{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:L74ULWS3FYRIMNVOWIIP5MVCEZ","short_pith_number":"pith:L74ULWS3","schema_version":"1.0","canonical_sha256":"5ff945da5b2e228636aeb210feb2a2266d5b2eb675a78fecc4031843c2705836","source":{"kind":"arxiv","id":"2106.11272","version":3},"attestation_state":"computed","paper":{"title":"Neural Marching Cubes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.GR","authors_text":"Hao Zhang, Zhiqin Chen","submitted_at":"2021-06-21T17:18:52Z","abstract_excerpt":"We introduce Neural Marching Cubes (NMC), a data-driven approach for extracting a triangle mesh from a discretized implicit field. Classical MC is defined by coarse tessellation templates isolated to individual cubes. While more refined tessellations have been proposed, they all make heuristic assumptions, such as trilinearity, when determining the vertex positions and local mesh topologies in each cube. In principle, none of these approaches can reconstruct geometric features that reveal coherence or dependencies between nearby cubes (e.g., a sharp edge), as such information is unaccounted fo"},"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":"2106.11272","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2021-06-21T17:18:52Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"22cabd703ed58063d9e4a7d4babf5100d70727c2641c4b797ca481c56dcadf34","abstract_canon_sha256":"3baa1bacba0ba5fd0473678b78afda55bb6c1131ed6775bff4362345b9507bdc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:13:30.099982Z","signature_b64":"F9R3IFWVWJYvDNyU015cl184/CCmAVYZXVNofupd9yoyUKLM7UAifokwboonDgiIHbTHMQNs6sFDFgYdU69JBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ff945da5b2e228636aeb210feb2a2266d5b2eb675a78fecc4031843c2705836","last_reissued_at":"2026-07-05T03:13:30.099562Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:13:30.099562Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Marching Cubes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.GR","authors_text":"Hao Zhang, Zhiqin Chen","submitted_at":"2021-06-21T17:18:52Z","abstract_excerpt":"We introduce Neural Marching Cubes (NMC), a data-driven approach for extracting a triangle mesh from a discretized implicit field. Classical MC is defined by coarse tessellation templates isolated to individual cubes. While more refined tessellations have been proposed, they all make heuristic assumptions, such as trilinearity, when determining the vertex positions and local mesh topologies in each cube. In principle, none of these approaches can reconstruct geometric features that reveal coherence or dependencies between nearby cubes (e.g., a sharp edge), as such information is unaccounted fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.11272","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/2106.11272/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":"2106.11272","created_at":"2026-07-05T03:13:30.099617+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.11272v3","created_at":"2026-07-05T03:13:30.099617+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.11272","created_at":"2026-07-05T03:13:30.099617+00:00"},{"alias_kind":"pith_short_12","alias_value":"L74ULWS3FYRI","created_at":"2026-07-05T03:13:30.099617+00:00"},{"alias_kind":"pith_short_16","alias_value":"L74ULWS3FYRIMNVO","created_at":"2026-07-05T03:13:30.099617+00:00"},{"alias_kind":"pith_short_8","alias_value":"L74ULWS3","created_at":"2026-07-05T03:13:30.099617+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.09579","citing_title":"Power Diagram Enhanced Adaptive Isosurface Extraction from Signed Distance Fields","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L74ULWS3FYRIMNVOWIIP5MVCEZ","json":"https://pith.science/pith/L74ULWS3FYRIMNVOWIIP5MVCEZ.json","graph_json":"https://pith.science/api/pith-number/L74ULWS3FYRIMNVOWIIP5MVCEZ/graph.json","events_json":"https://pith.science/api/pith-number/L74ULWS3FYRIMNVOWIIP5MVCEZ/events.json","paper":"https://pith.science/paper/L74ULWS3"},"agent_actions":{"view_html":"https://pith.science/pith/L74ULWS3FYRIMNVOWIIP5MVCEZ","download_json":"https://pith.science/pith/L74ULWS3FYRIMNVOWIIP5MVCEZ.json","view_paper":"https://pith.science/paper/L74ULWS3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.11272&json=true","fetch_graph":"https://pith.science/api/pith-number/L74ULWS3FYRIMNVOWIIP5MVCEZ/graph.json","fetch_events":"https://pith.science/api/pith-number/L74ULWS3FYRIMNVOWIIP5MVCEZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L74ULWS3FYRIMNVOWIIP5MVCEZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L74ULWS3FYRIMNVOWIIP5MVCEZ/action/storage_attestation","attest_author":"https://pith.science/pith/L74ULWS3FYRIMNVOWIIP5MVCEZ/action/author_attestation","sign_citation":"https://pith.science/pith/L74ULWS3FYRIMNVOWIIP5MVCEZ/action/citation_signature","submit_replication":"https://pith.science/pith/L74ULWS3FYRIMNVOWIIP5MVCEZ/action/replication_record"}},"created_at":"2026-07-05T03:13:30.099617+00:00","updated_at":"2026-07-05T03:13:30.099617+00:00"}