{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MYK7W7GN2OBGXMXMRMKI2F3MRX","short_pith_number":"pith:MYK7W7GN","schema_version":"1.0","canonical_sha256":"6615fb7ccdd3826bb2ec8b148d176c8dde83cc3c860aab1df6cf3d147db79f80","source":{"kind":"arxiv","id":"2409.10681","version":1},"attestation_state":"computed","paper":{"title":"Online Diffusion-Based 3D Occupancy Prediction at the Frontier with Probabilistic Map Reconciliation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Alec Reed, Bradley Hayes, Brendan Crowe, Christoffer Heckman, Lorin Achey","submitted_at":"2024-09-16T19:24:00Z","abstract_excerpt":"Autonomous navigation and exploration in unmapped environments remains a significant challenge in robotics due to the difficulty robots face in making commonsense inference of unobserved geometries. Recent advancements have demonstrated that generative modeling techniques, particularly diffusion models, can enable systems to infer these geometries from partial observation. In this work, we present implementation details and results for real-time, online occupancy prediction using a modified diffusion model. By removing attention-based visual conditioning and visual feature extraction component"},"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":"2409.10681","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-09-16T19:24:00Z","cross_cats_sorted":[],"title_canon_sha256":"7fcc461bb4a57af62435ca555fe452c193d138a91095b69f37206b9109ef0145","abstract_canon_sha256":"1102e5623fb25c865619569e60b05989a92287593f3e1dbc215c26074684fab7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:07:54.621172Z","signature_b64":"G79yM9xyTKpGki6b83cirPUHfLx2GKZAGu+awnsBa919bLNSC1R9b7yEmeqYmYrj50XPg6t86uAvENOg7As7BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6615fb7ccdd3826bb2ec8b148d176c8dde83cc3c860aab1df6cf3d147db79f80","last_reissued_at":"2026-07-05T09:07:54.620735Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:07:54.620735Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Online Diffusion-Based 3D Occupancy Prediction at the Frontier with Probabilistic Map Reconciliation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Alec Reed, Bradley Hayes, Brendan Crowe, Christoffer Heckman, Lorin Achey","submitted_at":"2024-09-16T19:24:00Z","abstract_excerpt":"Autonomous navigation and exploration in unmapped environments remains a significant challenge in robotics due to the difficulty robots face in making commonsense inference of unobserved geometries. Recent advancements have demonstrated that generative modeling techniques, particularly diffusion models, can enable systems to infer these geometries from partial observation. In this work, we present implementation details and results for real-time, online occupancy prediction using a modified diffusion model. By removing attention-based visual conditioning and visual feature extraction component"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.10681","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/2409.10681/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":"2409.10681","created_at":"2026-07-05T09:07:54.620796+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.10681v1","created_at":"2026-07-05T09:07:54.620796+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.10681","created_at":"2026-07-05T09:07:54.620796+00:00"},{"alias_kind":"pith_short_12","alias_value":"MYK7W7GN2OBG","created_at":"2026-07-05T09:07:54.620796+00:00"},{"alias_kind":"pith_short_16","alias_value":"MYK7W7GN2OBGXMXM","created_at":"2026-07-05T09:07:54.620796+00:00"},{"alias_kind":"pith_short_8","alias_value":"MYK7W7GN","created_at":"2026-07-05T09:07:54.620796+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.21423","citing_title":"MapDiffusion: Generative Diffusion for Vectorized Online HD Map Construction and Uncertainty Estimation in Autonomous Driving","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MYK7W7GN2OBGXMXMRMKI2F3MRX","json":"https://pith.science/pith/MYK7W7GN2OBGXMXMRMKI2F3MRX.json","graph_json":"https://pith.science/api/pith-number/MYK7W7GN2OBGXMXMRMKI2F3MRX/graph.json","events_json":"https://pith.science/api/pith-number/MYK7W7GN2OBGXMXMRMKI2F3MRX/events.json","paper":"https://pith.science/paper/MYK7W7GN"},"agent_actions":{"view_html":"https://pith.science/pith/MYK7W7GN2OBGXMXMRMKI2F3MRX","download_json":"https://pith.science/pith/MYK7W7GN2OBGXMXMRMKI2F3MRX.json","view_paper":"https://pith.science/paper/MYK7W7GN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.10681&json=true","fetch_graph":"https://pith.science/api/pith-number/MYK7W7GN2OBGXMXMRMKI2F3MRX/graph.json","fetch_events":"https://pith.science/api/pith-number/MYK7W7GN2OBGXMXMRMKI2F3MRX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MYK7W7GN2OBGXMXMRMKI2F3MRX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MYK7W7GN2OBGXMXMRMKI2F3MRX/action/storage_attestation","attest_author":"https://pith.science/pith/MYK7W7GN2OBGXMXMRMKI2F3MRX/action/author_attestation","sign_citation":"https://pith.science/pith/MYK7W7GN2OBGXMXMRMKI2F3MRX/action/citation_signature","submit_replication":"https://pith.science/pith/MYK7W7GN2OBGXMXMRMKI2F3MRX/action/replication_record"}},"created_at":"2026-07-05T09:07:54.620796+00:00","updated_at":"2026-07-05T09:07:54.620796+00:00"}