{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:UF4QRXFTVLOQORN5B4V2Y7TP2T","short_pith_number":"pith:UF4QRXFT","schema_version":"1.0","canonical_sha256":"a17908dcb3aadd0745bd0f2bac7e6fd4f2998564c1876a1dabb719e4d34d6641","source":{"kind":"arxiv","id":"2303.10406","version":1},"attestation_state":"computed","paper":{"title":"3DQD: Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Bingbing Ni, Fuzhen Wang, Xuanhong Chen, Yilin Sun, Yishun Dou, Yuhan Li, Yutian Liu","submitted_at":"2023-03-18T12:50:29Z","abstract_excerpt":"We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks including unconditional shape generation, point cloud completion, and cross-modality shape generation, etc. On one hand, to precisely capture local fine detailed shape information, a vector quantized variational autoencoder (VQ-VAE) is utilized to index local geometry from a compactly learned codebook based on a broad set of task training data. On the other hand, a discrete diffusion generator is introduced to model the inherent structural dependencies among different tokens. In the meantime, a multi-frequ"},"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":"2303.10406","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-18T12:50:29Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"591077f491dede2f78549cb4be5e640a573073cb96257bc9c8c06a97ca442928","abstract_canon_sha256":"ec9d9b4706bff01484e7fd2bf50290ed5ab0daf42a6be3ecb6164cd787998e02"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:52:31.956345Z","signature_b64":"ruK3hvGC2nO3bfOUGkztxOkru4BS90z11+P1o0yTnl6Xun04wNrrBDtRMYC5BNyV7sGcZmQ8IMfGqZerEskzCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a17908dcb3aadd0745bd0f2bac7e6fd4f2998564c1876a1dabb719e4d34d6641","last_reissued_at":"2026-07-05T05:52:31.955984Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:52:31.955984Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"3DQD: Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Bingbing Ni, Fuzhen Wang, Xuanhong Chen, Yilin Sun, Yishun Dou, Yuhan Li, Yutian Liu","submitted_at":"2023-03-18T12:50:29Z","abstract_excerpt":"We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks including unconditional shape generation, point cloud completion, and cross-modality shape generation, etc. On one hand, to precisely capture local fine detailed shape information, a vector quantized variational autoencoder (VQ-VAE) is utilized to index local geometry from a compactly learned codebook based on a broad set of task training data. On the other hand, a discrete diffusion generator is introduced to model the inherent structural dependencies among different tokens. In the meantime, a multi-frequ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.10406","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/2303.10406/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":"2303.10406","created_at":"2026-07-05T05:52:31.956041+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.10406v1","created_at":"2026-07-05T05:52:31.956041+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.10406","created_at":"2026-07-05T05:52:31.956041+00:00"},{"alias_kind":"pith_short_12","alias_value":"UF4QRXFTVLOQ","created_at":"2026-07-05T05:52:31.956041+00:00"},{"alias_kind":"pith_short_16","alias_value":"UF4QRXFTVLOQORN5","created_at":"2026-07-05T05:52:31.956041+00:00"},{"alias_kind":"pith_short_8","alias_value":"UF4QRXFT","created_at":"2026-07-05T05:52:31.956041+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/UF4QRXFTVLOQORN5B4V2Y7TP2T","json":"https://pith.science/pith/UF4QRXFTVLOQORN5B4V2Y7TP2T.json","graph_json":"https://pith.science/api/pith-number/UF4QRXFTVLOQORN5B4V2Y7TP2T/graph.json","events_json":"https://pith.science/api/pith-number/UF4QRXFTVLOQORN5B4V2Y7TP2T/events.json","paper":"https://pith.science/paper/UF4QRXFT"},"agent_actions":{"view_html":"https://pith.science/pith/UF4QRXFTVLOQORN5B4V2Y7TP2T","download_json":"https://pith.science/pith/UF4QRXFTVLOQORN5B4V2Y7TP2T.json","view_paper":"https://pith.science/paper/UF4QRXFT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.10406&json=true","fetch_graph":"https://pith.science/api/pith-number/UF4QRXFTVLOQORN5B4V2Y7TP2T/graph.json","fetch_events":"https://pith.science/api/pith-number/UF4QRXFTVLOQORN5B4V2Y7TP2T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UF4QRXFTVLOQORN5B4V2Y7TP2T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UF4QRXFTVLOQORN5B4V2Y7TP2T/action/storage_attestation","attest_author":"https://pith.science/pith/UF4QRXFTVLOQORN5B4V2Y7TP2T/action/author_attestation","sign_citation":"https://pith.science/pith/UF4QRXFTVLOQORN5B4V2Y7TP2T/action/citation_signature","submit_replication":"https://pith.science/pith/UF4QRXFTVLOQORN5B4V2Y7TP2T/action/replication_record"}},"created_at":"2026-07-05T05:52:31.956041+00:00","updated_at":"2026-07-05T05:52:31.956041+00:00"}