{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GYQVQ6LSV3KTTSVUOE4GN7WVYK","short_pith_number":"pith:GYQVQ6LS","schema_version":"1.0","canonical_sha256":"3621587972aed539cab4713866fed5c29b265d291792545f54aff98af9d8efde","source":{"kind":"arxiv","id":"2412.20911","version":1},"attestation_state":"computed","paper":{"title":"TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fang Li, Peixiang Huang, Shaoqing Xu, Zhi-Xin Yang, Ziying Song","submitted_at":"2024-12-30T12:44:20Z","abstract_excerpt":"Accurate multi-view 3D object detection is essential for applications such as autonomous driving. Researchers have consistently aimed to leverage LiDAR's precise spatial information to enhance camera-based detectors through methods like depth supervision and bird-eye-view (BEV) feature distillation. However, existing approaches often face challenges due to the inherent differences between LiDAR and camera data representations. In this paper, we introduce the TiGDistill-BEV, a novel approach that effectively bridges this gap by leveraging the strengths of both sensors. Our method distills knowl"},"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":"2412.20911","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-30T12:44:20Z","cross_cats_sorted":[],"title_canon_sha256":"2e767e190890b93e7ff48a1eeff48ac11946b42135400b144a1be94e0c75b6a8","abstract_canon_sha256":"7afe0b3dbe1240da51834425bb2c6f3f3a6277963b473777fbe3eead76d88b4e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:24.544363Z","signature_b64":"Dyixo8Od8zRbdPPeipqVjtaY2oKtfZYBrxgGJwI01Fzyx80ZEyw0B5igggGBjofGWooUOUyKxNzFZCYyGuRqBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3621587972aed539cab4713866fed5c29b265d291792545f54aff98af9d8efde","last_reissued_at":"2026-07-05T09:55:24.543790Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:24.543790Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fang Li, Peixiang Huang, Shaoqing Xu, Zhi-Xin Yang, Ziying Song","submitted_at":"2024-12-30T12:44:20Z","abstract_excerpt":"Accurate multi-view 3D object detection is essential for applications such as autonomous driving. Researchers have consistently aimed to leverage LiDAR's precise spatial information to enhance camera-based detectors through methods like depth supervision and bird-eye-view (BEV) feature distillation. However, existing approaches often face challenges due to the inherent differences between LiDAR and camera data representations. In this paper, we introduce the TiGDistill-BEV, a novel approach that effectively bridges this gap by leveraging the strengths of both sensors. Our method distills knowl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.20911","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/2412.20911/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":"2412.20911","created_at":"2026-07-05T09:55:24.543852+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.20911v1","created_at":"2026-07-05T09:55:24.543852+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.20911","created_at":"2026-07-05T09:55:24.543852+00:00"},{"alias_kind":"pith_short_12","alias_value":"GYQVQ6LSV3KT","created_at":"2026-07-05T09:55:24.543852+00:00"},{"alias_kind":"pith_short_16","alias_value":"GYQVQ6LSV3KTTSVU","created_at":"2026-07-05T09:55:24.543852+00:00"},{"alias_kind":"pith_short_8","alias_value":"GYQVQ6LS","created_at":"2026-07-05T09:55:24.543852+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.17024","citing_title":"CAM3DNet: Comprehensively mining the multi-scale features for 3D Object Detection with Multi-View Cameras","ref_index":44,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GYQVQ6LSV3KTTSVUOE4GN7WVYK","json":"https://pith.science/pith/GYQVQ6LSV3KTTSVUOE4GN7WVYK.json","graph_json":"https://pith.science/api/pith-number/GYQVQ6LSV3KTTSVUOE4GN7WVYK/graph.json","events_json":"https://pith.science/api/pith-number/GYQVQ6LSV3KTTSVUOE4GN7WVYK/events.json","paper":"https://pith.science/paper/GYQVQ6LS"},"agent_actions":{"view_html":"https://pith.science/pith/GYQVQ6LSV3KTTSVUOE4GN7WVYK","download_json":"https://pith.science/pith/GYQVQ6LSV3KTTSVUOE4GN7WVYK.json","view_paper":"https://pith.science/paper/GYQVQ6LS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.20911&json=true","fetch_graph":"https://pith.science/api/pith-number/GYQVQ6LSV3KTTSVUOE4GN7WVYK/graph.json","fetch_events":"https://pith.science/api/pith-number/GYQVQ6LSV3KTTSVUOE4GN7WVYK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GYQVQ6LSV3KTTSVUOE4GN7WVYK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GYQVQ6LSV3KTTSVUOE4GN7WVYK/action/storage_attestation","attest_author":"https://pith.science/pith/GYQVQ6LSV3KTTSVUOE4GN7WVYK/action/author_attestation","sign_citation":"https://pith.science/pith/GYQVQ6LSV3KTTSVUOE4GN7WVYK/action/citation_signature","submit_replication":"https://pith.science/pith/GYQVQ6LSV3KTTSVUOE4GN7WVYK/action/replication_record"}},"created_at":"2026-07-05T09:55:24.543852+00:00","updated_at":"2026-07-05T09:55:24.543852+00:00"}