{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:HR5NRCXDNRHF4GSOOR7MJBLGAY","short_pith_number":"pith:HR5NRCXD","schema_version":"1.0","canonical_sha256":"3c7ad88ae36c4e5e1a4e747ec48566062ae52c86c82daf9f2df9b104d495c006","source":{"kind":"arxiv","id":"2211.13785","version":3},"attestation_state":"computed","paper":{"title":"PuzzleFusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.AI","authors_text":"Mohammad Amin Shabani, Saghar Irandoust, Sepidehsadat Hosseini, Yasutaka Furukawa","submitted_at":"2022-11-24T20:06:11Z","abstract_excerpt":"This paper presents an end-to-end neural architecture based on Diffusion Models for spatial puzzle solving, particularly jigsaw puzzle and room arrangement tasks. In the latter task, for instance, the proposed system \"PuzzleFusion\" takes a set of room layouts as polygonal curves in the top-down view and aligns the room layout pieces by estimating their 2D translations and rotations, akin to solving the jigsaw puzzle of room layouts. A surprising discovery of the paper is that the simple use of a Diffusion Model effectively solves these challenging spatial puzzle tasks as a conditional generati"},"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":"2211.13785","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2022-11-24T20:06:11Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"6e8f106859f6b447339a8f80f617054ebafb8c4dc9d8dece734ed4a4a1c64318","abstract_canon_sha256":"0c47d02d72ce8c5073cd4f1ff8b55b91414022cc9595b47cb9aea83d072a8bf8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:56:52.899739Z","signature_b64":"xD958TYP7g810j5RpIVvnE70vhREZh3Q7iYXrNLUn1secNfIq3qw/KtDeKPHd7aMcM30hUk6QQSq00X6Y+mmBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c7ad88ae36c4e5e1a4e747ec48566062ae52c86c82daf9f2df9b104d495c006","last_reissued_at":"2026-07-05T06:56:52.899214Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:56:52.899214Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PuzzleFusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.AI","authors_text":"Mohammad Amin Shabani, Saghar Irandoust, Sepidehsadat Hosseini, Yasutaka Furukawa","submitted_at":"2022-11-24T20:06:11Z","abstract_excerpt":"This paper presents an end-to-end neural architecture based on Diffusion Models for spatial puzzle solving, particularly jigsaw puzzle and room arrangement tasks. In the latter task, for instance, the proposed system \"PuzzleFusion\" takes a set of room layouts as polygonal curves in the top-down view and aligns the room layout pieces by estimating their 2D translations and rotations, akin to solving the jigsaw puzzle of room layouts. A surprising discovery of the paper is that the simple use of a Diffusion Model effectively solves these challenging spatial puzzle tasks as a conditional generati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.13785","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/2211.13785/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":"2211.13785","created_at":"2026-07-05T06:56:52.899278+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.13785v3","created_at":"2026-07-05T06:56:52.899278+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.13785","created_at":"2026-07-05T06:56:52.899278+00:00"},{"alias_kind":"pith_short_12","alias_value":"HR5NRCXDNRHF","created_at":"2026-07-05T06:56:52.899278+00:00"},{"alias_kind":"pith_short_16","alias_value":"HR5NRCXDNRHF4GSO","created_at":"2026-07-05T06:56:52.899278+00:00"},{"alias_kind":"pith_short_8","alias_value":"HR5NRCXD","created_at":"2026-07-05T06:56:52.899278+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/HR5NRCXDNRHF4GSOOR7MJBLGAY","json":"https://pith.science/pith/HR5NRCXDNRHF4GSOOR7MJBLGAY.json","graph_json":"https://pith.science/api/pith-number/HR5NRCXDNRHF4GSOOR7MJBLGAY/graph.json","events_json":"https://pith.science/api/pith-number/HR5NRCXDNRHF4GSOOR7MJBLGAY/events.json","paper":"https://pith.science/paper/HR5NRCXD"},"agent_actions":{"view_html":"https://pith.science/pith/HR5NRCXDNRHF4GSOOR7MJBLGAY","download_json":"https://pith.science/pith/HR5NRCXDNRHF4GSOOR7MJBLGAY.json","view_paper":"https://pith.science/paper/HR5NRCXD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.13785&json=true","fetch_graph":"https://pith.science/api/pith-number/HR5NRCXDNRHF4GSOOR7MJBLGAY/graph.json","fetch_events":"https://pith.science/api/pith-number/HR5NRCXDNRHF4GSOOR7MJBLGAY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HR5NRCXDNRHF4GSOOR7MJBLGAY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HR5NRCXDNRHF4GSOOR7MJBLGAY/action/storage_attestation","attest_author":"https://pith.science/pith/HR5NRCXDNRHF4GSOOR7MJBLGAY/action/author_attestation","sign_citation":"https://pith.science/pith/HR5NRCXDNRHF4GSOOR7MJBLGAY/action/citation_signature","submit_replication":"https://pith.science/pith/HR5NRCXDNRHF4GSOOR7MJBLGAY/action/replication_record"}},"created_at":"2026-07-05T06:56:52.899278+00:00","updated_at":"2026-07-05T06:56:52.899278+00:00"}