{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XWHHTOVAD4AGXTGHA5I5LKNNDE","short_pith_number":"pith:XWHHTOVA","schema_version":"1.0","canonical_sha256":"bd8e79baa01f006bccc70751d5a9ad192d509dcf21e0e775b9b08e0ebbd4dc3d","source":{"kind":"arxiv","id":"2506.10977","version":1},"attestation_state":"computed","paper":{"title":"QuadricFormer: Scene as Superquadrics for 3D Semantic Occupancy Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiwen Lu, Longchao Yang, Sicheng Zuo, Wenzhao Zheng, Xiaoyong Han, Yong Pan","submitted_at":"2025-06-12T17:59:45Z","abstract_excerpt":"3D occupancy prediction is crucial for robust autonomous driving systems as it enables comprehensive perception of environmental structures and semantics. Most existing methods employ dense voxel-based scene representations, ignoring the sparsity of driving scenes and resulting in inefficiency. Recent works explore object-centric representations based on sparse Gaussians, but their ellipsoidal shape prior limits the modeling of diverse structures. In real-world driving scenes, objects exhibit rich geometries (e.g., cuboids, cylinders, and irregular shapes), necessitating excessive ellipsoidal "},"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":"2506.10977","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-12T17:59:45Z","cross_cats_sorted":[],"title_canon_sha256":"acf6848e17a7fb5b31f8f3b76fb633e944004ea78013e6cb5a001c93e1600d29","abstract_canon_sha256":"6664fd5b9926657448c15ed2b3ecfb9983caf6f70ef84be3faef6e14433543bc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:20:41.417723Z","signature_b64":"vzdarBu1BnH5BM9mjYp2cbi1GRrA3HjLtX0FZovAYHJ9JGlkNOpWaaTPQQFDtVzo0BMTJbvZvFbWuaxnflvEBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd8e79baa01f006bccc70751d5a9ad192d509dcf21e0e775b9b08e0ebbd4dc3d","last_reissued_at":"2026-07-05T11:20:41.417183Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:20:41.417183Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"QuadricFormer: Scene as Superquadrics for 3D Semantic Occupancy Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiwen Lu, Longchao Yang, Sicheng Zuo, Wenzhao Zheng, Xiaoyong Han, Yong Pan","submitted_at":"2025-06-12T17:59:45Z","abstract_excerpt":"3D occupancy prediction is crucial for robust autonomous driving systems as it enables comprehensive perception of environmental structures and semantics. Most existing methods employ dense voxel-based scene representations, ignoring the sparsity of driving scenes and resulting in inefficiency. Recent works explore object-centric representations based on sparse Gaussians, but their ellipsoidal shape prior limits the modeling of diverse structures. In real-world driving scenes, objects exhibit rich geometries (e.g., cuboids, cylinders, and irregular shapes), necessitating excessive ellipsoidal "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.10977","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/2506.10977/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":"2506.10977","created_at":"2026-07-05T11:20:41.417242+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.10977v1","created_at":"2026-07-05T11:20:41.417242+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.10977","created_at":"2026-07-05T11:20:41.417242+00:00"},{"alias_kind":"pith_short_12","alias_value":"XWHHTOVAD4AG","created_at":"2026-07-05T11:20:41.417242+00:00"},{"alias_kind":"pith_short_16","alias_value":"XWHHTOVAD4AGXTGH","created_at":"2026-07-05T11:20:41.417242+00:00"},{"alias_kind":"pith_short_8","alias_value":"XWHHTOVA","created_at":"2026-07-05T11:20:41.417242+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.13460","citing_title":"VISA: VLM-Guided Instance Semantic Auditing for 3D Occupancy World Models","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08957","citing_title":"Rethinking 3D Shape Generation: Diffusion over Superquadrics","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2602.06400","citing_title":"TFusionOcc: T-Primitive Based Object-Centric Multi-Sensor Fusion Framework for 3D Occupancy Prediction","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2604.00813","citing_title":"DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scale","ref_index":94,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XWHHTOVAD4AGXTGHA5I5LKNNDE","json":"https://pith.science/pith/XWHHTOVAD4AGXTGHA5I5LKNNDE.json","graph_json":"https://pith.science/api/pith-number/XWHHTOVAD4AGXTGHA5I5LKNNDE/graph.json","events_json":"https://pith.science/api/pith-number/XWHHTOVAD4AGXTGHA5I5LKNNDE/events.json","paper":"https://pith.science/paper/XWHHTOVA"},"agent_actions":{"view_html":"https://pith.science/pith/XWHHTOVAD4AGXTGHA5I5LKNNDE","download_json":"https://pith.science/pith/XWHHTOVAD4AGXTGHA5I5LKNNDE.json","view_paper":"https://pith.science/paper/XWHHTOVA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.10977&json=true","fetch_graph":"https://pith.science/api/pith-number/XWHHTOVAD4AGXTGHA5I5LKNNDE/graph.json","fetch_events":"https://pith.science/api/pith-number/XWHHTOVAD4AGXTGHA5I5LKNNDE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XWHHTOVAD4AGXTGHA5I5LKNNDE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XWHHTOVAD4AGXTGHA5I5LKNNDE/action/storage_attestation","attest_author":"https://pith.science/pith/XWHHTOVAD4AGXTGHA5I5LKNNDE/action/author_attestation","sign_citation":"https://pith.science/pith/XWHHTOVAD4AGXTGHA5I5LKNNDE/action/citation_signature","submit_replication":"https://pith.science/pith/XWHHTOVAD4AGXTGHA5I5LKNNDE/action/replication_record"}},"created_at":"2026-07-05T11:20:41.417242+00:00","updated_at":"2026-07-05T11:20:41.417242+00:00"}