{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:DXXTWF2DQDYPWO5QSFAJS2L7LN","short_pith_number":"pith:DXXTWF2D","schema_version":"1.0","canonical_sha256":"1def3b174380f0fb3bb0914099697f5b62151dd8a74db2bd66a0e291b31f2518","source":{"kind":"arxiv","id":"1707.09123","version":1},"attestation_state":"computed","paper":{"title":"Research on Shape Mapping of 3D Mesh Models based on Hidden Markov Random Field and EM Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Huai-Yu Wu, Yong Wang","submitted_at":"2017-07-28T07:03:29Z","abstract_excerpt":"How to establish the matching (or corresponding) between two different 3D shapes is a classical problem. This paper focused on the research on shape mapping of 3D mesh models, and proposed a shape mapping algorithm based on Hidden Markov Random Field and EM algorithm, as introducing a hidden state random variable associated with the adjacent blocks of shape matching when establishing HMRF. This algorithm provides a new theory and method to ensure the consistency of the edge data of adjacent blocks, and the experimental results show that the algorithm in this paper has a great improvement on th"},"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":"1707.09123","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-07-28T07:03:29Z","cross_cats_sorted":[],"title_canon_sha256":"65c6cc439c10fdede461374c427db5ccf75ef1e581e6c24a5a0029d6e48e7091","abstract_canon_sha256":"66c41a1a81e0491f2bcb0c564d7ef7332e9f346a7544d39b17857fffca067f65"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:17.034253Z","signature_b64":"63CMrSRcMYTTMAyqF2TJLrQQfXtvl4w1gcgnNeTMLgXJIJSd8OFEFYMt2FvHC7HvTZMUJWnwhuBFmEz2444fAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1def3b174380f0fb3bb0914099697f5b62151dd8a74db2bd66a0e291b31f2518","last_reissued_at":"2026-05-18T00:39:17.033628Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:17.033628Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Research on Shape Mapping of 3D Mesh Models based on Hidden Markov Random Field and EM Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Huai-Yu Wu, Yong Wang","submitted_at":"2017-07-28T07:03:29Z","abstract_excerpt":"How to establish the matching (or corresponding) between two different 3D shapes is a classical problem. This paper focused on the research on shape mapping of 3D mesh models, and proposed a shape mapping algorithm based on Hidden Markov Random Field and EM algorithm, as introducing a hidden state random variable associated with the adjacent blocks of shape matching when establishing HMRF. This algorithm provides a new theory and method to ensure the consistency of the edge data of adjacent blocks, and the experimental results show that the algorithm in this paper has a great improvement on th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.09123","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":""},"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":"1707.09123","created_at":"2026-05-18T00:39:17.033724+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.09123v1","created_at":"2026-05-18T00:39:17.033724+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.09123","created_at":"2026-05-18T00:39:17.033724+00:00"},{"alias_kind":"pith_short_12","alias_value":"DXXTWF2DQDYP","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_16","alias_value":"DXXTWF2DQDYPWO5Q","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_8","alias_value":"DXXTWF2D","created_at":"2026-05-18T12:31:12.930513+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/DXXTWF2DQDYPWO5QSFAJS2L7LN","json":"https://pith.science/pith/DXXTWF2DQDYPWO5QSFAJS2L7LN.json","graph_json":"https://pith.science/api/pith-number/DXXTWF2DQDYPWO5QSFAJS2L7LN/graph.json","events_json":"https://pith.science/api/pith-number/DXXTWF2DQDYPWO5QSFAJS2L7LN/events.json","paper":"https://pith.science/paper/DXXTWF2D"},"agent_actions":{"view_html":"https://pith.science/pith/DXXTWF2DQDYPWO5QSFAJS2L7LN","download_json":"https://pith.science/pith/DXXTWF2DQDYPWO5QSFAJS2L7LN.json","view_paper":"https://pith.science/paper/DXXTWF2D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.09123&json=true","fetch_graph":"https://pith.science/api/pith-number/DXXTWF2DQDYPWO5QSFAJS2L7LN/graph.json","fetch_events":"https://pith.science/api/pith-number/DXXTWF2DQDYPWO5QSFAJS2L7LN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DXXTWF2DQDYPWO5QSFAJS2L7LN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DXXTWF2DQDYPWO5QSFAJS2L7LN/action/storage_attestation","attest_author":"https://pith.science/pith/DXXTWF2DQDYPWO5QSFAJS2L7LN/action/author_attestation","sign_citation":"https://pith.science/pith/DXXTWF2DQDYPWO5QSFAJS2L7LN/action/citation_signature","submit_replication":"https://pith.science/pith/DXXTWF2DQDYPWO5QSFAJS2L7LN/action/replication_record"}},"created_at":"2026-05-18T00:39:17.033724+00:00","updated_at":"2026-05-18T00:39:17.033724+00:00"}