{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:CIROS3G27YW6POGWXJEJI5PZQG","short_pith_number":"pith:CIROS3G2","schema_version":"1.0","canonical_sha256":"1222e96cdafe2de7b8d6ba489475f981a869525131cd89c112ae487474278b3b","source":{"kind":"arxiv","id":"2310.08230","version":2},"attestation_state":"computed","paper":{"title":"DiscoMatch: Fast Discrete Optimisation for Geometrically Consistent 3D Shape Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahmed Abbas, Dongliang Cao, Florian Bernard, Paul Roetzer, Paul Swoboda","submitted_at":"2023-10-12T11:23:07Z","abstract_excerpt":"In this work we propose to combine the advantages of learningbased and combinatorial formalisms for 3D shape matching. While learningbased methods lead to state-of-the-art matching performance, they do not ensure geometric consistency, so that obtained matchings are locally non-smooth. On the contrary, axiomatic, optimisation-based methods allow to take geometric consistency into account by explicitly constraining the space of valid matchings. However, existing axiomatic formalisms do not scale to practically relevant problem sizes, and require user input for the initialisation of non-convex o"},"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":"2310.08230","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-12T11:23:07Z","cross_cats_sorted":[],"title_canon_sha256":"5629e0ea0c62e25c8fa1898203b84b77a9b03ced83f7245c007b20260cab0b43","abstract_canon_sha256":"1d4fdfc09f6c344546c93b081ce116cc5c48aae80f33307ae5fd6cb9f91ce7cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:14.958868Z","signature_b64":"quGpjjrkD59rgwDKYF5hdAKHIRY/btJdtf/xE7yjjBRUfyw4zC6CvsxX4UsnE842PqnDiD57gYvtxKKeT5PSCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1222e96cdafe2de7b8d6ba489475f981a869525131cd89c112ae487474278b3b","last_reissued_at":"2026-07-05T09:40:14.958398Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:14.958398Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DiscoMatch: Fast Discrete Optimisation for Geometrically Consistent 3D Shape Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahmed Abbas, Dongliang Cao, Florian Bernard, Paul Roetzer, Paul Swoboda","submitted_at":"2023-10-12T11:23:07Z","abstract_excerpt":"In this work we propose to combine the advantages of learningbased and combinatorial formalisms for 3D shape matching. While learningbased methods lead to state-of-the-art matching performance, they do not ensure geometric consistency, so that obtained matchings are locally non-smooth. On the contrary, axiomatic, optimisation-based methods allow to take geometric consistency into account by explicitly constraining the space of valid matchings. However, existing axiomatic formalisms do not scale to practically relevant problem sizes, and require user input for the initialisation of non-convex o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08230","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/2310.08230/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":"2310.08230","created_at":"2026-07-05T09:40:14.958460+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.08230v2","created_at":"2026-07-05T09:40:14.958460+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08230","created_at":"2026-07-05T09:40:14.958460+00:00"},{"alias_kind":"pith_short_12","alias_value":"CIROS3G27YW6","created_at":"2026-07-05T09:40:14.958460+00:00"},{"alias_kind":"pith_short_16","alias_value":"CIROS3G27YW6POGW","created_at":"2026-07-05T09:40:14.958460+00:00"},{"alias_kind":"pith_short_8","alias_value":"CIROS3G2","created_at":"2026-07-05T09:40:14.958460+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/CIROS3G27YW6POGWXJEJI5PZQG","json":"https://pith.science/pith/CIROS3G27YW6POGWXJEJI5PZQG.json","graph_json":"https://pith.science/api/pith-number/CIROS3G27YW6POGWXJEJI5PZQG/graph.json","events_json":"https://pith.science/api/pith-number/CIROS3G27YW6POGWXJEJI5PZQG/events.json","paper":"https://pith.science/paper/CIROS3G2"},"agent_actions":{"view_html":"https://pith.science/pith/CIROS3G27YW6POGWXJEJI5PZQG","download_json":"https://pith.science/pith/CIROS3G27YW6POGWXJEJI5PZQG.json","view_paper":"https://pith.science/paper/CIROS3G2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.08230&json=true","fetch_graph":"https://pith.science/api/pith-number/CIROS3G27YW6POGWXJEJI5PZQG/graph.json","fetch_events":"https://pith.science/api/pith-number/CIROS3G27YW6POGWXJEJI5PZQG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CIROS3G27YW6POGWXJEJI5PZQG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CIROS3G27YW6POGWXJEJI5PZQG/action/storage_attestation","attest_author":"https://pith.science/pith/CIROS3G27YW6POGWXJEJI5PZQG/action/author_attestation","sign_citation":"https://pith.science/pith/CIROS3G27YW6POGWXJEJI5PZQG/action/citation_signature","submit_replication":"https://pith.science/pith/CIROS3G27YW6POGWXJEJI5PZQG/action/replication_record"}},"created_at":"2026-07-05T09:40:14.958460+00:00","updated_at":"2026-07-05T09:40:14.958460+00:00"}