{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:6CMH7RC2OXPOMBQP2GU4ABHLNU","short_pith_number":"pith:6CMH7RC2","schema_version":"1.0","canonical_sha256":"f0987fc45a75dee6060fd1a9c004eb6d28d16b94d7ffdc9c0fe88bca852f4686","source":{"kind":"arxiv","id":"2104.00024","version":2},"attestation_state":"computed","paper":{"title":"RetrievalFuse: Neural 3D Scene Reconstruction with a Database","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Angela Dai, Fangchang Ma, Justus Thies, Matthias Nie{\\ss}ner, Qi Shan, Yawar Siddiqui","submitted_at":"2021-03-31T18:00:09Z","abstract_excerpt":"3D reconstruction of large scenes is a challenging problem due to the high-complexity nature of the solution space, in particular for generative neural networks. In contrast to traditional generative learned models which encode the full generative process into a neural network and can struggle with maintaining local details at the scene level, we introduce a new method that directly leverages scene geometry from the training database. First, we learn to synthesize an initial estimate for a 3D scene, constructed by retrieving a top-k set of volumetric chunks from the scene database. These candi"},"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":"2104.00024","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-31T18:00:09Z","cross_cats_sorted":[],"title_canon_sha256":"222cf3bb82b268747a70f903c964215ead91b21243fbe51d90ba29bbfb8c46eb","abstract_canon_sha256":"555abf3f35003964886aecb97d256ec379b6833ce47446539f52e252f4303240"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:04:23.004405Z","signature_b64":"Ux8xqRojaPmTGP/LMPJOEfj+zj+5PXic2iqx+/Rj4qaBpYzWjPTGPAkc1yO5H0V2y3gNZ85u6fcKIvOpzQYuDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0987fc45a75dee6060fd1a9c004eb6d28d16b94d7ffdc9c0fe88bca852f4686","last_reissued_at":"2026-07-05T03:04:23.003985Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:04:23.003985Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RetrievalFuse: Neural 3D Scene Reconstruction with a Database","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Angela Dai, Fangchang Ma, Justus Thies, Matthias Nie{\\ss}ner, Qi Shan, Yawar Siddiqui","submitted_at":"2021-03-31T18:00:09Z","abstract_excerpt":"3D reconstruction of large scenes is a challenging problem due to the high-complexity nature of the solution space, in particular for generative neural networks. In contrast to traditional generative learned models which encode the full generative process into a neural network and can struggle with maintaining local details at the scene level, we introduce a new method that directly leverages scene geometry from the training database. First, we learn to synthesize an initial estimate for a 3D scene, constructed by retrieving a top-k set of volumetric chunks from the scene database. These candi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.00024","kind":"arxiv","version":2},"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/2104.00024/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":"2104.00024","created_at":"2026-07-05T03:04:23.004042+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.00024v2","created_at":"2026-07-05T03:04:23.004042+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.00024","created_at":"2026-07-05T03:04:23.004042+00:00"},{"alias_kind":"pith_short_12","alias_value":"6CMH7RC2OXPO","created_at":"2026-07-05T03:04:23.004042+00:00"},{"alias_kind":"pith_short_16","alias_value":"6CMH7RC2OXPOMBQP","created_at":"2026-07-05T03:04:23.004042+00:00"},{"alias_kind":"pith_short_8","alias_value":"6CMH7RC2","created_at":"2026-07-05T03:04:23.004042+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.18429","citing_title":"CAOA -- Completion-Assisted Object-CAD Alignment","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6CMH7RC2OXPOMBQP2GU4ABHLNU","json":"https://pith.science/pith/6CMH7RC2OXPOMBQP2GU4ABHLNU.json","graph_json":"https://pith.science/api/pith-number/6CMH7RC2OXPOMBQP2GU4ABHLNU/graph.json","events_json":"https://pith.science/api/pith-number/6CMH7RC2OXPOMBQP2GU4ABHLNU/events.json","paper":"https://pith.science/paper/6CMH7RC2"},"agent_actions":{"view_html":"https://pith.science/pith/6CMH7RC2OXPOMBQP2GU4ABHLNU","download_json":"https://pith.science/pith/6CMH7RC2OXPOMBQP2GU4ABHLNU.json","view_paper":"https://pith.science/paper/6CMH7RC2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.00024&json=true","fetch_graph":"https://pith.science/api/pith-number/6CMH7RC2OXPOMBQP2GU4ABHLNU/graph.json","fetch_events":"https://pith.science/api/pith-number/6CMH7RC2OXPOMBQP2GU4ABHLNU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6CMH7RC2OXPOMBQP2GU4ABHLNU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6CMH7RC2OXPOMBQP2GU4ABHLNU/action/storage_attestation","attest_author":"https://pith.science/pith/6CMH7RC2OXPOMBQP2GU4ABHLNU/action/author_attestation","sign_citation":"https://pith.science/pith/6CMH7RC2OXPOMBQP2GU4ABHLNU/action/citation_signature","submit_replication":"https://pith.science/pith/6CMH7RC2OXPOMBQP2GU4ABHLNU/action/replication_record"}},"created_at":"2026-07-05T03:04:23.004042+00:00","updated_at":"2026-07-05T03:04:23.004042+00:00"}