani- ilidis, Davide Scaramuzza","venue":null,"work_id":"19d49dbf-ae94-4331-9481-a3d12fab28ef","year":2019},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.871248Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:6415851e10653cd096f189a46d750a2337009feeea62039a0413fda9d84c97bd","observation_id":"20bba755-0187-4e36-b36f-53a04d909377","resolution":{"observed_at":"2026-08-11T18:16:23.236298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.219020Z","title":"A spiking neural network model of depth from defocus for event-based neuromorphic vision","venue":null,"work_id":"46de1c7b-fdfc-4c2f-8caa-c5ee26f8f4bb","year":2019},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.875611Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:1549d1fc6f17fd18506ea90b753c1255f01bb75a7a59cd8aad9f999441191cb1","observation_id":"da27097a-087e-48de-b60e-e13ef5066ea8","resolution":{"observed_at":"2026-08-11T18:16:23.223490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.205584Z","title":"All- in-focus imaging from event focal stack","venue":null,"work_id":"72ce3724-8241-4a9b-ae8f-6c3a2ccfbdcd","year":2023},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.879653Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:9a311d955eb9a4bf87d254f0d3e8911dcf99c5b022c571a3ba5b4e9eec6aee25","observation_id":"0307d52c-84da-48fa-9847-4f4f4ea66d4e","resolution":{"observed_at":"2026-08-11T18:16:23.210486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.192908Z","title":"High speed and high dynamic range video with an event camera","venue":null,"work_id":"d512679a-b6f0-4bb5-b6a8-fa118e752f5f","year":1964},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.883364Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:6383e6407188533d6b59acfbb12d1b01bd5bbf080ded96f717dd2c1a48bf9deb","observation_id":"d6152d60-53a7-4b92-96f7-52c06bef3a19","resolution":{"observed_at":"2026-08-11T18:16:23.197544Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.179200Z","title":"Co-attention- guided bilinear model for echo-based depth estimation","venue":null,"work_id":"4e5e1379-e612-480e-8dc2-0847243a094b","year":2022},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.887346Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:9d2b837e9d16a8072e67280fbd54d8f9756f9e6ef3961607c02bd9aef440420b","observation_id":"4ae12d30-9457-404a-9d42-01f272064644","resolution":{"observed_at":"2026-08-11T18:16:23.184152Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.164560Z","title":"Multi-layer scene representation from composed focal stacks","venue":null,"work_id":"69d4aa7f-fce6-40b2-9a74-d2d18cdc9810","year":2023},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.890967Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:1eec83bfdd82de4fd33b1220cae6b2a5ad9ebea1e601204a3f5e2a8781c1ae69","observation_id":"82f051ee-93bf-4f74-a368-fcd3c2303ccc","resolution":{"observed_at":"2026-08-11T18:16:23.169136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.151462Z","title":"Learning monocular dense depth from events","venue":null,"work_id":"b64edc95-4104-41d1-a997-0e83213009ba","year":2020},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.894732Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:671d9cc881490e5cfd4a8f527103d81d5488a556d479749c71714f32c599da3c","observation_id":"4f512175-b6f9-459a-8148-3d2b81355b22","resolution":{"observed_at":"2026-08-11T18:16:23.156335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.139326Z","title":"Fo- cus on defocus: Bridging the synthetic to real domain gap for depth estimation","venue":null,"work_id":"2c94d1a4-2f49-41f4-9abf-9a62651d7ad2","year":2020},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.898079Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:0a8f4510730fa149992f8fa94aec514a3016092baa45132e73c5b2c4f4f7e6a4","observation_id":"ac317b00-d45d-4a6b-9b1b-facb31075e22","resolution":{"observed_at":"2026-08-11T18:16:23.143556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.126981Z","title":"M4depth: Monocular depth estimation for autonomous ve- hicles in unseen environments","venue":null,"work_id":"601f64ef-1a29-4170-9434-48b426399f14","year":2022},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.901393Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:7c7991b8e9f393e5604269121c252eaff9836e9551d24a37be5db490a1f6ca5c","observation_id":"7f2b44f0-9857-482d-bb7b-6a7595ccc2cb","resolution":{"observed_at":"2026-08-11T18:16:23.131437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.113698Z","title":"U- net: Convolutional networks for biomedical image segmen- tation","venue":null,"work_id":"00e04e6b-ffd2-4967-8496-735caa1f8176","year":null},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.905328Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:3b1e938ab4d4bb95c6a2f2463c1f89a621590411ca46f87acbbc9d5ade9df208","observation_id":"a8a8f488-0e06-4528-9f74-1d3315eec9ac","resolution":{"observed_at":"2026-08-11T18:16:23.118077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.098958Z","title":"Zhou and A","venue":null,"work_id":"1c44170c-2d89-4aca-aaa2-027f2d83d7f4","year":2016},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.909452Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:9b6e428de1aa9fbd8f58f95998d24a7fb2527269b843ede6670ee935877a509b","observation_id":"e46912a3-ef1d-45f7-bf37-6444a0eae841","resolution":{"observed_at":"2026-08-11T18:16:23.104661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.087362Z","title":"ESIM: an open event camera simulator","venue":null,"work_id":"9dc1dad2-d6e7-4e6d-8314-193859053605","year":2018},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.913890Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:8110f62bb2c2c5b0878eb7fb361d04f991b9278f73978accbe1a74a0a3fefd96","observation_id":"a431e999-ca26-435e-aa7e-5109f0cfad02","resolution":{"observed_at":"2026-08-11T18:16:23.091298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.072550Z","title":"Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer","venue":null,"work_id":"672347a8-c201-4839-8f71-a9444200325a","year":2022},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.917750Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:4b1def79e3c83276786ca8154bd3947b3b8de2278ace49b248b89f464ae5fd63","observation_id":"b8f74b43-4e00-44fd-ba63-4dee56f65976","resolution":{"observed_at":"2026-08-11T18:16:23.076998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.056633Z","title":"Dvs- voltmeter: Stochastic process-based event simulator for dy- namic vision sensors","venue":null,"work_id":"afd832f9-2aba-4921-b60a-036131fce611","year":2022},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.921972Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:3627152bfe2b60f7b32caf572046c6e84161b839b6bed31aff3cc3516e74ca32","observation_id":"d30de78a-8d85-49e9-84bd-e0039731e9f0","resolution":{"observed_at":"2026-08-11T18:16:23.063328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.041054Z","title":"Computer Vision: Algorithms and Appli- cations","venue":null,"work_id":"fd44a122-f07b-4d7b-94ea-f76356f8386d","year":2022},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.925551Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:b6c04df975eb2b37a7eaac1d198a88efbe8b2a5f2606b681091dfeab51dcb458","observation_id":"ea3d7277-4ef0-4fb9-83ae-6a9ba5bb12a5","resolution":{"observed_at":"2026-08-11T18:16:23.045867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.027205Z","title":"Event-based monocular dense depth estimation with recur- rent transformers","venue":null,"work_id":"307738c0-9cbf-45e8-8db0-cf0897681dad","year":2022},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.929824Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:d5bc7ecfb674c7bd783997c3ad4cdc911c8994bd6788180396b21528fbead720","observation_id":"5760078d-b786-417e-b663-3c10223ba8fc","resolution":{"observed_at":"2026-08-11T18:16:23.032535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.014459Z","title":"Depth anything: Unleashing the power of large-scale unlabeled data","venue":null,"work_id":"7802ef7d-1f6e-4239-a95b-5b238619a8ab","year":2024},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.933516Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:b60b5d1fd5bfdfdb90344e16ed6c34147f1bb93aa07f0f692f8c9d614da7f0e4","observation_id":"0f40eb47-87a1-4207-be1d-cce97c74f295","resolution":{"observed_at":"2026-08-11T18:16:23.019009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:23.000197Z","title":"Motion deblurring and depth estimation from multiple images","venue":null,"work_id":"df547e07-e64f-4408-8e08-4b5a51ed1973","year":2016},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.937007Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:3b790edfd29b31e0c6b40818f96b339dc6acfeef135a888734c402ee727fa8f4","observation_id":"dd1e043e-7d5f-476f-b978-741b0b3b70bf","resolution":{"observed_at":"2026-08-11T18:16:23.005075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:16:22.988581Z","title":"Unsupervised event-based optical flow using mo- tion compensation","venue":null,"work_id":"f13a2f72-d126-4034-9986-b8cba4428969","year":2018},"citing_paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T18:16:22.940791Z"},"links":{"citing_paper":"/paper/2412.08120"},"observation_digest":"sha256:d020353a2bdc87dc95f6ca96981539c0e95ad3c57f9915e70a7e06f7db1b4076","observation_id":"85a8769e-394e-4737-bad2-22b224ce2b9f","resolution":{"observed_at":"2026-08-11T18:16:22.992927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.08120","last_updated":"2024-12-11T06:13:38Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T18:09:34.173367Z","submitted_at":"2024-12-11T06:13:38Z","title":"Dense Depth from Event Focal Stack"},"reference_resolution":{"displayed":27,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":1,"verified_fuzzy":26},"total_outbound_references":27},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2412.08120."}