{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:UBSNBHH4ZMLHSN2DDQCJ3WE4HB","short_pith_number":"pith:UBSNBHH4","schema_version":"1.0","canonical_sha256":"a064d09cfccb167937431c049dd89c386e5ac813bd061eeb86a5695f5ced38d4","source":{"kind":"arxiv","id":"2606.03915","version":1},"attestation_state":"computed","paper":{"title":"PatchScene: Patch-based Voxel Diffusion for Large-Scale Scene Completion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chao Lu, Huanran Wang, Jiajun Zhu, Jiyao Zhang, Qingdong Xu, ShiLin Zhu, Xinjing He","submitted_at":"2026-06-02T17:09:20Z","abstract_excerpt":"We propose PatchScene, a novel diffusion-based framework for large-scale LiDAR scene completion. Unlike existing methods that rely on global latent representations or dense voxel grids, PatchScene adopts a patch-based voxel diffusion paradigm that explicitly generates fine-grained geometry within localized 3D regions. To ensure coherent reconstruction at both spatial and temporal scales, we introduce a confidence-guided spatio-temporal fusion mechanism that integrates overlapping patches and adjacent frames in a unified generative process. Furthermore, we design an Annular-Flow diffusion strat"},"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":"2606.03915","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-02T17:09:20Z","cross_cats_sorted":[],"title_canon_sha256":"f333a0e27d9c2098a83926e30be5e107fcad55f0c629e6d7943b8a2df468da9e","abstract_canon_sha256":"89463f2d2bf8d2d07d9b19fd51f9b6b1e1d237f7717966ff5614a0d935c2fd4e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T02:06:06.791432Z","signature_b64":"Fb5PbRLQ1lVJ+ahvHSGHm90PjD8gMxohYUWvLzYaBsk+i4N1qfXtQobC39cMXiFsbfMZX77YJIX8Jz9MeuJXAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a064d09cfccb167937431c049dd89c386e5ac813bd061eeb86a5695f5ced38d4","last_reissued_at":"2026-06-03T02:06:06.791029Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T02:06:06.791029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PatchScene: Patch-based Voxel Diffusion for Large-Scale Scene Completion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chao Lu, Huanran Wang, Jiajun Zhu, Jiyao Zhang, Qingdong Xu, ShiLin Zhu, Xinjing He","submitted_at":"2026-06-02T17:09:20Z","abstract_excerpt":"We propose PatchScene, a novel diffusion-based framework for large-scale LiDAR scene completion. Unlike existing methods that rely on global latent representations or dense voxel grids, PatchScene adopts a patch-based voxel diffusion paradigm that explicitly generates fine-grained geometry within localized 3D regions. To ensure coherent reconstruction at both spatial and temporal scales, we introduce a confidence-guided spatio-temporal fusion mechanism that integrates overlapping patches and adjacent frames in a unified generative process. Furthermore, we design an Annular-Flow diffusion strat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.03915","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/2606.03915/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":"2606.03915","created_at":"2026-06-03T02:06:06.791079+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.03915v1","created_at":"2026-06-03T02:06:06.791079+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.03915","created_at":"2026-06-03T02:06:06.791079+00:00"},{"alias_kind":"pith_short_12","alias_value":"UBSNBHH4ZMLH","created_at":"2026-06-03T02:06:06.791079+00:00"},{"alias_kind":"pith_short_16","alias_value":"UBSNBHH4ZMLHSN2D","created_at":"2026-06-03T02:06:06.791079+00:00"},{"alias_kind":"pith_short_8","alias_value":"UBSNBHH4","created_at":"2026-06-03T02:06:06.791079+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UBSNBHH4ZMLHSN2DDQCJ3WE4HB","json":"https://pith.science/pith/UBSNBHH4ZMLHSN2DDQCJ3WE4HB.json","graph_json":"https://pith.science/api/pith-number/UBSNBHH4ZMLHSN2DDQCJ3WE4HB/graph.json","events_json":"https://pith.science/api/pith-number/UBSNBHH4ZMLHSN2DDQCJ3WE4HB/events.json","paper":"https://pith.science/paper/UBSNBHH4"},"agent_actions":{"view_html":"https://pith.science/pith/UBSNBHH4ZMLHSN2DDQCJ3WE4HB","download_json":"https://pith.science/pith/UBSNBHH4ZMLHSN2DDQCJ3WE4HB.json","view_paper":"https://pith.science/paper/UBSNBHH4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.03915&json=true","fetch_graph":"https://pith.science/api/pith-number/UBSNBHH4ZMLHSN2DDQCJ3WE4HB/graph.json","fetch_events":"https://pith.science/api/pith-number/UBSNBHH4ZMLHSN2DDQCJ3WE4HB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UBSNBHH4ZMLHSN2DDQCJ3WE4HB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UBSNBHH4ZMLHSN2DDQCJ3WE4HB/action/storage_attestation","attest_author":"https://pith.science/pith/UBSNBHH4ZMLHSN2DDQCJ3WE4HB/action/author_attestation","sign_citation":"https://pith.science/pith/UBSNBHH4ZMLHSN2DDQCJ3WE4HB/action/citation_signature","submit_replication":"https://pith.science/pith/UBSNBHH4ZMLHSN2DDQCJ3WE4HB/action/replication_record"}},"created_at":"2026-06-03T02:06:06.791079+00:00","updated_at":"2026-06-03T02:06:06.791079+00:00"}