{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SWLF2Z2ESE5CXWWNMSWWS2S2B2","short_pith_number":"pith:SWLF2Z2E","schema_version":"1.0","canonical_sha256":"95965d6744913a2bdacd64ad696a5a0ea504bde042eb7d24330648b137086399","source":{"kind":"arxiv","id":"2401.13785","version":3},"attestation_state":"computed","paper":{"title":"A Spatiotemporal Approach to Tri-Perspective Representation for 3D Semantic Occupancy Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gihan Jayatilaka, Muhammad Haris Khan, Roshan Ragel, Sathira Silva, Savindu Bhashitha Wannigama","submitted_at":"2024-01-24T20:06:59Z","abstract_excerpt":"Holistic understanding and reasoning in 3D scenes are crucial for the success of autonomous driving systems. The evolution of 3D semantic occupancy prediction as a pretraining task for autonomous driving and robotic applications captures finer 3D details compared to traditional 3D detection methods. Vision-based 3D semantic occupancy prediction is increasingly overlooked in favor of LiDAR-based approaches, which have shown superior performance in recent years. However, we present compelling evidence that there is still potential for enhancing vision-based methods. Existing approaches predomina"},"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":"2401.13785","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-01-24T20:06:59Z","cross_cats_sorted":[],"title_canon_sha256":"60738e7bbe2b19120f0a45ef01897406193fa4fe9ae6fd3d5d67754038215b4d","abstract_canon_sha256":"7f3afc2d8e61310279ad94600d6b048d9db7f5694a3188c2c1781eed037bf3ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:14:37.906005Z","signature_b64":"WKhxiDMHk7l3h8B69V2zeqMWkz2Df8KQfUKElSCErRPQ7pQ00M9X1PUbTkdtd6Y6POyOiQyu8Xxwu4PZ8qguAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"95965d6744913a2bdacd64ad696a5a0ea504bde042eb7d24330648b137086399","last_reissued_at":"2026-07-05T10:14:37.905546Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:14:37.905546Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Spatiotemporal Approach to Tri-Perspective Representation for 3D Semantic Occupancy Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gihan Jayatilaka, Muhammad Haris Khan, Roshan Ragel, Sathira Silva, Savindu Bhashitha Wannigama","submitted_at":"2024-01-24T20:06:59Z","abstract_excerpt":"Holistic understanding and reasoning in 3D scenes are crucial for the success of autonomous driving systems. The evolution of 3D semantic occupancy prediction as a pretraining task for autonomous driving and robotic applications captures finer 3D details compared to traditional 3D detection methods. Vision-based 3D semantic occupancy prediction is increasingly overlooked in favor of LiDAR-based approaches, which have shown superior performance in recent years. However, we present compelling evidence that there is still potential for enhancing vision-based methods. Existing approaches predomina"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.13785","kind":"arxiv","version":3},"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/2401.13785/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":"2401.13785","created_at":"2026-07-05T10:14:37.905602+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.13785v3","created_at":"2026-07-05T10:14:37.905602+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.13785","created_at":"2026-07-05T10:14:37.905602+00:00"},{"alias_kind":"pith_short_12","alias_value":"SWLF2Z2ESE5C","created_at":"2026-07-05T10:14:37.905602+00:00"},{"alias_kind":"pith_short_16","alias_value":"SWLF2Z2ESE5CXWWN","created_at":"2026-07-05T10:14:37.905602+00:00"},{"alias_kind":"pith_short_8","alias_value":"SWLF2Z2E","created_at":"2026-07-05T10:14:37.905602+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.15384","citing_title":"MetaOcc: Spatio-Temporal Fusion of Surround-View 4D Radar and Camera for 3D Occupancy Prediction with Dual Training Strategies","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SWLF2Z2ESE5CXWWNMSWWS2S2B2","json":"https://pith.science/pith/SWLF2Z2ESE5CXWWNMSWWS2S2B2.json","graph_json":"https://pith.science/api/pith-number/SWLF2Z2ESE5CXWWNMSWWS2S2B2/graph.json","events_json":"https://pith.science/api/pith-number/SWLF2Z2ESE5CXWWNMSWWS2S2B2/events.json","paper":"https://pith.science/paper/SWLF2Z2E"},"agent_actions":{"view_html":"https://pith.science/pith/SWLF2Z2ESE5CXWWNMSWWS2S2B2","download_json":"https://pith.science/pith/SWLF2Z2ESE5CXWWNMSWWS2S2B2.json","view_paper":"https://pith.science/paper/SWLF2Z2E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.13785&json=true","fetch_graph":"https://pith.science/api/pith-number/SWLF2Z2ESE5CXWWNMSWWS2S2B2/graph.json","fetch_events":"https://pith.science/api/pith-number/SWLF2Z2ESE5CXWWNMSWWS2S2B2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SWLF2Z2ESE5CXWWNMSWWS2S2B2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SWLF2Z2ESE5CXWWNMSWWS2S2B2/action/storage_attestation","attest_author":"https://pith.science/pith/SWLF2Z2ESE5CXWWNMSWWS2S2B2/action/author_attestation","sign_citation":"https://pith.science/pith/SWLF2Z2ESE5CXWWNMSWWS2S2B2/action/citation_signature","submit_replication":"https://pith.science/pith/SWLF2Z2ESE5CXWWNMSWWS2S2B2/action/replication_record"}},"created_at":"2026-07-05T10:14:37.905602+00:00","updated_at":"2026-07-05T10:14:37.905602+00:00"}