{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EVNVWSVD3VEGABPPRJVCM2GEVV","short_pith_number":"pith:EVNVWSVD","schema_version":"1.0","canonical_sha256":"255b5b4aa3dd486005ef8a6a2668c4ad48d26931eeeeff0121009f9e393fe8b6","source":{"kind":"arxiv","id":"2405.10591","version":2},"attestation_state":"computed","paper":{"title":"GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chaojie Fan, Lizhuang Ma, Wenbin Wu, Xin Tan, Yong Peng, Yuan Xie, Zhiwei Zhang, Zhizhong Zhang","submitted_at":"2024-05-17T07:31:20Z","abstract_excerpt":"3D occupancy perception holds a pivotal role in recent vision-centric autonomous driving systems by converting surround-view images into integrated geometric and semantic representations within dense 3D grids. Nevertheless, current models still encounter two main challenges: modeling depth accurately in the 2D-3D view transformation stage, and overcoming the lack of generalizability issues due to sparse LiDAR supervision. To address these issues, this paper presents GEOcc, a Geometric-Enhanced Occupancy network tailored for vision-only surround-view perception. Our approach is three-fold: 1) I"},"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":"2405.10591","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-17T07:31:20Z","cross_cats_sorted":[],"title_canon_sha256":"25237d9f88f145531bb0cc679817673e247814680a967b0235d338b3f92bbea4","abstract_canon_sha256":"6c8082ef4dccebde4d6df772b689937d25b8c7a60f1808a54e36aae3b1138453"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:37:26.341686Z","signature_b64":"4D5Uz6vesZl7JqJlHXZQqewja0pixsDCRVvCW6G8kAa4R3UP8pnbNP/QH5QydWN7XIBNCWgNl5FyAHKeW6LSDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"255b5b4aa3dd486005ef8a6a2668c4ad48d26931eeeeff0121009f9e393fe8b6","last_reissued_at":"2026-07-05T10:37:26.341100Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:37:26.341100Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chaojie Fan, Lizhuang Ma, Wenbin Wu, Xin Tan, Yong Peng, Yuan Xie, Zhiwei Zhang, Zhizhong Zhang","submitted_at":"2024-05-17T07:31:20Z","abstract_excerpt":"3D occupancy perception holds a pivotal role in recent vision-centric autonomous driving systems by converting surround-view images into integrated geometric and semantic representations within dense 3D grids. Nevertheless, current models still encounter two main challenges: modeling depth accurately in the 2D-3D view transformation stage, and overcoming the lack of generalizability issues due to sparse LiDAR supervision. To address these issues, this paper presents GEOcc, a Geometric-Enhanced Occupancy network tailored for vision-only surround-view perception. Our approach is three-fold: 1) I"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.10591","kind":"arxiv","version":2},"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/2405.10591/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":"2405.10591","created_at":"2026-07-05T10:37:26.341167+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.10591v2","created_at":"2026-07-05T10:37:26.341167+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.10591","created_at":"2026-07-05T10:37:26.341167+00:00"},{"alias_kind":"pith_short_12","alias_value":"EVNVWSVD3VEG","created_at":"2026-07-05T10:37:26.341167+00:00"},{"alias_kind":"pith_short_16","alias_value":"EVNVWSVD3VEGABPP","created_at":"2026-07-05T10:37:26.341167+00:00"},{"alias_kind":"pith_short_8","alias_value":"EVNVWSVD","created_at":"2026-07-05T10:37:26.341167+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.02250","citing_title":"FMOcc: TPV-Driven Flow Matching for 3D Occupancy Prediction with Selective State Space Model","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EVNVWSVD3VEGABPPRJVCM2GEVV","json":"https://pith.science/pith/EVNVWSVD3VEGABPPRJVCM2GEVV.json","graph_json":"https://pith.science/api/pith-number/EVNVWSVD3VEGABPPRJVCM2GEVV/graph.json","events_json":"https://pith.science/api/pith-number/EVNVWSVD3VEGABPPRJVCM2GEVV/events.json","paper":"https://pith.science/paper/EVNVWSVD"},"agent_actions":{"view_html":"https://pith.science/pith/EVNVWSVD3VEGABPPRJVCM2GEVV","download_json":"https://pith.science/pith/EVNVWSVD3VEGABPPRJVCM2GEVV.json","view_paper":"https://pith.science/paper/EVNVWSVD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.10591&json=true","fetch_graph":"https://pith.science/api/pith-number/EVNVWSVD3VEGABPPRJVCM2GEVV/graph.json","fetch_events":"https://pith.science/api/pith-number/EVNVWSVD3VEGABPPRJVCM2GEVV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EVNVWSVD3VEGABPPRJVCM2GEVV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EVNVWSVD3VEGABPPRJVCM2GEVV/action/storage_attestation","attest_author":"https://pith.science/pith/EVNVWSVD3VEGABPPRJVCM2GEVV/action/author_attestation","sign_citation":"https://pith.science/pith/EVNVWSVD3VEGABPPRJVCM2GEVV/action/citation_signature","submit_replication":"https://pith.science/pith/EVNVWSVD3VEGABPPRJVCM2GEVV/action/replication_record"}},"created_at":"2026-07-05T10:37:26.341167+00:00","updated_at":"2026-07-05T10:37:26.341167+00:00"}