{"as_of":"2026-08-16T09:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b69456657e35aa09397a89a10d850b5110c7ce6c81b02bfd54f0a9d2cf38b6af","coverage":[{"denominator":300,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T16:56:45.940439Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2508.19593/citation-record","integrity":"/paper/2508.19593/integrity","json":"/paper/2508.19593/citation-record.json","paper":"/paper/2508.19593"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T16:56:45.522176Z","title":"http://www.cvlibs.net/datasets/kitti/eval_object.php? obj_benchmark=3d","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.522176Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:731d0ad6eeadb3a267bb08faf597a87d6ac3e0c74aff9677852fff29d38b293d","observation_id":"0023e98d-1609-48cb-ae04-3331bcf748d1","resolution":{"observed_at":"2026-08-15T16:56:45.522176Z","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":"2026-08-15T16:56:45.528794Z","title":"Augmentedrealitymeetscomputervision: Efficientdatagenerationforurbandrivingscenes","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.528794Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:cbf1f8346ba9774a99bd51a1133d35b16bd0d71b6523cabac7cb0a1e41a4d85d","observation_id":"4da58671-c078-4769-958e-981511608947","resolution":{"observed_at":"2026-08-15T16:56:45.528794Z","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":"2026-08-15T16:56:45.536710Z","title":"Learningdetectionwithdiverseproposals","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.536710Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:b8ff56a6567e222c125aee0b393d64960f1399794a580ef271b85ac1ed7ff4d5","observation_id":"92d957ec-6aac-4912-b885-ba4b87f27b68","resolution":{"observed_at":"2026-08-15T16:56:45.536710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.12781","last_updated":"2025-03-22T15:40:42Z","snapshot_observed_at":"2026-08-15T03:30:37.048803Z","submitted_at":"2024-07-17T17:59:05Z","title":"VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.12781","snapshot_observed_at":"2026-08-15T16:56:45.540994Z","title":"VD3D: Taming large video diffusion transformers for 3D camera control.arXiv preprint arXiv:2407.12781, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.540994Z"},"links":{"cited_paper":"/paper/2407.12781","citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:3a491eb90a52eec41234518ddc5a1b9f1f6d5e89626fee8306582b7dce81f012","observation_id":"17093077-e287-4242-b0b0-ebbe2b100c9f","resolution":{"observed_at":"2026-08-15T16:56:45.540994Z","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":"2026-08-15T16:56:45.545398Z","title":"MonoFENet: Monocular3D object detection with feature enhancement networks.IEEE Transactions on Image Processing, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.545398Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:14b888c274b302ac67f0fddef89d0188bb60c05b523fdde505d3c31ee03386cf","observation_id":"b20655a9-4c02-4e4f-92ba-e6c1c0bb34de","resolution":{"observed_at":"2026-08-15T16:56:45.545398Z","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":"2026-08-15T16:56:45.549785Z","title":"SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.549785Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:1da90f6539e6e698230e16c7904715a0f95033fcb468a5def443f365c87d3234","observation_id":"b55ff6da-8ed7-49f9-a09b-93f3e6f5d02c","resolution":{"observed_at":"2026-08-15T16:56:45.549785Z","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":"2026-08-15T16:56:45.553918Z","title":"Monocular differentiable rendering for self-supervised3D object detection","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.553918Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:7b428b9c2329d0e304213e233029c05cd44cb3697985485a097683b719b9ef70","observation_id":"83110045-b69c-46c2-ba2d-22cac73271f0","resolution":{"observed_at":"2026-08-15T16:56:45.553918Z","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":"2026-08-15T16:56:45.560593Z","title":"Learning with differentiable perturbed optimizers","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.560593Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:87ff05ff006803a6022d7c441118bb56b973cb354566929383da724c2fcaaea9","observation_id":"31306a7c-5d34-4dbe-9ddc-1477a5ac95fa","resolution":{"observed_at":"2026-08-15T16:56:45.560593Z","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":"2026-08-15T16:56:45.564714Z","title":"An inequality for Mill’s ratio.The Annals of Mathematical Statistics,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.564714Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:b11f1655aa1ce2aa656b4831b403ef8e634e18c19a1b1f6141a62ae6e5ef00d0","observation_id":"8f1f1a19-fabf-461a-9210-20cc77093e85","resolution":{"observed_at":"2026-08-15T16:56:45.564714Z","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":"2026-08-15T16:56:45.569615Z","title":"Fast differentiable sorting and ranking","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.569615Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:2b7722053b05944cab371d0e10c867fc8a442918eb0c614c45e30005f4505356","observation_id":"6646b1e8-2efc-4f9c-be78-858a19a71b72","resolution":{"observed_at":"2026-08-15T16:56:45.569615Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.10934","last_updated":"2020-04-23T02:10:02Z","snapshot_observed_at":"2026-08-16T04:20:57.069798Z","submitted_at":"2020-04-23T02:10:02Z","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.10934","snapshot_observed_at":"2026-08-15T16:56:45.573780Z","title":"YOLOv4: Optimal speed and accuracy of object detection.arXiv preprint arXiv:2004.10934, 2020","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.573780Z"},"links":{"cited_paper":"/paper/2004.10934","citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:ab4c28f9dd27d64b7cee7e9ec3b7068b4fbe0efea8d2984d9e5224ca135f9f75","observation_id":"fd094294-ce06-4b7a-847b-5639a252f8d1","resolution":{"observed_at":"2026-08-15T16:56:45.573780Z","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":"2026-08-15T16:56:45.578309Z","title":"Soft-NMS–improving object detection with one line of code","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.578309Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:511ba0ade9f1fcbd4025dd3e34532eba08db98c6bfaebdf5afff802f805cbf43","observation_id":"66fdfb8a-ba95-4b05-84ba-b95ce6a24b38","resolution":{"observed_at":"2026-08-15T16:56:45.578309Z","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":"2026-08-15T16:56:45.583474Z","title":"Soft-NMSimplementa- tion","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.583474Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:ca05f43a4a69a00c4fc01f55f74360444bc4c75de131309fe420d0a280f7b204","observation_id":"d73c2ef1-ca77-4f23-a943-4fc2c1fd627c","resolution":{"observed_at":"2026-08-15T16:56:45.583474Z","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":"2026-08-15T16:56:45.595768Z","title":"Omni3D: A large benchmark and model for3D object detection in the wild","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.595768Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:b2cf3cc90dd678bc6a7dd9021a56018a106f9d23d0ac993485cb81f702dfb5c6","observation_id":"104d9128-7471-4b6a-9820-4164f1329948","resolution":{"observed_at":"2026-08-15T16:56:45.595768Z","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":"2026-08-15T16:56:45.600473Z","title":"M3D-RPN: Monocular3D region proposal network for object detection","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.600473Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:ea9b2a4d2ff9152811ec39012a67e6db354be658e8a4736875290feb411bf742","observation_id":"9a6a7c09-bb65-40a8-b3e7-4b23fa402dc0","resolution":{"observed_at":"2026-08-15T16:56:45.600473Z","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":"2026-08-15T16:56:45.604687Z","title":"Pedestriandetectionwithautoregressivenetworkphases","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.604687Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:1d9659794ef449e18f442e1290a50992b3331bd82e301283cc55813c7f5d46be","observation_id":"f042fba5-5961-4fbc-a876-7b04aa952fd0","resolution":{"observed_at":"2026-08-15T16:56:45.604687Z","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":"2026-08-15T16:56:45.608496Z","title":"Kinematic3D object detectioninmonocularvideo","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.608496Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:03eab90542f707fe587f8e32219ef400857ec0d452b263ee45bdc768a57d9fc7","observation_id":"6f9967a6-9200-41ae-a200-b673ade73999","resolution":{"observed_at":"2026-08-15T16:56:45.608496Z","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":"2026-08-15T16:56:45.612691Z","title":"Illuminating pedestrians via simultaneous detection & segmentation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.612691Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:ab966ce8d51cec63acfc103671f9b82c57ae8b4b6f06dcdc9c16616389ef193c","observation_id":"6b4185e8-60d2-4838-a823-162752425d05","resolution":{"observed_at":"2026-08-15T16:56:45.612691Z","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":"2026-08-15T16:56:45.616890Z","title":"Convolution from first principles","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.616890Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:76421c499b0839a7ed98065a6bdc5477794a37f6b85aa1a8bb41dfec43ff278f","observation_id":"66377f09-439c-4887-ac13-4f5d3e9a192e","resolution":{"observed_at":"2026-08-15T16:56:45.616890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.13478","last_updated":"2021-05-02T16:16:03Z","snapshot_observed_at":"2026-08-12T23:06:05.148534Z","submitted_at":"2021-04-27T21:09:51Z","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.13478","snapshot_observed_at":"2026-08-15T16:56:45.620760Z","title":"Geometricdeeplearning: Grids, groups, graphs, geodesics, and gauges.arXiv preprint arXiv:2104.13478, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.620760Z"},"links":{"cited_paper":"/paper/2104.13478","citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:db0b6ae746a683f879ee69df19b9eb75c22971fa5902aeb1c9a1aa914d826412","observation_id":"90f18c77-8708-4f9e-aab2-2f33362eb4d9","resolution":{"observed_at":"2026-08-15T16:56:45.620760Z","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":"2026-08-15T16:56:45.625021Z","title":"The non-existence of general-case view-invariants","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.625021Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:3b3cc6dcd5573567ae6a5c85168459ffd82915a5626d5c67371218660b90ab94","observation_id":"40816baf-05b6-40ce-8329-027e231a12c8","resolution":{"observed_at":"2026-08-15T16:56:45.625021Z","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":"2026-08-15T16:56:45.628976Z","title":"nuScenes: A multimodal dataset for autonomous driving","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.628976Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:dd9fdd562a4a6497cdbd7f8b0e6ca35f4e2aa4a3b1af32d064abf28a68100e71","observation_id":"9ca668fc-ea6b-47ad-83b0-f571309969cb","resolution":{"observed_at":"2026-08-15T16:56:45.628976Z","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":"2026-08-15T16:56:45.632488Z","title":"2 die when tesla crashes into parked tractor-trailer in florida","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.632488Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:fe402a3eada70d3b2d0670fd5ce45815ab27f1d0ec338087091bf8a5ceeb0f68","observation_id":"a0298d51-b210-4b0e-be63-c6b556f14fe9","resolution":{"observed_at":"2026-08-15T16:56:45.632488Z","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":"2026-08-15T16:56:45.636293Z","title":"End-to-endobjectdetectionwithtransformers","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.636293Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:74de8009e2e8885cb31c040b4559fdc7b9e0885bf06c0643de9c9e18a4d2425f","observation_id":"4d3ded41-75c3-4105-bc12-ce7343c7fb99","resolution":{"observed_at":"2026-08-15T16:56:45.636293Z","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":"2026-08-15T16:56:45.640266Z","title":"Deep MANTA: A coarse-to-fine many-task network for joint2D and 3D vehi- cle analysis from monocular image","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.640266Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:6e18cb28745accd9527c604186a3fa439b9a0f4d75eeb00dce012cbcadb94a57","observation_id":"24254ba2-ff0a-4b7a-b2bc-15113f52781e","resolution":{"observed_at":"2026-08-15T16:56:45.640266Z","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":"2026-08-15T16:56:45.647669Z","title":"Unified domain generalization and adaptation for multi-view3D 81 object detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.647669Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:04cfcd6c0f53b7a40aa0c572d937b9b8b904702c53da94e7867d50cab1e32473","observation_id":"591364f5-f662-4b19-95de-9e5c98fd15ac","resolution":{"observed_at":"2026-08-15T16:56:45.647669Z","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":"2026-08-15T16:56:45.652307Z","title":"CMDA: Cross-modal and domain adversarial adaptation for LiDAR-based3D object detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.652307Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:f1b00bd6613ecc158def874580302f87acbdece2814ad7de3d49dc4655887c5c","observation_id":"e7f2cbe4-2f46-41cb-9aa8-4191900911c6","resolution":{"observed_at":"2026-08-15T16:56:45.652307Z","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":"2026-08-15T16:56:45.656640Z","title":"RecurrentBEV: A long-term temporal fusion framework for multi-view3D detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.656640Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:33ae4e9133ecefe809d5ce83bd24212a3625fb115260cc53a2d42a3079d8c6a8","observation_id":"6f805356-5393-4cf4-8874-50868897851d","resolution":{"observed_at":"2026-08-15T16:56:45.656640Z","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":"2026-08-15T16:56:45.663035Z","title":"Viewpoint equivariance for multi-view3D object detection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.663035Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:ae92954af575d142dc8f4ef5cba3b1635016c07c09434e2ac92e3339e9fa258b","observation_id":"7e45ce87-f8d8-4a60-95b1-42656a52a39b","resolution":{"observed_at":"2026-08-15T16:56:45.663035Z","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":"2026-08-15T16:56:45.668359Z","title":"AP-Lossforaccurate one-stage object detection.TPAMI, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.668359Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:853eada62ecd4aef969cec4312e32010c465768b59e1b1849f2e3df5858bd8d6","observation_id":"ebacaf2b-39af-4a16-97b8-eccb0342324f","resolution":{"observed_at":"2026-08-15T16:56:45.668359Z","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":"2026-08-15T16:56:45.672162Z","title":"Monocular 3D object detection for autonomous driving","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.672162Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:0b7df847f02b0f8de166c5e152424560eb43fca9375fe5330403bfc32eebbfd8","observation_id":"ceecd3d2-2f44-4ab7-b32c-40e1169fdcda","resolution":{"observed_at":"2026-08-15T16:56:45.672162Z","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":"2026-08-15T16:56:45.676597Z","title":"InNeurIPS,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.676597Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:fea44699dc2ef2ae4af4a4fb4a3b84e4b6bc3033ba209f276aefdd93998b5dd4","observation_id":"5a1debf9-9565-4a16-8e87-914354d80801","resolution":{"observed_at":"2026-08-15T16:56:45.676597Z","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":"2026-08-15T16:56:45.680849Z","title":"Multi-view3D object detection network for autonomous driving","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.680849Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:d5ed793d16240a42abf076af9fc69ae31aafc5cc5258213302cdc9663605fc58","observation_id":"35cda1f6-6894-4a83-b98b-147012b83f43","resolution":{"observed_at":"2026-08-15T16:56:45.680849Z","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":"2026-08-15T16:56:45.684801Z","title":"DSGN: Deep stereo geometry network for 3D object detection","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.684801Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:8c8b51c00f5e49eafc9bd7d44bd5cec0952714039c130fff21815a1b18847ba4","observation_id":"719a152c-0ef6-4ceb-ade2-4fddb9a3c509","resolution":{"observed_at":"2026-08-15T16:56:45.684801Z","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":"2026-08-15T16:56:45.688625Z","title":"MonoPair: Monocular 3D object detection using pairwise spatial relationships","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.688625Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:e85040620d8b58219f1ed1a345dfd2636cf3d31387239866c8c6a498915f04a2","observation_id":"5b73a95a-3aa2-4a3c-a109-1a5a9933e2ee","resolution":{"observed_at":"2026-08-15T16:56:45.688625Z","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":"2026-08-15T16:56:45.692283Z","title":"Learning high-resolution vector representation from multi-camera images for 3D object detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.692283Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:55efccbafbf6c74235966425f78191a5a41e09f412ffc1c4c2ddaa8d9aa003ca","observation_id":"d32066cb-daa6-419f-8340-a6fd9ce1053b","resolution":{"observed_at":"2026-08-15T16:56:45.692283Z","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":"2026-08-15T16:56:45.697219Z","title":"NEAT: Neural attention fields for end-to-end autonomous driving","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.697219Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:95fac0e4ed15d7349ce5b2445c8f70fb7edc9f594455990ec3d2c5f86c6b8c96","observation_id":"c7f2229b-1ca5-45f4-8828-bbf71c7e51b7","resolution":{"observed_at":"2026-08-15T16:56:45.697219Z","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":"2026-08-15T16:56:45.701299Z","title":"Depth-discriminativemetriclearningfor monocular 3D object detection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.701299Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:6eb5e0f1f4aad10fa48a8eb813b5ae0a42be853b7b31b1b367da8c1c1f6e66ca","observation_id":"da2d4bc6-99ac-415d-9262-c369947f3d80","resolution":{"observed_at":"2026-08-15T16:56:45.701299Z","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":"2026-08-15T16:56:45.705468Z","title":"MonoDistill: Learningspatialfeaturesformonocular 3Dobjectdetection","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.705468Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:b6a95fddcadc693450b535e2f3eda24c48e12ade334e116c36cdb3b0bd639c82","observation_id":"641ee501-239c-4df1-833b-34a4da035a75","resolution":{"observed_at":"2026-08-15T16:56:45.705468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05711","last_updated":"2025-08-30T03:24:40Z","snapshot_observed_at":"2026-08-13T13:04:57.122536Z","submitted_at":"2023-01-13T06:02:31Z","title":"OA-DET3D: Embedding Object Awareness as a General Plug-in for Multi-Camera 3D Object Detection","version":3},"cited_work":{"arxiv_id":"2301.05711","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.05711","snapshot_observed_at":"2026-08-15T16:56:47.187167Z","title":"OA-DET3D: Embedding Object Awareness as a General Plug-in for Multi-Camera 3D Object Detection","venue":"cs.CV","work_id":"a680b35a-a76b-4c36-ad8d-7433f2cd9541","year":2023},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.709425Z"},"links":{"cited_paper":"/paper/2301.05711","citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:df0992dd96997e6f9d63b4d45cef8ef9020b3d6eab284842c428810e7fde9d2f","observation_id":"3086c173-60c1-46c4-a144-bdee9429abff","resolution":{"observed_at":"2026-08-15T16:56:47.195168Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T16:56:45.714337Z","title":"Spherical CNNs","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.714337Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:16379ba409a71d1c1025077d8347534f5e50c61b83ae651f56bf99b0c3ac1eed","observation_id":"a8d043f0-eeef-434f-b6c5-0bd2368a3061","resolution":{"observed_at":"2026-08-15T16:56:45.714337Z","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":"2026-08-15T16:56:45.718733Z","title":"Learning the irreducible representations of commutative lie groups","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.718733Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:9b38fcd52a355352761f54bc54932930d5ccb2b6b42fd9de127f0d4699299929","observation_id":"3fc2cd06-ebb5-426c-9a8e-40992c99a0fe","resolution":{"observed_at":"2026-08-15T16:56:45.718733Z","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":"2026-08-15T16:56:45.722976Z","title":"Group equivariant convolutional networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.722976Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:69ed710181cb65fa13c3863f97469242f1600df9c2ef496ed3eea1ec1c4074b2","observation_id":"905cd0fb-8cb5-4e46-b35d-80da319aed61","resolution":{"observed_at":"2026-08-15T16:56:45.722976Z","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":"2026-08-15T16:56:45.726933Z","title":"MMDetection3D: OpenMMLab next-generation platform forgeneral 3Dobjectdetection","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.726933Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:f4b06589a0f2099ca04a64fd4e40a90776209f7fbf24a634c6757748baa939fc","observation_id":"372ea970-0a5c-4d9d-ae12-fffd4f9ab963","resolution":{"observed_at":"2026-08-15T16:56:45.726933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.09796","last_updated":"2020-09-10T19:31:04Z","snapshot_observed_at":"2026-08-09T08:30:49.737024Z","submitted_at":"2020-09-10T19:31:04Z","title":"Multi-Task Learning with Deep Neural Networks: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.09796","snapshot_observed_at":"2026-08-15T16:56:45.731876Z","title":"Multi-tasklearningwithdeepneuralnetworks: Asurvey","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.731876Z"},"links":{"cited_paper":"/paper/2009.09796","citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:08478d32d6ea04391dd4183c916f0c28c0672aecfb5e8daf667243adf48f8bb7","observation_id":"97879b9e-ed55-4fb9-ba55-b767c193b876","resolution":{"observed_at":"2026-08-15T16:56:45.731876Z","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":"2026-08-15T16:56:45.736143Z","title":"Differentiableranksandsortingusing optimal transport","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.736143Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:9645281351b151edfedc53b30aa225fe5a0459012a4a895a52d798007bf1c865","observation_id":"9b21043b-3e36-41d0-ad9e-53ac20e8c579","resolution":{"observed_at":"2026-08-15T16:56:45.736143Z","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":"2026-08-15T16:56:45.741115Z","title":"Histograms of oriented gradients for human detection","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.741115Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:836d8918d08fbe6860ad016973794b5b311a54eb23e3d4759e63388a04d3784b","observation_id":"ff5dc338-7427-4509-8ed8-4799e3ffa1bb","resolution":{"observed_at":"2026-08-15T16:56:45.741115Z","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":"2026-08-15T16:56:45.745113Z","title":"ImageNet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.745113Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:5a363ff5f1811bdfd1b745680a7f086d8191bd6514f2ade457aec0b26c6014da","observation_id":"7f81a7af-f81a-47b5-aa24-afcdf5c832b9","resolution":{"observed_at":"2026-08-15T16:56:45.745113Z","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":"2026-08-15T16:56:45.748796Z","title":"Discriminative models for multi- class object layout.IJCV, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.748796Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:72800ab457d367aa0bb9bd07a698f4fa99529739f3c5cc1a5d49ad57aa5eb963","observation_id":"0cf2d74f-4810-4382-a924-a3e49e4dab54","resolution":{"observed_at":"2026-08-15T16:56:45.748796Z","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":"2026-08-15T16:56:45.754004Z","title":"Exploiting cyclic symmetry in convolutional neural networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.754004Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:ccaf403db0654b3728068df751b5ba937f0780e6fb82126b4351f2a269a55cc7","observation_id":"69057f43-8f57-4351-955e-ce0dbc0ac48d","resolution":{"observed_at":"2026-08-15T16:56:45.754004Z","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":"2026-08-15T16:56:45.757486Z","title":"Howdoneuralnetworksseedepthinsingleimages? In ICCV, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.757486Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:bfe03cc9350d762b553ffc6e1881646b0ca12e58347cccbd0c3795b0fdd75cae","observation_id":"7a923d11-5ace-4512-937c-95bbf9aa723e","resolution":{"observed_at":"2026-08-15T16:56:45.757486Z","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":"2026-08-15T16:56:45.761015Z","title":"Learning depth-guided convolutions for monocular3D object detection","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.761015Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:7824b4bb0d8094a2bb4fb4629941e00eada7fab10a3fda31f96dbcc53695fc72","observation_id":"22c7b739-4c01-4bce-8259-ef915e7c7bbc","resolution":{"observed_at":"2026-08-15T16:56:45.761015Z","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":"2026-08-15T16:56:45.765487Z","title":"SpatialDETR:Robustscalabletransformer-based 3Dobjectdetectionfrom multi-view camera images with global cross-sensor attention","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.765487Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:1b525bedba7c8a683a7df92650e06e9486d9465cdb23f14f528626c719b864c7","observation_id":"949cbf4c-4173-42df-b15b-d71411082eed","resolution":{"observed_at":"2026-08-15T16:56:45.765487Z","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":"2026-08-15T16:56:45.768788Z","title":"Benchmarking robustness of3D object detection to common corruptions","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.768788Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:9578bd2b6697bd1195dd3911e59c5b5bfb861c2b1eef526332f25fd984f9fbbc","observation_id":"63dfbb1c-d143-4d14-bac0-6622a3f4dea7","resolution":{"observed_at":"2026-08-15T16:56:45.768788Z","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":"2026-08-15T16:56:45.772171Z","title":"Animageisworth16x16words: Transformersforimage recognition at scale","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.772171Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:363308fc7805b75a892ca6d41a66d937223178a8c44adad222f07e194adc7e49","observation_id":"491a953a-1df0-4c3a-9c10-53387b6dd76e","resolution":{"observed_at":"2026-08-15T16:56:45.772171Z","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":"2026-08-15T16:56:45.775909Z","title":"Polar transformer networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.775909Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:bcde40d41f93b6e463b87ab465e43fad6db12dc08634aab0b7bf1b333a6438a8","observation_id":"30d63e81-6122-4994-935a-c70c0aca1a21","resolution":{"observed_at":"2026-08-15T16:56:45.775909Z","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":"2026-08-15T16:56:45.779672Z","title":"CAM-Convs: Camera-aware multi-scale convolutions for single-view depth","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.779672Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:a734c6df0d44958659e60310878b957f44313b4045cf1675c177bd06ff61df91","observation_id":"c10843fb-eb3e-4500-bd66-b988ec4ba005","resolution":{"observed_at":"2026-08-15T16:56:45.779672Z","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":"2026-08-15T16:56:45.785633Z","title":"Fully sparse3D object detection","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.785633Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:e7995a4bbf36511b06209a2cf87786ac8375e9b84af10482b76b6f82d26434e5","observation_id":"9a878927-a0bd-45b7-958d-98d42ca3ac02","resolution":{"observed_at":"2026-08-15T16:56:45.785633Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.12501","last_updated":"2023-04-04T09:34:04Z","snapshot_observed_at":"2026-08-13T13:37:11.361595Z","submitted_at":"2022-11-22T18:59:52Z","title":"AeDet: Azimuth-invariant Multi-view 3D Object Detection","version":3},"cited_work":{"arxiv_id":"2211.12501","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.12501","snapshot_observed_at":"2026-08-15T16:56:47.148748Z","title":"AeDet: Azimuth-invariant Multi-view 3D Object Detection","venue":"cs.CV","work_id":"9ce54354-43bf-4c9e-b4a9-43dbdd96cf2d","year":2022},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.789445Z"},"links":{"cited_paper":"/paper/2211.12501","citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:fa30f137185badda1a1b9c625a1669cf00b6f722eb3e2cad20cd1d50f7cf42a9","observation_id":"0da82e6f-6f61-4452-92aa-a077cb36949f","resolution":{"observed_at":"2026-08-15T16:56:47.153968Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T16:56:45.793348Z","title":"A tesla driver was killed after smashing into a firetruck on a california highway","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.793348Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:0e514fcc05535582f7fd203943cd8b11e2a38ebeac2a19b3a6d67dfebbe284c9","observation_id":"0997ac6c-e360-445d-ae31-629649817569","resolution":{"observed_at":"2026-08-15T16:56:45.793348Z","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":"2026-08-15T16:56:45.797209Z","title":"3D object detection and viewpoint estimation with a deformable3D cuboid model","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.797209Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:85db835b6657245b6340eb785b85521957855faccabd49936e04c8152e3af582","observation_id":"118aaf3e-03a2-4440-8c9b-392577dbdaef","resolution":{"observed_at":"2026-08-15T16:56:45.797209Z","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":"2026-08-15T16:56:45.800676Z","title":"The design and use of steerable filters.TPAMI,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.800676Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:f79029b00542af4154dadac4069efb9296e1b3e0bf7994e484359d19dd2bf851","observation_id":"22824d8e-6c40-4296-8044-ee046d445aec","resolution":{"observed_at":"2026-08-15T16:56:45.800676Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10044","last_updated":"2021-06-18T10:31:00Z","snapshot_observed_at":"2026-08-13T19:00:57.410286Z","submitted_at":"2021-06-18T10:31:00Z","title":"Training or Architecture? How to Incorporate Invariance in Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10044","snapshot_observed_at":"2026-08-15T16:56:45.804865Z","title":"Training or architecture? how to incorporate invariance in neural networks.arXiv preprint arXiv:2106.10044, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.804865Z"},"links":{"cited_paper":"/paper/2106.10044","citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:4c22f1269935220e1d7be494b4a446ab3973d9273810883fb52a559f297fc42f","observation_id":"93cd73fd-5fb6-4914-b2fd-819fef221054","resolution":{"observed_at":"2026-08-15T16:56:45.804865Z","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":"2026-08-15T16:56:45.808441Z","title":"Hyperbolic neural net- works","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.808441Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:387ce0849aca2aad547c4bec5cafb96de70669939a6c468782c88d353b7ad63c","observation_id":"b1b99301-d893-49ee-817c-ffc680d44934","resolution":{"observed_at":"2026-08-15T16:56:45.808441Z","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":"2026-08-15T16:56:45.811856Z","title":"3D-LaneNet: end-to-end 3D multiple lane detection","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.811856Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:38c085872a8086806e839307bd83c7d1e465b8cba7226b1305b22b2088e17d09","observation_id":"a19506ca-982d-45aa-b107-3b4783598c04","resolution":{"observed_at":"2026-08-15T16:56:45.811856Z","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":"2026-08-15T16:56:45.815389Z","title":"Vision meets robotics: The KITTI dataset.IJRR, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.815389Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:98386eaf2625bab72bd933d510e0841e44aba111c838411faf0011901aa82475","observation_id":"dae74b9c-40cf-433e-be4b-ee2ae0668de6","resolution":{"observed_at":"2026-08-15T16:56:45.815389Z","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":"2026-08-15T16:56:45.818952Z","title":"Are we ready for autonomous driving? the KITTI vision benchmark suite","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.818952Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:779b11ee933ff7ca57b71f808abf0dc002726f771eca1407fe0ed492a3fbab99","observation_id":"096ce334-55b3-459c-ab57-4b67895b77a3","resolution":{"observed_at":"2026-08-15T16:56:45.818952Z","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":"2026-08-15T16:56:45.822741Z","title":"Scalesteerablefiltersforlocallyscale-invariantconvolu- tional neural networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.822741Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:30c783386de01a734867f8d8e74cf0a6a85d09bd822f2b558b41af0f759bd553","observation_id":"cf937a2e-dcb1-45cc-a386-8048076f74c0","resolution":{"observed_at":"2026-08-15T16:56:45.822741Z","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":"2026-08-15T16:56:45.826407Z","title":"Fast R-CNN","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.826407Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:5c216c0cdc680baf206cc575bfc326dbd165bac96910a8e7cac48a696b3a177a","observation_id":"5973bac5-04f2-46a2-8741-0262ebc6fe45","resolution":{"observed_at":"2026-08-15T16:56:45.826407Z","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":"2026-08-15T16:56:45.830024Z","title":"Rich feature hierarchies for accurate object detection and semantic segmentation","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.830024Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:11adc8486a3733fd76f19a84fea2032eb034e9c125e5e8b35babd984290f7479","observation_id":"f17ee392-30b8-47d1-8485-dc49eefb9826","resolution":{"observed_at":"2026-08-15T16:56:45.830024Z","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":"2026-08-15T16:56:45.833265Z","title":"Matrix computations","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.833265Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:3a1142cd07a1f1678e329366a5ee751802a64e2c0593ffdb898394d7aba7e7a6","observation_id":"732e54b0-7328-423b-96f1-0bf0c472f8e0","resolution":{"observed_at":"2026-08-15T16:56:45.833265Z","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":"2026-08-15T16:56:45.836799Z","title":"Bird’s-eye-viewpanopticsegmentationusingmonocular frontal view images.RAL, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.836799Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:49dde2211b1f61a8a2031e129543ea7639d4e9725d070e790d38f5c4fb37d4c5","observation_id":"c058e2a0-ee5c-41cd-9233-036fa8dacdae","resolution":{"observed_at":"2026-08-15T16:56:45.836799Z","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":"2026-08-15T16:56:45.839978Z","title":"Gen-lanenet: A generalized and scalable approach for3D lane detection","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.839978Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:027ccb7f893b10b04d345a515e43e441a7f860da3e76cc8fa0d708880da4717f","observation_id":"2aba1ecd-6d3d-4c4f-b849-b00337f8e265","resolution":{"observed_at":"2026-08-15T16:56:45.839978Z","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":"2026-08-15T16:56:45.843164Z","title":"Simple- BEV: What really matters for multi-sensor BEV perception? InCoRL, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.843164Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:4161697485b596a76fe158ee94a14ead2cd0158f95f2fc52bcff9c4f3e53aa76","observation_id":"338394f6-c899-4c0f-afbd-4ebac863447b","resolution":{"observed_at":"2026-08-15T16:56:45.843164Z","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":"2026-08-15T16:56:45.846648Z","title":"A combined corner and edge detector","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.846648Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:a001c2a611fb23a2661902e0212ff29d2ed7ae12c9680676552336340f0edf91","observation_id":"432d29b3-97c5-4a42-a9f2-684104512074","resolution":{"observed_at":"2026-08-15T16:56:45.846648Z","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":"2026-08-15T16:56:45.849918Z","title":"Cam- bridge university press, 2003","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.849918Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:2a5dd75367a7ef96a4489c223bcbb95473dd380a276feea7999c13eecd255f59","observation_id":"fb116794-e8ca-4bf1-92a8-5e5a7121e48a","resolution":{"observed_at":"2026-08-15T16:56:45.849918Z","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":"2026-08-15T16:56:45.853809Z","title":"Deepresiduallearningforimage recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.853809Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:954d836791b3ed5268077253f81350df87739b052024cee59832e6cae350c84c","observation_id":"45142b55-45e9-401e-811e-fa34ada183de","resolution":{"observed_at":"2026-08-15T16:56:45.853809Z","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":"2026-08-15T16:56:45.857266Z","title":"End-to-end training of object class detectors for mean average precision","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.857266Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:dc2955b3b0db62ce4eadced5febb2e958f303a19ac2194148f966b33bb547a43","observation_id":"10e67bea-9fcb-4f51-8101-2b6ff984f317","resolution":{"observed_at":"2026-08-15T16:56:45.857266Z","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":"2026-08-15T16:56:45.861374Z","title":"Warped convolutions: Efficient invariance to spatial transformations","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.861374Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:21fe41b8fbfefc2e022f0aef03d38890f6a0d5aa407f4b9ea41d79206b242b6b","observation_id":"b876e363-2853-4ca6-8716-3c57715aaa9b","resolution":{"observed_at":"2026-08-15T16:56:45.861374Z","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":"2026-08-15T16:56:45.865423Z","title":"A convnet for non-maximum suppres- sion","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.865423Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:fd953bd8d5f372cb771ddf1f613a67fe318a3077bf5e996fea81c19f7a41f25d","observation_id":"65287b1e-fab1-42fe-80e0-b3cf2da7fd2b","resolution":{"observed_at":"2026-08-15T16:56:45.865423Z","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":"2026-08-15T16:56:45.869457Z","title":"Learning non-maximum suppression","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.869457Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:fa02a916fb653402e544065b74121e566fb4bd79adf536d78d5fcb780e97ca38","observation_id":"4ff339ca-c637-48dc-ae01-cb264a91f335","resolution":{"observed_at":"2026-08-15T16:56:45.869457Z","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":"2026-08-15T16:56:45.873022Z","title":"OPEN:Object-wisepositionembeddingformulti-view 3Dobjectdetection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.873022Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:525937b481027fe959b2ec20cba022479a98c09f13b45104a3b40a2c77b43c47","observation_id":"cf9697ae-b467-4656-a0d3-c8677ded8bed","resolution":{"observed_at":"2026-08-15T16:56:45.873022Z","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":"2026-08-15T16:56:45.877622Z","title":"FIERY: future instance prediction in bird’s-eye view from surround monocular cameras","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.877622Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:ffbc400ed59bc1b1e28a3c96736634ab73d963b0fd4ba10bb7ba44013aebc494","observation_id":"bf1f9cdd-1737-4bb0-8ada-b2f09dc26ec3","resolution":{"observed_at":"2026-08-15T16:56:45.877622Z","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":"2026-08-15T16:56:45.882838Z","title":"Investigating theimpactofmulti-LiDARplacementonobjectdetectionforautonomousdriving","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.882838Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:46454ba4d12808febc9496b2d045dd969edf7d2904afb4768e9bfcb8f62157a8","observation_id":"4f4d569c-1606-402e-a632-9cd98c1fca03","resolution":{"observed_at":"2026-08-15T16:56:45.882838Z","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":"2026-08-15T16:56:45.887410Z","title":"What you see is what you get: Exploiting visibility for3D object detection","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.887410Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:8ee32a981a121f65bf62b5c1d639248525ae72cdb8e4c6641443e0616cdd47a1","observation_id":"c987b4ff-a4ce-4248-a129-04b6b09d5017","resolution":{"observed_at":"2026-08-15T16:56:45.887410Z","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":"2026-08-15T16:56:45.891011Z","title":"Densely connected convolutional networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.891011Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:8025136a3dc7296c4232255fd5072008399db0e0de4915582ead11e37fa08f4b","observation_id":"46507a7f-b06c-4838-8385-a81cca6561a1","resolution":{"observed_at":"2026-08-15T16:56:45.891011Z","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":"2026-08-14T23:33:45.566779Z","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":"2026-08-15T16:56:45.894740Z","title":"BEVDet4D: Exploit temporal cues in multi-camera3D object detection.arXiv preprint arXiv:2203.17054, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.894740Z"},"links":{"cited_paper":"/paper/2203.17054","citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:d3267887d11c3fea86c5fff923f3a34aba8ac5670fb1de8a66129521ae81664d","observation_id":"76615bec-adc8-403f-974a-e6447d6958c1","resolution":{"observed_at":"2026-08-15T16:56:45.894740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.11790","last_updated":"2022-06-16T09:15:52Z","snapshot_observed_at":"2026-07-06T12:21:28.059661Z","submitted_at":"2021-12-22T10:48:06Z","title":"BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.11790","snapshot_observed_at":"2026-08-15T16:56:45.898373Z","title":"BEVDet: High-performance multi-camera 3D object detection in bird-eye-view","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.898373Z"},"links":{"cited_paper":"/paper/2112.11790","citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:8f7535912971882f963fe145f2483e4f2adfddf7b7e8106cb5aebed40722fa1d","observation_id":"e442f13b-143d-4692-8547-261bf2ffe443","resolution":{"observed_at":"2026-08-15T16:56:45.898373Z","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":"2026-08-15T16:56:45.901950Z","title":"InCVPR, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.901950Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:8e72ad26d298177da8de04b459aeec5e4494ab6e5d00bed2c73f6ecff00b252a","observation_id":"5af0e755-7f2c-49d3-90a8-c966086c08fb","resolution":{"observed_at":"2026-08-15T16:56:45.901950Z","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":"2026-08-15T16:56:45.905279Z","title":"EPNet: Enhancing point features with image semantics for3D object detection","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.905279Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:4729a556e2f7fa545f5bbc9cce7233732e89a66d571f05fea61ff35d6c841894","observation_id":"931123df-4a08-4c16-b118-9eab790ac36c","resolution":{"observed_at":"2026-08-15T16:56:45.905279Z","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":"2026-08-15T16:56:45.908937Z","title":"STXD:Structural andtemporalcross-modaldistillationformulti-view 3Dobjectdetection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.908937Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:a66bc458b70eefc473b876957099c4b258831adf0dcd689c8dc10f43510d60a6","observation_id":"1ff38759-d08c-40d6-a87c-0897fe51ad35","resolution":{"observed_at":"2026-08-15T16:56:45.908937Z","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":"2026-08-15T16:56:45.912408Z","title":"Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales.IJCV, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.912408Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:68f7dc903af7cc09816914f603c543e8aa66cc1fc7ce3f46d233a51221736190","observation_id":"e5c0501d-7922-42b5-b0eb-013d1b66fedd","resolution":{"observed_at":"2026-08-15T16:56:45.912408Z","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":"2026-08-15T16:56:45.916288Z","title":"Enhancing 3Dobjectdetectionwith 2D detection-guided query anchors","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.916288Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:56fe7c83739041eac9aef5acf7625de3397ea965003d7d5ef4c6bc0f14cb2cf6","observation_id":"12276854-acee-4b6f-9ef6-cb33a48e0074","resolution":{"observed_at":"2026-08-15T16:56:45.916288Z","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":"2026-08-15T16:56:45.919605Z","title":"MonoUNI:Aunifiedvehicleandinfrastructure-side monocular 3D object detection network with sufficient depth clues","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.919605Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:154900f1618775b08c4ed140b6ba546f39519851cea2abdabd24bc391d88007d","observation_id":"41d9af42-244c-473b-9cd8-1de4547bef9e","resolution":{"observed_at":"2026-08-15T16:56:45.919605Z","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":"2026-08-15T16:56:45.923850Z","title":"Polarformer: Multi-camera 3D object detection with polar transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.923850Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:048bb53eab7336b044fa72ed435f05f1fb8907ba67a6cd342be0aff5b9fa7d2f","observation_id":"daceddb3-9159-437f-8906-480dd33b230e","resolution":{"observed_at":"2026-08-15T16:56:45.923850Z","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":"2026-08-15T16:56:45.926976Z","title":"FSD-BEV:Foregroundself-distillationformulti-view 3Dobjectdetection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.926976Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:a21103ab27a446da4ee48bd63965f6fc5c3c8c47f4f5cc3d56c3bcaeaec21b0e","observation_id":"ab75cb65-7434-4c3a-b6ec-81e96ba92097","resolution":{"observed_at":"2026-08-15T16:56:45.926976Z","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":"2026-08-15T16:56:45.930245Z","title":"Physical symmetry enhanced neural networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.930245Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:3b0aa68927c60399aba1a74c4df0c82095661f2b21d6b1736f62f631af1443b9","observation_id":"3cee754c-933e-42fe-825f-8eead9bedca4","resolution":{"observed_at":"2026-08-15T16:56:45.930245Z","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":"2026-08-15T16:56:45.933752Z","title":"Locally scale-invariant convolu- tional neural networks","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.933752Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:efd2f707a75c24e8ec9686810a2b1d8b69172a97d650dbd74819bc13e3d0fee6","observation_id":"f1521cef-1759-4d50-a840-782ccf7154c6","resolution":{"observed_at":"2026-08-15T16:56:45.933752Z","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":"2026-08-15T16:56:45.937310Z","title":"Probabilistic anchor assignment with IoU prediction for object 86 detection","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.937310Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:803a6410f404f1b8e12a50d1cfa666cc66da8cb26ebec16ad40caa21c6ec147c","observation_id":"6ca68146-f531-4ca9-b3ad-7ee5c1ba99e9","resolution":{"observed_at":"2026-08-15T16:56:45.937310Z","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":"2026-08-15T16:56:45.940439Z","title":"La- belDistill: Label-guided cross-modal knowledge distillation for camera-based3D object detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-15T16:56:45.940439Z"},"links":{"citing_paper":"/paper/2508.19593"},"observation_digest":"sha256:8f89e979b3ef47d202e3cec6fd76d1b9359cc8962de58d241851c45db563731f","observation_id":"4b316b94-3d08-4d88-8256-6d9ce06fad10","resolution":{"observed_at":"2026-08-15T16:56:45.940439Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2508.19593","last_updated":"2025-08-27T06:06:18Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-15T16:47:50.421667Z","submitted_at":"2025-08-27T06:06:18Z","title":"Generalizing Monocular 3D Object Detection"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":98,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":300},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 100 of 300 outbound references and 0 inbound Pith citation observations for arXiv:2508.19593."}