{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:R45J4AVIC6FVNKIE7WKZEDAMXR","short_pith_number":"pith:R45J4AVI","schema_version":"1.0","canonical_sha256":"8f3a9e02a8178b56a904fd95920c0cbc42062693a80b0517eda5751c41669eba","source":{"kind":"arxiv","id":"2107.08862","version":1},"attestation_state":"computed","paper":{"title":"Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Cameras","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jing Song, Jizheng Wang, Man Wang, Muqing Fang, Wenkai Zhang, Xinchao Gou, Yuanzhu Gan, ZiZhang Wu","submitted_at":"2021-07-19T13:24:21Z","abstract_excerpt":"The 3D visual perception for vehicles with the surround-view fisheye camera system is a critical and challenging task for low-cost urban autonomous driving. While existing monocular 3D object detection methods perform not well enough on the fisheye images for mass production, partly due to the lack of 3D datasets of such images. In this paper, we manage to overcome and avoid the difficulty of acquiring the large scale of accurate 3D labeled truth data, by breaking down the 3D object detection task into some sub-tasks, such as vehicle's contact point detection, type classification, re-identific"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2107.08862","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-07-19T13:24:21Z","cross_cats_sorted":[],"title_canon_sha256":"7ccd3f21450eb23a827db1cd050e9edc85c80c87861e3a734d8faa497d81219e","abstract_canon_sha256":"4809e336132d27d367a64ee0de7a78cee42f7cd1d12c8d05b2ebcdbc8568c89b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:58:53.765524Z","signature_b64":"akuA2Ql71uutf1YiXSC8xt1nC1FTv1DXNAoigPZBLe006TI2hYQYymyVbSmR8K27fwT4ZXTzAWBYpkOOKIarAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f3a9e02a8178b56a904fd95920c0cbc42062693a80b0517eda5751c41669eba","last_reissued_at":"2026-07-05T02:58:53.765114Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:58:53.765114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Cameras","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jing Song, Jizheng Wang, Man Wang, Muqing Fang, Wenkai Zhang, Xinchao Gou, Yuanzhu Gan, ZiZhang Wu","submitted_at":"2021-07-19T13:24:21Z","abstract_excerpt":"The 3D visual perception for vehicles with the surround-view fisheye camera system is a critical and challenging task for low-cost urban autonomous driving. While existing monocular 3D object detection methods perform not well enough on the fisheye images for mass production, partly due to the lack of 3D datasets of such images. In this paper, we manage to overcome and avoid the difficulty of acquiring the large scale of accurate 3D labeled truth data, by breaking down the 3D object detection task into some sub-tasks, such as vehicle's contact point detection, type classification, re-identific"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.08862","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2107.08862/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2107.08862","created_at":"2026-07-05T02:58:53.765170+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.08862v1","created_at":"2026-07-05T02:58:53.765170+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.08862","created_at":"2026-07-05T02:58:53.765170+00:00"},{"alias_kind":"pith_short_12","alias_value":"R45J4AVIC6FV","created_at":"2026-07-05T02:58:53.765170+00:00"},{"alias_kind":"pith_short_16","alias_value":"R45J4AVIC6FVNKIE","created_at":"2026-07-05T02:58:53.765170+00:00"},{"alias_kind":"pith_short_8","alias_value":"R45J4AVI","created_at":"2026-07-05T02:58:53.765170+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R45J4AVIC6FVNKIE7WKZEDAMXR","json":"https://pith.science/pith/R45J4AVIC6FVNKIE7WKZEDAMXR.json","graph_json":"https://pith.science/api/pith-number/R45J4AVIC6FVNKIE7WKZEDAMXR/graph.json","events_json":"https://pith.science/api/pith-number/R45J4AVIC6FVNKIE7WKZEDAMXR/events.json","paper":"https://pith.science/paper/R45J4AVI"},"agent_actions":{"view_html":"https://pith.science/pith/R45J4AVIC6FVNKIE7WKZEDAMXR","download_json":"https://pith.science/pith/R45J4AVIC6FVNKIE7WKZEDAMXR.json","view_paper":"https://pith.science/paper/R45J4AVI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.08862&json=true","fetch_graph":"https://pith.science/api/pith-number/R45J4AVIC6FVNKIE7WKZEDAMXR/graph.json","fetch_events":"https://pith.science/api/pith-number/R45J4AVIC6FVNKIE7WKZEDAMXR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R45J4AVIC6FVNKIE7WKZEDAMXR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R45J4AVIC6FVNKIE7WKZEDAMXR/action/storage_attestation","attest_author":"https://pith.science/pith/R45J4AVIC6FVNKIE7WKZEDAMXR/action/author_attestation","sign_citation":"https://pith.science/pith/R45J4AVIC6FVNKIE7WKZEDAMXR/action/citation_signature","submit_replication":"https://pith.science/pith/R45J4AVIC6FVNKIE7WKZEDAMXR/action/replication_record"}},"created_at":"2026-07-05T02:58:53.765170+00:00","updated_at":"2026-07-05T02:58:53.765170+00:00"}