{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:4U62HILWKAYS23EXVY3YQRV44N","short_pith_number":"pith:4U62HILW","schema_version":"1.0","canonical_sha256":"e53da3a17650312d6c97ae378846bce365d6a27aa02ee3885dd71ad00ff9e4de","source":{"kind":"arxiv","id":"1901.02875","version":3},"attestation_state":"computed","paper":{"title":"Learning to Infer and Execute 3D Shape Programs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Andrew Luo, Jiajun Wu, Joshua B. Tenenbaum, Kevin Ellis, William T. Freeman, Xingyuan Sun, Yonglong Tian","submitted_at":"2019-01-09T18:55:03Z","abstract_excerpt":"Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape structure such as the repetition and reflective symmetry of object parts. In contrast, recent advances in 3D shape sensing focus more on low-level geometry but less on these higher-level relationships. In this paper, we propose 3D shape programs, integrating bottom-up recognition systems with top-down, symbolic program structure to capture both low-level geometry and high-level structural priors for 3D shapes. Because ther"},"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":"1901.02875","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-09T18:55:03Z","cross_cats_sorted":["cs.AI","cs.GR","cs.LG"],"title_canon_sha256":"babec829747b5d83dd67ecd4389abcd195bcf5371f5cb32d531dd3caa9ab975c","abstract_canon_sha256":"cedba370036cea97e95da908c8b8a5e5132f7a99ee1a69e6c25cba4bb2166a11"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:52:41.566110Z","signature_b64":"OPuXwSY07uWh5AjWybPkmAQ+3bB0M35bHY3/lQj3W7/zOSH8zQh7cOSZ3i9b2+mP0PGHl6+CdueuGofQelLSDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e53da3a17650312d6c97ae378846bce365d6a27aa02ee3885dd71ad00ff9e4de","last_reissued_at":"2026-07-04T23:52:41.565675Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:52:41.565675Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Infer and Execute 3D Shape Programs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Andrew Luo, Jiajun Wu, Joshua B. Tenenbaum, Kevin Ellis, William T. Freeman, Xingyuan Sun, Yonglong Tian","submitted_at":"2019-01-09T18:55:03Z","abstract_excerpt":"Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape structure such as the repetition and reflective symmetry of object parts. In contrast, recent advances in 3D shape sensing focus more on low-level geometry but less on these higher-level relationships. In this paper, we propose 3D shape programs, integrating bottom-up recognition systems with top-down, symbolic program structure to capture both low-level geometry and high-level structural priors for 3D shapes. Because ther"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.02875","kind":"arxiv","version":3},"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/1901.02875/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":"1901.02875","created_at":"2026-07-04T23:52:41.565729+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.02875v3","created_at":"2026-07-04T23:52:41.565729+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.02875","created_at":"2026-07-04T23:52:41.565729+00:00"},{"alias_kind":"pith_short_12","alias_value":"4U62HILWKAYS","created_at":"2026-07-04T23:52:41.565729+00:00"},{"alias_kind":"pith_short_16","alias_value":"4U62HILWKAYS23EX","created_at":"2026-07-04T23:52:41.565729+00:00"},{"alias_kind":"pith_short_8","alias_value":"4U62HILW","created_at":"2026-07-04T23:52:41.565729+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.27228","citing_title":"Compositionality and the lexicon in evolutionary semantics","ref_index":87,"is_internal_anchor":false},{"citing_arxiv_id":"2607.02407","citing_title":"Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments","ref_index":58,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4U62HILWKAYS23EXVY3YQRV44N","json":"https://pith.science/pith/4U62HILWKAYS23EXVY3YQRV44N.json","graph_json":"https://pith.science/api/pith-number/4U62HILWKAYS23EXVY3YQRV44N/graph.json","events_json":"https://pith.science/api/pith-number/4U62HILWKAYS23EXVY3YQRV44N/events.json","paper":"https://pith.science/paper/4U62HILW"},"agent_actions":{"view_html":"https://pith.science/pith/4U62HILWKAYS23EXVY3YQRV44N","download_json":"https://pith.science/pith/4U62HILWKAYS23EXVY3YQRV44N.json","view_paper":"https://pith.science/paper/4U62HILW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.02875&json=true","fetch_graph":"https://pith.science/api/pith-number/4U62HILWKAYS23EXVY3YQRV44N/graph.json","fetch_events":"https://pith.science/api/pith-number/4U62HILWKAYS23EXVY3YQRV44N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4U62HILWKAYS23EXVY3YQRV44N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4U62HILWKAYS23EXVY3YQRV44N/action/storage_attestation","attest_author":"https://pith.science/pith/4U62HILWKAYS23EXVY3YQRV44N/action/author_attestation","sign_citation":"https://pith.science/pith/4U62HILWKAYS23EXVY3YQRV44N/action/citation_signature","submit_replication":"https://pith.science/pith/4U62HILWKAYS23EXVY3YQRV44N/action/replication_record"}},"created_at":"2026-07-04T23:52:41.565729+00:00","updated_at":"2026-07-04T23:52:41.565729+00:00"}