{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AEKA4QFV7IWL7G6GGR2PZNSVJQ","short_pith_number":"pith:AEKA4QFV","schema_version":"1.0","canonical_sha256":"01140e40b5fa2cbf9bc63474fcb6554c2318f3d99b4c6c49b994bdbc5d84cc6d","source":{"kind":"arxiv","id":"2502.15180","version":1},"attestation_state":"computed","paper":{"title":"OccProphet: Pushing Efficiency Frontier of Camera-Only 4D Occupancy Forecasting with Observer-Forecaster-Refiner Framework","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Huaiyuan Xu, Junliang Chen, Lap-Pui Chau, Yi Wang","submitted_at":"2025-02-21T03:21:48Z","abstract_excerpt":"Predicting variations in complex traffic environments is crucial for the safety of autonomous driving. Recent advancements in occupancy forecasting have enabled forecasting future 3D occupied status in driving environments by observing historical 2D images. However, high computational demands make occupancy forecasting less efficient during training and inference stages, hindering its feasibility for deployment on edge agents. In this paper, we propose a novel framework, i.e., OccProphet, to efficiently and effectively learn occupancy forecasting with significantly lower computational requirem"},"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":"2502.15180","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-21T03:21:48Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"6aeb6a1eecf6f1f7e184ed4a7360cb31df3b2b47a0d50e624768e958a542ecb4","abstract_canon_sha256":"fc43ec387eaea9de94e9b848cf91c34bcb7265397bbe4d44af093b25de55ff85"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:17:57.417115Z","signature_b64":"oIrueOo5FyzDQ+XqNLvRlmAHe5r/QrFyfNwpkEbPMhu5MubgHIIAhcSdlrz2pYQlIuO92/U0GHcwH7gu2rfaCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01140e40b5fa2cbf9bc63474fcb6554c2318f3d99b4c6c49b994bdbc5d84cc6d","last_reissued_at":"2026-07-05T10:17:57.416627Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:17:57.416627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OccProphet: Pushing Efficiency Frontier of Camera-Only 4D Occupancy Forecasting with Observer-Forecaster-Refiner Framework","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Huaiyuan Xu, Junliang Chen, Lap-Pui Chau, Yi Wang","submitted_at":"2025-02-21T03:21:48Z","abstract_excerpt":"Predicting variations in complex traffic environments is crucial for the safety of autonomous driving. Recent advancements in occupancy forecasting have enabled forecasting future 3D occupied status in driving environments by observing historical 2D images. However, high computational demands make occupancy forecasting less efficient during training and inference stages, hindering its feasibility for deployment on edge agents. In this paper, we propose a novel framework, i.e., OccProphet, to efficiently and effectively learn occupancy forecasting with significantly lower computational requirem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.15180","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/2502.15180/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":"2502.15180","created_at":"2026-07-05T10:17:57.416690+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.15180v1","created_at":"2026-07-05T10:17:57.416690+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.15180","created_at":"2026-07-05T10:17:57.416690+00:00"},{"alias_kind":"pith_short_12","alias_value":"AEKA4QFV7IWL","created_at":"2026-07-05T10:17:57.416690+00:00"},{"alias_kind":"pith_short_16","alias_value":"AEKA4QFV7IWL7G6G","created_at":"2026-07-05T10:17:57.416690+00:00"},{"alias_kind":"pith_short_8","alias_value":"AEKA4QFV","created_at":"2026-07-05T10:17:57.416690+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.30421","citing_title":"OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2511.22039","citing_title":"SparseWorld-TC: Trajectory-Conditioned Sparse Occupancy World Model","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AEKA4QFV7IWL7G6GGR2PZNSVJQ","json":"https://pith.science/pith/AEKA4QFV7IWL7G6GGR2PZNSVJQ.json","graph_json":"https://pith.science/api/pith-number/AEKA4QFV7IWL7G6GGR2PZNSVJQ/graph.json","events_json":"https://pith.science/api/pith-number/AEKA4QFV7IWL7G6GGR2PZNSVJQ/events.json","paper":"https://pith.science/paper/AEKA4QFV"},"agent_actions":{"view_html":"https://pith.science/pith/AEKA4QFV7IWL7G6GGR2PZNSVJQ","download_json":"https://pith.science/pith/AEKA4QFV7IWL7G6GGR2PZNSVJQ.json","view_paper":"https://pith.science/paper/AEKA4QFV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.15180&json=true","fetch_graph":"https://pith.science/api/pith-number/AEKA4QFV7IWL7G6GGR2PZNSVJQ/graph.json","fetch_events":"https://pith.science/api/pith-number/AEKA4QFV7IWL7G6GGR2PZNSVJQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AEKA4QFV7IWL7G6GGR2PZNSVJQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AEKA4QFV7IWL7G6GGR2PZNSVJQ/action/storage_attestation","attest_author":"https://pith.science/pith/AEKA4QFV7IWL7G6GGR2PZNSVJQ/action/author_attestation","sign_citation":"https://pith.science/pith/AEKA4QFV7IWL7G6GGR2PZNSVJQ/action/citation_signature","submit_replication":"https://pith.science/pith/AEKA4QFV7IWL7G6GGR2PZNSVJQ/action/replication_record"}},"created_at":"2026-07-05T10:17:57.416690+00:00","updated_at":"2026-07-05T10:17:57.416690+00:00"}