{"as_of":"2026-08-07T21:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4cfe296b278f2bc1fbfa69e5cdb66d1ee7b14438577e754b649146f77ad84c7b","coverage":[{"denominator":76,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":76,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:27:09.805799Z","state":"measured"},{"denominator":76,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":76,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2506.05473/citation-record","integrity":"/paper/2506.05473/integrity","json":"/paper/2506.05473/citation-record.json","paper":"/paper/2506.05473"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:27:10.481753Z","title":"nuscenes: A multi- modal dataset for autonomous driving","venue":null,"work_id":"8d8372af-5a9e-4312-85f1-12adbf932453","year":2020},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.487995Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:acb56c6fd12807db8d0b80599fde15901800c6bc68464f0d36f664647216a041","observation_id":"ec715d49-3300-48f5-a407-eddbbef6c3d4","resolution":{"observed_at":"2026-08-07T10:27:10.484701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.473710Z","title":"Monoscene: Monoc- ular 3d semantic scene completion","venue":null,"work_id":"0e43d786-dee3-450f-b244-b44bf146871d","year":2022},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.491620Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:11803223bb30b4d586e28d7995011b6d952f994c19a17afe6021f28c52e80323","observation_id":"dfed3133-d0ce-47de-a937-b780449ec365","resolution":{"observed_at":"2026-08-07T10:27:10.476484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.464455Z","title":"Monoscene: Monoc- ular 3d semantic scene completion","venue":null,"work_id":"8e3b0054-ce1b-40c5-9470-5a7d040a8446","year":2022},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.494660Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:a84972b65cb07561672b6429170ffb9753f410cab4473e7380a5785417c0201e","observation_id":"ac198b7c-97f6-4681-b79b-d34d1f8de786","resolution":{"observed_at":"2026-08-07T10:27:10.467622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.455786Z","title":"End-to- end object detection with transformers","venue":null,"work_id":"36f09b26-9d0d-4897-872c-a8b1d6f2b178","year":2020},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.497533Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:c1160995470cb10a807ae8f585ba78dc36043d666e5ae1926cb7f91d2f035799","observation_id":"a695b184-2389-4f28-b866-6ba31893d83f","resolution":{"observed_at":"2026-08-07T10:27:10.458605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.447370Z","title":"pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction","venue":null,"work_id":"78367dc6-094c-44ec-adae-b10011dbcbf7","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.599090Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:1d259a7a451572b03b3137b09a7f45aef6220765ba403a8348c83acfbfe2f573","observation_id":"6ad09cd5-0600-4a22-95a4-9a6f4f38c708","resolution":{"observed_at":"2026-08-07T10:27:10.450108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.439216Z","title":"Maptracker: Tracking with strided memory fusion for consistent vector hd mapping","venue":null,"work_id":"1cc2aab0-16e8-41db-9739-d1444e5061c7","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.602480Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:0bffa9f8fd5cad87ce64f370ec0c194183d75d7f432bd453876e787c24b57ad8","observation_id":"445246a3-384f-4ece-88d7-b2fdf20d459a","resolution":{"observed_at":"2026-08-07T10:27:10.442029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.14135","last_updated":"2022-06-23T17:53:32Z","snapshot_observed_at":"2026-07-06T13:14:48.753329Z","submitted_at":"2022-05-27T17:53:09Z","title":"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.14135","snapshot_observed_at":"2026-08-07T10:27:09.605655Z","title":"Flashattention: Fast and memory- efficient exact attention with io-awareness.arXiv preprint arXiv:2205.14135, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.605655Z"},"links":{"cited_paper":"/paper/2205.14135","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:0e408eb68623c813853a6122d5793b70cf12ed70d2bea23a2a1037b6561cf01a","observation_id":"dc740e94-982c-454d-9613-f13739488f4b","resolution":{"observed_at":"2026-08-07T10:27:09.605655Z","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":"2026-08-07T10:27:10.430296Z","title":"A comprehensive framework for 3d occupancy estimation in autonomous driving.IEEE Transactions on Intelligent Vehi- cles, 2024","venue":null,"work_id":"c1dbd425-10e9-43ea-b323-456a76f97f35","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.608976Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:978670de420bb33c877c90e5580e662fc0ec407689061c1110588443ba70f6f5","observation_id":"7d01e829-9a1e-45bc-ae3b-2674c994bb59","resolution":{"observed_at":"2026-08-07T10:27:10.433237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.422002Z","title":"Are we ready for autonomous driving? the kitti vision benchmark suite","venue":null,"work_id":"53686465-a7a0-4883-948b-efc8f2e9ec86","year":2012},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.611878Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:2b1857f94e46e65fd715f9801f15dc53078b0a38b1b1aa1fa56dfe03319cfaeb","observation_id":"894e5836-6c54-4d1c-b1ff-486a5fed1a3a","resolution":{"observed_at":"2026-08-07T10:27:10.424779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:09.614813Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.614813Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:e3846fa2d1c225e14b88b807dc0999e83b236832a0e9bf16a703ee03d6e5c019","observation_id":"38ad241b-afb8-4224-ba21-ba91525b2bbe","resolution":{"observed_at":"2026-08-07T10:27:09.614813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.17054","last_updated":"2022-06-16T09:44:08Z","snapshot_observed_at":"2026-07-06T12:55:16.868412Z","submitted_at":"2022-03-31T14:21:19Z","title":"BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.17054","snapshot_observed_at":"2026-08-07T10:27:09.618142Z","title":"Bevdet4d: Exploit tempo- ral cues in multi-camera 3d object detection.arXiv preprint arXiv:/2203.17054, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.618142Z"},"links":{"cited_paper":"/paper/2203.17054","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:c059bdeab378c4e634a90f16cbfe51a9651df3f6ab49760a02b1bc746cc4d5a5","observation_id":"b7976c3c-add3-4e97-928a-0d782da86e59","resolution":{"observed_at":"2026-08-07T10:27:09.618142Z","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":"2026-08-07T10:27:10.407752Z","title":"Bevdet4d: Exploit temporal cues in multi-camera 3d object detection, 2022","venue":null,"work_id":"aa70ebd0-f2f3-406d-981f-72dbfe963a67","year":2022},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.621239Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:334426a019db154b47fc394adfc523b6b64707f9b7942d37e01a57ad59b3c645","observation_id":"57e1e364-0652-4343-95d0-8a7966db0da6","resolution":{"observed_at":"2026-08-07T10:27:10.410639Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.399402Z","title":"Tri-perspective view for vision- based 3d semantic occupancy prediction","venue":null,"work_id":"2c147893-4eb5-4a49-a606-b487b8a8e37f","year":2023},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.623933Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:ebd95451bd86df94292894f3eba3bc0fa6dd0577b9b54f9b0b05ee19f95f5319","observation_id":"8bbdb542-7db0-4c59-8b3e-79f4d9788ec4","resolution":{"observed_at":"2026-08-07T10:27:10.402201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:09.626667Z","title":"Tri-perspective view for vision-based 3d se- mantic occupancy prediction","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.626667Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:c3189d297bcb1ae7a44d79bd69b1cb92affbe605bfb2352c287d34aff7172b82","observation_id":"6e3968ee-06d6-43e4-ab84-48a002c04052","resolution":{"observed_at":"2026-08-07T10:27:09.626667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.04384","last_updated":"2024-12-06T15:43:40Z","snapshot_observed_at":"2026-07-06T20:02:21.509050Z","submitted_at":"2024-12-05T17:59:58Z","title":"GaussianFormer-2: Probabilistic Gaussian Superposition for Efficient 3D Occupancy Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.04384","snapshot_observed_at":"2026-08-07T10:27:09.629639Z","title":"Prob- abilistic gaussian superposition for efficient 3d occupancy prediction.arXiv preprint arXiv:2412.04384, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.629639Z"},"links":{"cited_paper":"/paper/2412.04384","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:892569e776044099220e9bff09d4c6fae89d51da4aa504338a2f8fd592b03625","observation_id":"c46607a2-0075-4bb9-94c8-b2a0fe43c548","resolution":{"observed_at":"2026-08-07T10:27:09.629639Z","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":"2026-08-07T10:27:10.384085Z","title":"Gaussianformer: Scene as gaussians for vision-based 3d semantic occupancy prediction, 2024","venue":null,"work_id":"13e9e67a-1136-49fa-8ff5-7c34c6606281","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.632839Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:3402c32db863f26e79f8f3f0bfbe60b893ced8fe74aa82d3273f9e91a9fee7f1","observation_id":"0a8c4d85-6343-4b0f-9035-3c1025747a3c","resolution":{"observed_at":"2026-08-07T10:27:10.387283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:09.635455Z","title":"Gaussianformer: Scene as gaussians for vision-based 3d semantic occupancy prediction","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.635455Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:af01117028b75aa20d893f2d1f64b60df734d6e2a45e5aa7a9bca363760c5764","observation_id":"82de688c-5db4-47d9-a60e-fe25e9959a67","resolution":{"observed_at":"2026-08-07T10:27:09.635455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:27:09.638422Z","title":"3d gaussian splatting for real-time radiance field rendering.ACM Trans","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.638422Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:5687e6985e24c94a9355d0da604a76e548814a57462203c798cb2c09579aa7db","observation_id":"3d385177-acdc-43aa-8f05-d83274a1db6f","resolution":{"observed_at":"2026-08-07T10:27:09.638422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.06307","last_updated":"2022-03-18T08:15:56Z","snapshot_observed_at":"2026-08-05T09:30:42.951825Z","submitted_at":"2021-07-13T18:06:46Z","title":"HDMapNet: An Online HD Map Construction and Evaluation Framework","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.06307","snapshot_observed_at":"2026-08-07T10:27:09.641207Z","title":"Hdmapnet: A local semantic map learning and evaluation framework","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.641207Z"},"links":{"cited_paper":"/paper/2107.06307","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:dcb34c2b7eea3aa9f5a96291ef2fe8e89d09a17e9c72db8fda04322d0ed56f45","observation_id":"6c84bc02-6f10-4741-aca9-c4dba1b51837","resolution":{"observed_at":"2026-08-07T10:27:09.641207Z","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":"2026-08-07T10:27:10.363931Z","title":"Bevdepth: Acquisition of reliable depth for multi-view 3d object detec- tion, 2022","venue":null,"work_id":"ec28fe6a-f3d8-4347-a23f-d2666934ce4d","year":2022},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.644498Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:42a093364f760a2ae942ac46a5203b6835238ff9ee621ab6e3dfb069f927621a","observation_id":"403160d7-3a65-4fac-87da-0b7dca146d52","resolution":{"observed_at":"2026-08-07T10:27:10.367024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.355741Z","title":"V oxformer: Sparse voxel transformer for camera- based 3d semantic scene completion","venue":null,"work_id":"7a388c29-34cd-4c9b-a1e6-4209c1893ca3","year":2023},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.647360Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:7c66ccd0913fadb60d770b4179c6d9da8e51f44442c790366b48869101ea367f","observation_id":"8febc59c-8347-41b6-97a6-190dfe387c7e","resolution":{"observed_at":"2026-08-07T10:27:10.358641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.346603Z","title":"Alvarez, Sanja Fidler, Chen Feng, and Anima Anandkumar","venue":null,"work_id":"9a682d33-cb56-46fd-9338-f6cd4c9d6687","year":2023},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.650189Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:7e51ed05747fe9b1d48466b871105e4d1c1407e5bcdc2d056f9e6f3913af3dbc","observation_id":"e8912af6-b17d-411a-be56-d90b32c20898","resolution":{"observed_at":"2026-08-07T10:27:10.349522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.337774Z","title":"Sscbench: A large-scale 3d semantic scene comple- tion benchmark for autonomous driving","venue":null,"work_id":"a274b9ac-0691-455f-a788-524085988ed9","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.653012Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:985ff6d3b18197c4178be331ec56ce0bfaa530f3b38b62cb740164490628888a","observation_id":"124dc0ed-b6ff-47c8-b618-5c266bb9aa15","resolution":{"observed_at":"2026-08-07T10:27:10.340865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.328495Z","title":"Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers","venue":null,"work_id":"843d86fc-a73f-416f-b426-58231fc76a58","year":2022},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.655784Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:cfa4f4d86947b8dea280570584cc7a12cb70399a9fbb12fd5f75dac8b6f3829e","observation_id":"7e7b7c33-0a4e-47bb-a633-8a5b461fe80e","resolution":{"observed_at":"2026-08-07T10:27:10.331757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.17270","last_updated":"2022-07-13T06:57:28Z","snapshot_observed_at":"2026-07-06T12:55:23.720351Z","submitted_at":"2022-03-31T17:59:01Z","title":"BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.17270","snapshot_observed_at":"2026-08-07T10:27:09.658408Z","title":"Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers.arXiv preprint arXiv:2203.17270, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.658408Z"},"links":{"cited_paper":"/paper/2203.17270","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:11635f6dad5f3a612dce547dcf6b9ea97be25cba6cba397d37a2422adcb9c194","observation_id":"a6edca6f-c2c7-4d14-8e66-efec5e422ac1","resolution":{"observed_at":"2026-08-07T10:27:09.658408Z","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":"2026-08-07T10:27:10.319492Z","title":null,"venue":null,"work_id":"ffdaf537-f898-475f-9942-ef462a20cbee","year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.661647Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:06765f986fac7afc41f7e8d3729bf85fb44fb4f868eefc90155fe0aaa439d96f","observation_id":"c6437916-9931-408d-b43b-31e7aaa1d736","resolution":{"observed_at":"2026-08-07T10:27:10.322628Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01492","last_updated":"2023-07-04T05:55:54Z","snapshot_observed_at":"2026-07-06T15:50:02.603016Z","submitted_at":"2023-07-04T05:55:54Z","title":"FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01492","snapshot_observed_at":"2026-08-07T10:27:09.664421Z","title":"Fb-occ: 3d occupancy prediction based on forward-backward view transformation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.664421Z"},"links":{"cited_paper":"/paper/2307.01492","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:df651e0a80ba51e1e8b3d8cbbdff807e332281a80f5dc3fe83f4e5c8988bb9a9","observation_id":"2a7a1ce1-b2ff-45ca-b856-8c8de5699911","resolution":{"observed_at":"2026-08-07T10:27:09.664421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.14437","last_updated":"2023-01-30T02:39:49Z","snapshot_observed_at":"2026-07-06T13:47:00.653824Z","submitted_at":"2022-08-30T17:55:59Z","title":"MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.14437","snapshot_observed_at":"2026-08-07T10:27:09.667668Z","title":"Maptr: Structured modeling and learning for online vectorized hd map construction.arXiv preprint arXiv:2208.14437, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.667668Z"},"links":{"cited_paper":"/paper/2208.14437","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:26dd67d0a889420d1f894f305765649a0f0d865fc1af6f22b283f3926fb00f79","observation_id":"a163fb4f-b2db-4120-aa9b-15443681e2a9","resolution":{"observed_at":"2026-08-07T10:27:09.667668Z","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":"2026-08-07T10:27:10.311165Z","title":"Maptrv2: An end-to-end framework for online vectorized hd map construction.International Journal of Computer Vision, pages 1–23, 2024","venue":null,"work_id":"dd2a6f11-0ece-4c27-9974-65047cab3a85","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.670494Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:721a733a408fe25202c732f640c5fa5fbf37eae2e430f3985df606b88172e864","observation_id":"b31e1660-596d-4d5c-a2d9-fbf828acb704","resolution":{"observed_at":"2026-08-07T10:27:10.314050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.302306Z","title":"Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3):3292–3310, 2022","venue":null,"work_id":"947ad71e-5817-4e55-a4c9-037bd6a4ad11","year":2022},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.673380Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:1820b60114f8b8030c43b33d3d7708306936ace5b5c9f77340061c669627f76e","observation_id":"f52cf583-05f0-43fc-9a01-33179fda1226","resolution":{"observed_at":"2026-08-07T10:27:10.305385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.292703Z","title":"Sparse4d: Multi-view 3d object detection with sparse spatial-temporal fusion, 2022","venue":null,"work_id":"679d5b21-6bf2-4ca7-859c-42ee56ab7d5a","year":2022},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.676194Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:48e4c2e288854adfb12a91da972567789b60500a5f2237d02aff1a23b0ed03ac","observation_id":"cfe1a25a-b3a1-4528-828c-d82ae1e818a2","resolution":{"observed_at":"2026-08-07T10:27:10.296218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.11722","last_updated":"2023-11-20T12:37:58Z","snapshot_observed_at":"2026-07-06T16:49:53.770544Z","submitted_at":"2023-11-20T12:37:58Z","title":"Sparse4D v3: Advancing End-to-End 3D Detection and Tracking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.11722","snapshot_observed_at":"2026-08-07T10:27:09.678966Z","title":"Sparse4d v3: Advancing end-to-end 3d detec- tion and tracking.arXiv preprint arXiv:2311.11722, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.678966Z"},"links":{"cited_paper":"/paper/2311.11722","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:d407a7599ff04f2e3ec03a97f89ff21d014a2a9ffe5e59029a88b48eb3b4ff2b","observation_id":"a848f260-8689-4e84-9941-abc9fc3a1397","resolution":{"observed_at":"2026-08-07T10:27:09.678966Z","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":"2026-08-07T10:27:10.283946Z","title":"Sparsebev: High-performance sparse 3d object de- tection from multi-camera videos","venue":null,"work_id":"b4255292-6407-4497-98d5-51c2413c1bcb","year":2023},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.681969Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:d8d0f6863b798468148bb23fb5fa9864198fcc85907e223627a21665ad589a2c","observation_id":"6c4ce289-d4c9-4a5f-9872-bf8e17a50d9f","resolution":{"observed_at":"2026-08-07T10:27:10.286911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:09.684609Z","title":"Fully sparse 3d occupancy prediction","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.684609Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:55e581f97fffd2e54b08d2877c5f45f31506c8036b6239dd9126dd704869aa18","observation_id":"2e7875af-d4e5-4445-9679-329b7f281fc0","resolution":{"observed_at":"2026-08-07T10:27:09.684609Z","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":"2026-08-07T10:27:10.269598Z","title":"Petr: Position embedding transformation for multi-view 3d object detection","venue":null,"work_id":"690ac07d-6ca1-4da6-8aad-abeb1766b71e","year":2022},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.687644Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:3831083213add370eb33caa390533690ce86351a5de96f5957081baaac14f5d7","observation_id":"adedbfc9-f045-47a5-afca-5a3fe57368de","resolution":{"observed_at":"2026-08-07T10:27:10.272501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.07849","last_updated":"2022-12-15T14:18:47Z","snapshot_observed_at":"2026-07-06T14:30:59.253425Z","submitted_at":"2022-12-15T14:18:47Z","title":"DETR4D: Direct Multi-View 3D Object Detection with Sparse Attention","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.07849","snapshot_observed_at":"2026-08-07T10:27:09.690707Z","title":"Detr4d: Direct multi-view 3d object detection with sparse attention.arXiv preprint arXiv:2212.07849, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.690707Z"},"links":{"cited_paper":"/paper/2212.07849","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:4b7f09c9e74a3ce75dd10f8e04e7990b839f4ff9fbccaef30dbee377d91ffc90","observation_id":"749f3f96-f7d8-4b0d-aa43-114799082f66","resolution":{"observed_at":"2026-08-07T10:27:09.690707Z","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":"2026-08-07T10:27:10.260490Z","title":"Taming 3dgs: High-quality radiance fields with limited resources","venue":null,"work_id":"1f05e3ca-fa48-40ec-b12f-846fae5ce7fe","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.693921Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:645c9ff376ed8b0e5a6a8b9c18693eafe27f0cf8e8216645372b9980dca844f3","observation_id":"a865c520-0ccd-49c3-9df4-4ffe92ce507f","resolution":{"observed_at":"2026-08-07T10:27:10.263456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.252390Z","title":"Mobileye under the hood.https://www","venue":null,"work_id":"16322456-c586-4d32-9ed8-49e9fbb11ff5","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.697193Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:c9a280b0cecc7f2cb97d19f18f2ea87bbd949a7036b478591c41a05d117628b0","observation_id":"cc4fc14e-11ec-44bf-9174-ded5fd34d940","resolution":{"observed_at":"2026-08-07T10:27:10.255162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.243710Z","title":"Atlas: End- to-end 3d scene reconstruction from posed images","venue":null,"work_id":"a08ba91a-5641-408a-b776-7cd7da403c85","year":2020},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.699815Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:40356f082bf2b90e7f4f2801f48cdcc7759bad29495e94cb4f9f8e167451980c","observation_id":"d8d8655e-4c86-4b6d-859d-212f085b2158","resolution":{"observed_at":"2026-08-07T10:27:10.246544Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.234919Z","title":"Renderocc: Vision-centric 3d occupancy pre- diction with 2d rendering supervision","venue":null,"work_id":"5e98f207-8820-4a46-a622-99e852b50334","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.702462Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:151ff25fd16bdcd0d893508827a85626d7716ef2fef846e75f8492e2eef1d015","observation_id":"c61a913f-7c00-441f-90a6-838cbd0832c0","resolution":{"observed_at":"2026-08-07T10:27:10.237795Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.11921","last_updated":"2025-07-26T22:36:59Z","snapshot_observed_at":"2026-07-06T19:52:11.774784Z","submitted_at":"2024-11-18T05:49:16Z","title":"DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.11921","snapshot_observed_at":"2026-08-07T10:27:09.705146Z","title":"Desire-gs: 4d street gaussians for static-dynamic decomposition and surface reconstruction for urban driving scenes.arXiv preprint arXiv:2411.11921,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.705146Z"},"links":{"cited_paper":"/paper/2411.11921","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:a7df9dd1ec7a10080eb7d7b879f8fd60169a5348fab45b8a6040b765854ec821","observation_id":"968ebb8e-1724-4d44-8c80-58415cc703b2","resolution":{"observed_at":"2026-08-07T10:27:09.705146Z","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":"2026-08-07T10:27:10.225729Z","title":"Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d","venue":null,"work_id":"f38b0476-cffe-46c2-865e-3886dcd641cb","year":2020},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.708524Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:8b4782178c33b659e3e40f894f0fb6a2178bebcd8278f5f33c6b7936945ee2fd","observation_id":"ef67c319-07ff-46f9-a5ce-fffa73826b44","resolution":{"observed_at":"2026-08-07T10:27:10.228763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.215853Z","title":"Lmscnet: Lightweight multiscale 3d semantic com- pletion","venue":null,"work_id":"02a26079-f117-428f-9dee-9c58f253bd40","year":2020},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.711701Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:c788ce638440ce32d37b6d182486059daf8eec74c4b79a101ed10d28bbb7e57e","observation_id":"2a68c60a-74a1-4df7-88ff-1f1d889446c5","resolution":{"observed_at":"2026-08-07T10:27:10.219122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.206860Z","title":"Occupancy as set of points, 2024","venue":null,"work_id":"b0297738-33d3-4ce4-b465-09ccd7542bd3","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.714656Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:66ab68db1af37f2241f3eeb81499fc11edf2c58d5feddff066104e8f168c1564","observation_id":"fe0d7a90-7661-437c-97a3-330d5537fa82","resolution":{"observed_at":"2026-08-07T10:27:10.209896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.197625Z","title":"Semantic scene com- pletion from a single depth image","venue":null,"work_id":"637afbfe-054d-48e7-af54-071e70afe467","year":2017},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.717199Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:554895d8299e7c2e63f7a20d1a6b15953e31ab0e38e47029e7f9fe39b8603f02","observation_id":"285b43f2-7056-4570-82e4-21cf229a03ef","resolution":{"observed_at":"2026-08-07T10:27:10.200718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.188330Z","title":"Tesla AI Day.https://www.youtube.com/ watch?v=ODSJsviD_SU, 2022","venue":null,"work_id":"fa06a1ef-3053-4ef4-af7f-8cc35bd580db","year":2022},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.719750Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:62926865ff0d9a527c8ba57de167c11887392e33389881cf02a6e32eabb291ae","observation_id":"a2026a7c-542d-4af5-894e-24f12a92bf46","resolution":{"observed_at":"2026-08-07T10:27:10.191606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.179154Z","title":"Occ3d: A large-scale 3d occupancy prediction benchmark for autonomous driving.Advances in Neural Information Processing Systems, 36:64318–64330, 2023","venue":null,"work_id":"dc425fbf-5ee7-47d1-ab84-60a15cb60432","year":2023},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.722538Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:fe54c01508dd6d412d12c5b66e8ec43aa3474c35faf991ced642996b74c52fb4","observation_id":"7fe32a69-1bc4-4c40-92bb-088ae0e1efb5","resolution":{"observed_at":"2026-08-07T10:27:10.182366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:09.725182Z","title":"Scene as occupancy","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.725182Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:20ad7bbc7d091d95f749e29efba013b880ff1161460692cb1aecf3000315f598","observation_id":"46de8fe4-d99c-4297-8a8d-04608fddd16b","resolution":{"observed_at":"2026-08-07T10:27:09.725182Z","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":"2026-08-07T10:27:10.163940Z","title":"Exploring object-centric temporal modeling for efficient multi-view 3d object detection","venue":null,"work_id":"75537981-d7c5-4a56-b77f-9b580b0d71c2","year":2023},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.727846Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:a7cc21d3fb31ae0dac2c1835c99db2c7f938761d1dfaff7af30f37d1cbab8445","observation_id":"aefb7714-4dfa-42e8-aece-e5fce032081a","resolution":{"observed_at":"2026-08-07T10:27:10.167004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.154275Z","title":"Exploring object-centric temporal modeling for efficient multi-view 3d object detection","venue":null,"work_id":"4d9cb36b-cd3f-445b-b3ec-49df279e3fce","year":2023},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.730391Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:69813a76289191b56e466a6d11db54c189d99ccc50be3b169efc7b26b0bc8997","observation_id":"fdfefbaf-67e0-4233-9b5d-774a0cb37a57","resolution":{"observed_at":"2026-08-07T10:27:10.157587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.06922","last_updated":"2021-10-13T17:59:35Z","snapshot_observed_at":"2026-07-06T11:57:35.819061Z","submitted_at":"2021-10-13T17:59:35Z","title":"DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.06922","snapshot_observed_at":"2026-08-07T10:27:09.732948Z","title":"Detr3d: 3d object detection from multi-view images via 3d-to-2d queries.arXiv preprint arXiv:2110.06922, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.732948Z"},"links":{"cited_paper":"/paper/2110.06922","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:c8efc05d98daca4b20eb0089eb111cbad89a04e5cd20806a6e4ad9b47542300f","observation_id":"e5fbff04-fcfd-4e18-88eb-0d3d39cbef80","resolution":{"observed_at":"2026-08-07T10:27:09.732948Z","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":"2026-08-07T10:27:10.145575Z","title":"Panoocc: Unified occupancy representation for camera-based 3d panoptic segmentation","venue":null,"work_id":"1d5a75d9-040d-4458-9b3b-3480e68ce734","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.736389Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:fba735323a4088a4c838717db95325ebd64b4d6c3ab50a4a385ba8d4fb6a4b44","observation_id":"1e5c0d5e-161c-47e7-b696-26522dde13f4","resolution":{"observed_at":"2026-08-07T10:27:10.148540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.136211Z","title":"End-to-End Autonomy: A New Era of Self-Driving","venue":null,"work_id":"a2a5dbcd-cb31-4909-bc09-d1984fe7ff1d","year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.739108Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:05fdad5783908465f3a093b224e1cfd5aa2f3f43fd28bfcf399f2695c17508d8","observation_id":"d3e951b9-5bbd-4a5a-a300-1fe803b9bcb6","resolution":{"observed_at":"2026-08-07T10:27:10.139021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.126839Z","title":"Surroundocc: Multi-camera 3d occu- pancy prediction for autonomous driving","venue":null,"work_id":"09e1ee66-3430-4342-9f67-2345484e1d72","year":2023},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.741923Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:7f8e5aae70d34dc0b998b34ca277a4d59109264b13c7462dda5b32ee254fb798","observation_id":"0dd8dbeb-97d3-40e4-8717-343ed3623ec6","resolution":{"observed_at":"2026-08-07T10:27:10.129743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.117850Z","title":"Surroundocc: Multi-camera 3d occu- pancy prediction for autonomous driving","venue":null,"work_id":"e0884a62-f1dc-43cf-a9a4-7613951d8460","year":2023},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.744491Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:4fefade1509fc1f42239e818348ae8b635b4d5b8188a3475c99c54c8e5e94bba","observation_id":"3651c42e-65b3-449e-a862-ae00cf92bc7f","resolution":{"observed_at":"2026-08-07T10:27:10.121269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.12452","last_updated":"2024-11-19T12:19:45Z","snapshot_observed_at":"2026-08-04T14:11:04.295401Z","submitted_at":"2024-11-19T12:19:45Z","title":"GaussianPretrain: A Simple Unified 3D Gaussian Representation for Visual Pre-training in Autonomous Driving","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.12452","snapshot_observed_at":"2026-08-07T10:27:09.747320Z","title":"Gaussianpretrain: A simple uni- fied 3d gaussian representation for visual pre-training in au- tonomous driving.arXiv preprint arXiv:2411.12452, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.747320Z"},"links":{"cited_paper":"/paper/2411.12452","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:7549c80d39c98ee4b448dda5891e02299916f10c994df5dbb55d7ba5ef42627e","observation_id":"08b62c34-1da2-40aa-a769-5ff31de27d9b","resolution":{"observed_at":"2026-08-07T10:27:09.747320Z","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":"2026-08-07T10:27:10.109258Z","title":"Unipad: A universal pre-training paradigm for autonomous driving","venue":null,"work_id":"a96567b3-6aeb-41a7-a253-d28b2f795f24","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.750286Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:6fa7b350972c58d720fc846c6a4d739a2a1f767f21910b4c7e129c6f993f089b","observation_id":"d1602a8f-40f8-48ba-8c7b-39df733310c4","resolution":{"observed_at":"2026-08-07T10:27:10.112129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.100312Z","title":"Cvt-occ: Cost volume temporal fusion for 3d occupancy prediction","venue":null,"work_id":"9772bb9b-68cc-4c9a-8966-caa77d172109","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.753206Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:1deb983806d48352731d44be396987498bad5eccf1f5cf74732b815306da5e66","observation_id":"3625d30c-0b04-404b-8da5-2d31482b1f1e","resolution":{"observed_at":"2026-08-07T10:27:10.103211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:09.755971Z","title":"Center- based 3d object detection and tracking","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.755971Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:b468496e180c0d0a34f7407e38b0f29cedc114aee28710ea8bf938557de133b8","observation_id":"6aa9e232-7c16-4486-8c95-2e3f0abd9d75","resolution":{"observed_at":"2026-08-07T10:27:09.755971Z","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":"2026-08-07T10:27:10.085655Z","title":"Metric3d: Towards zero-shot metric 3d prediction from a single image","venue":null,"work_id":"671af111-436c-4698-9dce-be5d9dee2b17","year":2023},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.759656Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:474cabe324a44a0b9140500e6d0a2e71c36f5a1bee36f4f71f2dda0aa9ef06f6","observation_id":"55a0167a-cfea-4703-8222-b3bfe9d92dfa","resolution":{"observed_at":"2026-08-07T10:27:10.089039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.076364Z","title":"Streammapnet: Streaming mapping network for vectorized online hd map construction","venue":null,"work_id":"e0c60fa1-9e69-44df-930b-f9d083e9800c","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.762494Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:29a7435a4e16c4287d61578219020dfd2fb762d7b40eed511b7408e314721760","observation_id":"ffc31f88-51d8-4cba-80e8-42ff01331e6e","resolution":{"observed_at":"2026-08-07T10:27:10.079724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:09.765185Z","title":"Occformer: Dual-path transformer for vision-based 3d semantic occu- pancy prediction","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.765185Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:21244275e3fcd880557f76b317dfd9b42a1ae79730cb3842fffb86e45e5e3700","observation_id":"d20936a2-3e1b-4f1c-a614-7c73d9f384fe","resolution":{"observed_at":"2026-08-07T10:27:09.765185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:27:09.768007Z","title":"Occformer: Dual-path transformer for vision-based 3d semantic occu- pancy prediction","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.768007Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:124af0d85d82b53d4a882e21a2204d56b3bd53b055f7d4157129b8db8b00ee37","observation_id":"7ca0a8eb-d541-4685-b235-22bd8f8972b1","resolution":{"observed_at":"2026-08-07T10:27:09.768007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.02595","last_updated":"2024-07-08T06:30:01Z","snapshot_observed_at":"2026-07-06T18:09:46.934515Z","submitted_at":"2024-05-04T07:39:25Z","title":"Vision-based 3D occupancy prediction in autonomous driving: a review and outlook","version":2},"cited_work":{"arxiv_id":"2405.02595","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.02595","snapshot_observed_at":"2026-08-07T10:27:09.865567Z","title":"Vision-based 3D occupancy prediction in autonomous driving: a review and outlook","venue":"cs.CV","work_id":"ace97f51-4d83-4bdd-b153-817820754590","year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.770716Z"},"links":{"cited_paper":"/paper/2405.02595","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:43a7816f899446a69b56850e03d1ed71a47fa4a4ce693fae379562799ae60758","observation_id":"507b2178-8d67-4e22-b796-e3b284de607a","resolution":{"observed_at":"2026-08-07T10:27:09.870438Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:09.773652Z","title":"Occworld: Learning a 3d occupancy world model for autonomous driving","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.773652Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:423d8e16eb3e9f55731c852b5fb67c3d860c39201422ae2129e14f51dccbba9f","observation_id":"8d578570-d6eb-4550-9d9a-bf4e471f4b8c","resolution":{"observed_at":"2026-08-07T10:27:09.773652Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.10371","last_updated":"2024-12-13T18:59:30Z","snapshot_observed_at":"2026-07-06T20:06:43.917795Z","submitted_at":"2024-12-13T18:59:30Z","title":"GaussianAD: Gaussian-Centric End-to-End Autonomous Driving","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.10371","snapshot_observed_at":"2026-08-07T10:27:09.776419Z","title":"Gaussianad: Gaussian-centric end-to- end autonomous driving.arXiv preprint arXiv:2412.10371,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.776419Z"},"links":{"cited_paper":"/paper/2412.10371","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:fff2ae8af7522295d1e3b249153dfc7df04ac73a2f9ba6b6fad14a0913ced99b","observation_id":"1a5d0288-f2f0-445e-af30-3ac3e00acefb","resolution":{"observed_at":"2026-08-07T10:27:09.776419Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04159","last_updated":"2021-03-18T03:14:26Z","snapshot_observed_at":"2026-07-06T10:02:45.105181Z","submitted_at":"2020-10-08T17:59:21Z","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.04159","snapshot_observed_at":"2026-08-07T10:27:09.779311Z","title":"Deformable detr: Deformable trans- formers for end-to-end object detection.arXiv preprint arXiv:2010.04159, 2020","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.779311Z"},"links":{"cited_paper":"/paper/2010.04159","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:531dcd445f84ea80a8660280908c3d93cf738742ba00033f008c321ab625d92e","observation_id":"1132323e-fe16-4588-9492-99ba2a9f1b6c","resolution":{"observed_at":"2026-08-07T10:27:09.779311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.10373","last_updated":"2024-12-13T18:59:54Z","snapshot_observed_at":"2026-08-06T19:23:47.886492Z","submitted_at":"2024-12-13T18:59:54Z","title":"GaussianWorld: Gaussian World Model for Streaming 3D Occupancy Prediction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.10373","snapshot_observed_at":"2026-08-07T10:27:09.782435Z","title":"Gaussianworld: Gaussian world model for streaming 3d occupancy prediction.arXiv preprint arXiv:2412.10373, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.782435Z"},"links":{"cited_paper":"/paper/2412.10373","citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:05be98069f92579626ad741d391e28823c53d3bfe6527eb965993969c27387e6","observation_id":"263af1f5-4254-4c17-8502-2df24acfbbc6","resolution":{"observed_at":"2026-08-07T10:27:09.782435Z","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":"2026-08-07T10:27:10.050578Z","title":"The nuScenes dataset[1] provides 1000 scenes of surround- view driving scenes","venue":null,"work_id":"890f4236-8d15-42c0-9710-072ea8bef74f","year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.785417Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:f0073abc3d907dad1a3c03fe04be17226da7fb0024eb2a8c408568eaa4a9353e","observation_id":"fbb5822f-dd41-4455-aa85-50d6b1af5d0a","resolution":{"observed_at":"2026-08-07T10:27:10.053642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.042051Z","title":"S2GO- Small uses an ImageNet1k backbone, while S2GO-Base leverages nuImages pre-training","venue":null,"work_id":"77b484c7-491a-46b7-98bc-d8e2d1374c02","year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.788487Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:6f1dc689c26dd45fa45a27e73f6829fb7d3e1878c87b61478979109eef65edfd","observation_id":"6434e753-cee2-4f01-9748-9945b89a1cf2","resolution":{"observed_at":"2026-08-07T10:27:10.044757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.032725Z","title":null,"venue":null,"work_id":"405871b4-c8c1-444b-b7c6-04c1435aefed","year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.791320Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:8845b7b279e4b2ddd55b40d194f47ad08208c1b7f7b4871fe581d48983f1070d","observation_id":"a1f896ab-2a40-42d7-8b34-c1b99508d832","resolution":{"observed_at":"2026-08-07T10:27:10.035806Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.022925Z","title":"We emphasize that unlike prior projection-based works, S2GO incursno additional costfrom a longer history","venue":null,"work_id":"3dd6caaa-93a1-41b4-aa6f-e0a42cc38ec8","year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.794121Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:f2e5bd53a44ce3be745b30fadf36bf187de9b7927cd1de263d5d09f2361704cf","observation_id":"e2ebea77-7d41-462b-bb33-c7c3f4ff1b1c","resolution":{"observed_at":"2026-08-07T10:27:10.026266Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.013247Z","title":"The backbone, temporal transformer, gaussian pre- diction, and propagation take 11.54ms, 22.79ms, 2.22ms, and 1.45ms, respectively","venue":null,"work_id":"88429671-cd12-488f-acd2-948f162092a3","year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.796958Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:e7ffe9058bd2edb8844667ccd2ef8f1f534a32eafb16c4cdb38143330b7e2e56","observation_id":"a7854486-93b7-4f8c-b8d2-e4f90419f215","resolution":{"observed_at":"2026-08-07T10:27:10.016874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:10.003674Z","title":"We find that this largely maintains performance, indicating the general- ity of our pretraining pipeline","venue":null,"work_id":"74d4040b-6650-49d2-97ff-bdd5e31c489b","year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.799683Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:3ef3662d076fc89d334ad5213851b263a16bd85f5f88deb421504195959cb406","observation_id":"8fb8d43f-63b8-4ebf-a62a-f2343cdf4f0e","resolution":{"observed_at":"2026-08-07T10:27:10.007049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:09.993469Z","title":null,"venue":null,"work_id":"624f42f8-a954-4592-8edd-51bda7508d55","year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.802483Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:10595a5725b403a220682985db50f3b7ef9837dcb5094c8b916c54be2fe1568d","observation_id":"a1303125-dc2d-488a-861f-f9e209b0d23f","resolution":{"observed_at":"2026-08-07T10:27:09.996853Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:27:09.984208Z","title":"6 we visualize example predictions and ground truth from the SSCBench-KITTI-360 dataset","venue":null,"work_id":"b15905f6-b44f-4188-b6d7-a8c6b3ea8c6e","year":null},"citing_paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T10:27:09.805799Z"},"links":{"citing_paper":"/paper/2506.05473"},"observation_digest":"sha256:75863e323be3d1d684dce5dcec87a9c0624f851291d8a9ddf9033cc52fdaa562","observation_id":"b99ab42b-5852-4ad4-b882-7f6091f24cca","resolution":{"observed_at":"2026-08-07T10:27:09.987262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.05473","last_updated":"2025-06-05T18:00:11Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T10:18:24.067035Z","submitted_at":"2025-06-05T18:00:11Z","title":"S2GO: Streaming Sparse Gaussian Occupancy Prediction"},"reference_resolution":{"displayed":76,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":28,"verified_exact":1,"verified_fuzzy":46},"total_outbound_references":76},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 0 inbound Pith citation observations for arXiv:2506.05473."}