{"as_of":"2026-08-12T20:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6d3650da3d966eab788b6cd3a75011961ac5404bfb7a975397ef995600808e15","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T21:38:59.954987Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2501.04373/citation-record","integrity":"/paper/2501.04373/integrity","json":"/paper/2501.04373/citation-record.json","paper":"/paper/2501.04373"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:39:00.700339Z","title":"Temporal point cloud fusion with scene flow for robust 3d object tracking,","venue":null,"work_id":"e5bac582-6c0c-4309-92a7-5984ab18d1c0","year":2022},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.701601Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:f0c90335edd056e0370ffec89b1d25d114bec76948d9e1c33c37f78351ef17d7","observation_id":"e81e79c9-9029-4fe2-a905-40b4789bf7aa","resolution":{"observed_at":"2026-08-10T21:39:00.706658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.678992Z","title":"Caltracker: Cross-task association learning for multiple object tracking,","venue":null,"work_id":"be26da91-9536-492e-82ca-646aa5ea857d","year":2023},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.708350Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:5379fa451138fa088c403ef4bf0469aded79d9e37f1151d10d53eeaf672689e6","observation_id":"ad9138da-33bc-430e-9445-248ba444b69d","resolution":{"observed_at":"2026-08-10T21:39:00.685091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.658567Z","title":"Hybrid cross-transformer-kpconv for point cloud segmentation,","venue":null,"work_id":"cac8ea8e-4a94-4a44-9135-fe0429d80a02","year":2023},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.717003Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:38eccdd8740d6c11485d1eae330309ef315e4e9b69852c5bbc3ba9484b838e11","observation_id":"1547eee6-31e3-4bd8-8a5f-a86a4f24681d","resolution":{"observed_at":"2026-08-10T21:39:00.666211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.639432Z","title":"Pointrcnn: 3d object proposal generation and detection from point cloud,","venue":null,"work_id":"22c2c0f8-133e-44e2-b30f-26e4ff6393b3","year":2019},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.723316Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:7f3040cacd83ee080741f251a939759bf2c51fa906fe50c98895b4479eb2c3ff","observation_id":"e769ebce-304b-46b0-8546-1b45d3e1aa4b","resolution":{"observed_at":"2026-08-10T21:39:00.645826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:38:59.732093Z","title":"Pv- rcnn: Point-voxel feature set abstraction for 3d object detection,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.732093Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:f0420a16b7ab86b64ba060686da49a26d4f35038ff63b128bf0a9689196f9cc4","observation_id":"8b8a3920-da5a-47c8-8a31-8ea5f0f34158","resolution":{"observed_at":"2026-08-10T21:38:59.732093Z","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":"2026-08-10T21:39:00.607642Z","title":"V oxel r- cnn: Towards high performance voxel-based 3d object detection,","venue":null,"work_id":"972a9f93-5593-42c6-9c6e-d6cb0a26479d","year":2021},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.738439Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:d73072532880055822757407bd4195330132bbdb309e7b3531810cea6bdf65ea","observation_id":"01848d62-4715-45c0-a852-381779e3d374","resolution":{"observed_at":"2026-08-10T21:39:00.613860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.589855Z","title":"Dtssd: Dual-channel transformer-based network for point-based 3d object detection,","venue":null,"work_id":"26122314-b7ea-4e2c-9a9e-9777c1abd6fb","year":2023},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.744655Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:bb9f641821ae10bb87dbe25c80869401e2e54f50ca255fbd6dec7cccfc26c92e","observation_id":"ee9649ca-8185-49e2-8d5c-eec832c4dee0","resolution":{"observed_at":"2026-08-10T21:39:00.595895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.572511Z","title":"Imfusion: Boosting two- stage 3d object detection via image candidates,","venue":null,"work_id":"61249608-e777-42bd-9290-5a197563a746","year":2023},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.750762Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:b82204c3a39755c8cacbc20aba452630cf5268d1b50919526ad0ad3107b837a6","observation_id":"4cbb73b6-0dd1-4b21-956d-3efba726d0b7","resolution":{"observed_at":"2026-08-10T21:39:00.577713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:38:59.756760Z","title":"Frustum pointnets for 3d object detection from rgb-d data,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.756760Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:93d3642cb4cd7d4831fa92cc5ccd89418a3bd4149f3f981a782ea0feb5643b4d","observation_id":"a62fff31-2754-4374-9786-33724e76dd4f","resolution":{"observed_at":"2026-08-10T21:38:59.756760Z","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":"2026-08-10T21:39:00.544984Z","title":"Frustum- pointpillars: A multi-stage approach for 3d object detection using rgb camera and lidar,","venue":null,"work_id":"daac3e49-e737-463c-9d3e-beebcbaaafa7","year":2021},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.762349Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:8d6785cea753fec212ceda3dbca09a6044f885e11bd9b0e2092b161c1642a1fb","observation_id":"f7613c04-1c87-438c-afed-657677182e53","resolution":{"observed_at":"2026-08-10T21:39:00.550265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.526943Z","title":"Frustum convnet: Sliding frustums to aggregate local point-wise features for amodal 3d object detection,","venue":null,"work_id":"08727227-9394-42da-976d-efda4115949f","year":2019},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.768561Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:19b39b02d134e24f6d277955f5bc6ff5de567f5d491298424483baafb37ff436","observation_id":"bf623a97-0c4b-4920-92a9-c9a17d77e941","resolution":{"observed_at":"2026-08-10T21:39:00.533008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.508292Z","title":"Multi-view 3d object detection network for autonomous driving,","venue":null,"work_id":"c3fa6a5d-ab53-4422-9c61-9383165efd41","year":2017},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.775064Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:d33fae8f4364332b380211eed80230a30be074c16e0b51a2b944a59699a9f8e2","observation_id":"9131893e-38e8-4878-9a9c-81abf93181dd","resolution":{"observed_at":"2026-08-10T21:39:00.513855Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.489760Z","title":"Joint 3d proposal generation and object detection from view aggregation,","venue":null,"work_id":"9561a7e4-d6db-4c03-afa9-4feb6967f092","year":2018},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.781271Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:df4c93e97e9fa0f6a8bae53fee59ca0e1182a3b6aec4084047d2fe62c9b3f9dd","observation_id":"8c0363c4-c7fa-4365-9f4b-02f9772d391f","resolution":{"observed_at":"2026-08-10T21:39:00.495743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.472942Z","title":"Deep continuous fusion for multi-sensor 3d object detection,","venue":null,"work_id":"a17576d6-384c-4ce6-9b12-8a787023b18a","year":2018},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.787559Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:4c22ed73a348cda031fb20dd7015592b18ffd38d7fcc1c1856c98d6caf0c7782","observation_id":"79920e2c-9598-42da-ab59-d8e8209597c4","resolution":{"observed_at":"2026-08-10T21:39:00.478200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.455964Z","title":"Scanet: Spatial-channel attention network for 3d object detection,","venue":null,"work_id":"82508e48-c18f-4016-be9a-0feb10b68c7f","year":2019},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.792696Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:089e00db6bc60920a86f9583dcbd62fc85e7bf3be2c78b0b94fd185affeabf47","observation_id":"20b40b15-ebd9-488e-85ea-1b5ab730140c","resolution":{"observed_at":"2026-08-10T21:39:00.461155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.438266Z","title":"Graphalign: Enhancing accurate feature alignment by graph matching for multi-modal 3d object detection,","venue":null,"work_id":"6ec2b2ea-5540-4f0b-b69c-878e66bf794b","year":2023},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.797850Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:4f0e677f89a019b21fb20792b17058e0431e4fad670c9a59c0c48a21ca75585c","observation_id":"3cdd04c5-1f1a-4102-a280-6493c573ba7e","resolution":{"observed_at":"2026-08-10T21:39:00.443838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:38:59.803833Z","title":"Is-fusion: Instance-scene collaborative fusion for multimodal 3d object detection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.803833Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:bcf858bb0ab68b52f7a271a61572626eb1abdecca8e4e6da3da19b19e526f1c0","observation_id":"f9068e8a-30e6-43bb-874d-d4896a332730","resolution":{"observed_at":"2026-08-10T21:38:59.803833Z","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":"2026-08-10T21:39:00.408377Z","title":"Graphbev: Towards robust bev feature alignment for multi-modal 3d object detection,","venue":null,"work_id":"cb28114b-f592-4a0a-ba09-405970a1fd53","year":2025},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.809240Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:cf51cbaa6f8ef54b16f063eaafe21bb65bb25b230ea0542ae4a0bc1077e29923","observation_id":"c7b97e07-e7c1-4364-9382-6f1e0034b8a7","resolution":{"observed_at":"2026-08-10T21:39:00.414137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.389061Z","title":"Pointpainting: Se- quential fusion for 3d object detection,","venue":null,"work_id":"4d00989f-a589-4762-8638-43cf9ae46b85","year":2020},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.818589Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:a0ac85fd5a27e87ba17580341027fd93cbeca7f6a4fe33bf05e68d3f23bb250d","observation_id":"e4b6d66f-100f-40e9-9ac7-d4f30bb4378c","resolution":{"observed_at":"2026-08-10T21:39:00.395821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.368941Z","title":"Mvx-net: Multimodal voxelnet for 3d object detection,","venue":null,"work_id":"7a51518e-ee59-41ef-9f52-4986d33fecdf","year":2019},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.825082Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:0a39eb0e0de090515c54d204eaca2e86f1ea0c7e93fab3f9bafb443ea7ce0175","observation_id":"41bb301a-6c20-4904-b2ab-15092da16984","resolution":{"observed_at":"2026-08-10T21:39:00.376129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.348375Z","title":"Epnet: Enhancing point features with image semantics for 3d object detection,","venue":null,"work_id":"381c44e1-6688-436a-887c-32a2f447ce65","year":2020},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.830616Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:c3647cfc1757ada3b705776590524f03390a6381cafab908060b485f5ba98656","observation_id":"f4ff4f51-1b5f-4990-9ac1-4b28eda1879c","resolution":{"observed_at":"2026-08-10T21:39:00.354387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.329159Z","title":"Pi-rcnn: An efficient multi-sensor 3d object detector with point-based attentive cont-conv fusion module,","venue":null,"work_id":"33ede876-2a5e-45cf-967d-43cccba1e148","year":2020},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.835442Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:3e38cb6015ad06a828646ab25e6bc5fb39add2623cefbe325c9c7252ec9d5605","observation_id":"5d42e194-256b-4506-888b-c4e319d8e3f7","resolution":{"observed_at":"2026-08-10T21:39:00.335541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.309032Z","title":"Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection,","venue":null,"work_id":"1deab07e-deca-4a7b-b3cd-95418fd7ef03","year":2022},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.841091Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:ad65443817935ac71763c6b7a4b94fcd69ddb0d169ef352d555cc6de4fec31e5","observation_id":"2fd79ee8-06cf-498f-861b-50b63630f3ad","resolution":{"observed_at":"2026-08-10T21:39:00.314660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.289772Z","title":"Cat-det: Contrastively augmented transformer for multi-modal 3d object detection,","venue":null,"work_id":"4180bc08-dc33-47fa-bb9b-d22b7a2d7bf7","year":2022},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.847072Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:cf84cab3477c3236f193fd3434ecc1e0313b7192ec0fe56eb227ef4c2e1d44c9","observation_id":"1bf40150-4810-44f7-9c5c-7363854a38bc","resolution":{"observed_at":"2026-08-10T21:39:00.295982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.270776Z","title":"Multi-task multi- sensor fusion for 3d object detection,","venue":null,"work_id":"0b88c994-043a-4909-b31b-a8227565fe89","year":2019},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.853049Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:ff80c4c0543e6d4676809e7962330deefec4c4e3b84e92ea87dd4431354219f5","observation_id":"41f467af-3408-4e02-9855-04e4b49bb78b","resolution":{"observed_at":"2026-08-10T21:39:00.276610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.10642","last_updated":"2023-04-15T13:05:44Z","snapshot_observed_at":"2026-07-06T12:50:01.067926Z","submitted_at":"2022-03-20T20:41:55Z","title":"FUTR3D: A Unified Sensor Fusion Framework for 3D Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.10642","snapshot_observed_at":"2026-08-10T21:38:59.859436Z","title":"Futr3d: A unified sensor fusion framework for 3d detection,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.859436Z"},"links":{"cited_paper":"/paper/2203.10642","citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:8d8b1cc137361796cd0b1055e137e9fd2a82a42a8d9d07342b7b3a81116c1559","observation_id":"d94ee8ab-177b-41c5-ac96-4231688d4219","resolution":{"observed_at":"2026-08-10T21:38:59.859436Z","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":"2026-08-10T21:38:59.868146Z","title":"Focal sparse convolutional networks for 3d object detection,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.868146Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:a06f3267e250b942a6b961858db1ce4af3093164e653b7bb59f280a8bc7b1738","observation_id":"b238ab5a-72c8-49b4-a477-380de0e7e377","resolution":{"observed_at":"2026-08-10T21:38:59.868146Z","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":"2026-08-10T21:39:00.239909Z","title":"Centerfusion: Center-based radar and camera fusion for 3d object detection,","venue":null,"work_id":"56e524d8-7d3b-4974-9e58-357888c428ae","year":2021},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.874395Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:93235a23ace5cc342b38468a0d5122b3efa4ac8e8a7a248a1fa5d60295adf494","observation_id":"b601b76a-fee0-4887-8cfe-402512dc25bf","resolution":{"observed_at":"2026-08-10T21:39:00.245530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:38:59.883112Z","title":"Pointnet++: Deep hierarchical feature learning on point sets in a metric space,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.883112Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:4952f8cbde35c10b1918cd47d250d66daf8ab9f78daf3b656ec9b4f51741578a","observation_id":"062dd99c-9eee-4cef-9a79-991728934022","resolution":{"observed_at":"2026-08-10T21:38:59.883112Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.09492","last_updated":"2019-08-26T06:27:01Z","snapshot_observed_at":"2026-08-10T20:08:05.494461Z","submitted_at":"2019-08-26T06:27:01Z","title":"Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.09492","snapshot_observed_at":"2026-08-10T21:38:59.889917Z","title":"Class-balanced grouping and sampling for point cloud 3d object detection,","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.889917Z"},"links":{"cited_paper":"/paper/1908.09492","citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:ddc630ed3142f0f3f4687caf0f12ec8d43e0da32eb4741cfab0db9811684156d","observation_id":"f4670acc-31be-478c-bc52-e420808a66b1","resolution":{"observed_at":"2026-08-10T21:38:59.889917Z","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":"2026-08-10T21:38:59.899953Z","title":"Center-based 3d object detection and tracking,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.899953Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:dc2addf9d35ade349404a3c6e3178749cc9affd698922ff0bc06fb25d9b02ef8","observation_id":"0c35d3c9-57e2-4dad-a566-6011bcacd8c1","resolution":{"observed_at":"2026-08-10T21:38:59.899953Z","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":"2026-08-10T21:38:59.905540Z","title":"V oxelnext: Fully sparse voxelnet for 3d object detection and tracking,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.905540Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:7f205fe0fa3f0f9c58bb89c9299c66fa1921e69e459e3568edc7ca7f6a8b6e88","observation_id":"fe2625e3-db2f-4854-b56c-a169be7baab6","resolution":{"observed_at":"2026-08-10T21:38:59.905540Z","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":"2026-08-10T21:39:00.185633Z","title":"Largekernel3d: Scaling up kernels in 3d sparse cnns,","venue":null,"work_id":"d86f4da5-39d8-4f5a-abc2-b6ecdde1db17","year":2023},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.911995Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:72d07248030f7271dca29941242b14c66f267b09e6665421936e2a973688ad50","observation_id":"827ad8c9-2da4-4ab0-9c72-8d2a895f15c6","resolution":{"observed_at":"2026-08-10T21:39:00.191049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.167620Z","title":"Vista: Boosting 3d object detection via dual cross-view spatial attention,","venue":null,"work_id":"1c6e16ef-ff9f-4ced-ad49-8bd9e7ac13ae","year":2022},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.917212Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:5b9c23b1de8728fe253a881a8c62739dc5c19476d67c9181d0be08d90e83a792","observation_id":"f7d57cad-c5b3-4cb3-afdf-25a17389baaa","resolution":{"observed_at":"2026-08-10T21:39:00.172856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.148406Z","title":"Multimodal virtual point 3d detection,","venue":null,"work_id":"c2470f9b-dd1d-40b3-b300-0a327e4fee55","year":2021},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.923871Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:b61833b4c24fbba2f403d15bd7c3ec75192a9c54c69d6303ce6d2bdb0b4a5d49","observation_id":"bbce5770-fd02-4fdf-b0ae-22762d43993a","resolution":{"observed_at":"2026-08-10T21:39:00.154642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:39:00.127964Z","title":"A unified query- based paradigm for point cloud understanding,","venue":null,"work_id":"5c2138f4-0734-4507-b846-46efebca0f09","year":2022},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.929866Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:060cb24096c6231445f6a3615890884ca9693a21f669bd926fe3a37496b395a0","observation_id":"5e3b7611-74e7-4712-ba70-92563c32958c","resolution":{"observed_at":"2026-08-10T21:39:00.133720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:38:59.936800Z","title":"Are we ready for autonomous driving? the kitti vision benchmark suite,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.936800Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:38e3e321047c4e366409b01ba604881efc6eed2537541676594206f58907e40b","observation_id":"6b920179-080c-4902-a7a8-71b638e46e0a","resolution":{"observed_at":"2026-08-10T21:38:59.936800Z","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":"2026-08-10T21:38:59.943799Z","title":"nuscenes: A multimodal dataset for autonomous driving,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.943799Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:194ecf5fec3782dc296e2d89c0a113812fb37afc83cb4dcc06ade0bbe98aa745","observation_id":"e23c1a36-0d17-4555-a321-30fa1409be1e","resolution":{"observed_at":"2026-08-10T21:38:59.943799Z","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":"2026-08-10T21:38:59.949351Z","title":"Penet: Towards precise and efficient image guided depth completion,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.949351Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:82b3af57be743e99d3e8e0b815468a7622d1d3de6796883a47aaf4a624847111","observation_id":"803ca46e-32aa-4d78-8cbd-d417d34b2771","resolution":{"observed_at":"2026-08-10T21:38:59.949351Z","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":"2026-08-10T21:39:00.060980Z","title":"Openpcdet: An open-source toolbox for 3d object detection from point clouds,","venue":null,"work_id":"33df3a29-2d25-4cf9-b42e-4fd8be87ec01","year":2020},"citing_paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T21:38:59.954987Z"},"links":{"citing_paper":"/paper/2501.04373"},"observation_digest":"sha256:401d7bc1738407054db6fa47e6ca2014f7463113b15b84956a6aca70fd3b76ec","observation_id":"6464f750-4e03-4f8b-aedb-ff35d793003d","resolution":{"observed_at":"2026-08-10T21:39:00.070108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.04373","last_updated":"2025-01-08T09:26:36Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T00:00:27.923776Z","submitted_at":"2025-01-08T09:26:36Z","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":0,"verified_fuzzy":28},"total_outbound_references":40},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2501.04373."}