{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/jigsaw-learning-to-assemble-multiple-1","title":"Jigsaw: Learning to Assemble Multiple Fractured Objects","arxiv_id":"2305.17975","date":"2023-05-29","proceeding":"NeurIPS 2023 11","authors":["Jiaxin Lu","Yifan Sun","QiXing Huang"],"abstract":"Automated assembly of 3D fractures is essential in orthopedics, archaeology, and our daily life. This paper presents Jigsaw, a novel framework for assembling physically broken 3D objects from multiple pieces. Our approach leverages hierarchical features of global and local geometry to match and align the fracture surfaces. Our framework consists of four components: (1) front-end point feature extractor with attention layers, (2) surface segmentation to separate fracture and original parts, (3) multi-parts matching to find correspondences among fracture surface points, and (4) robust global alignment to recover the global poses of the pieces. We show how to jointly learn segmentation and matching and seamlessly integrate feature matching and rigidity constraints. We evaluate Jigsaw on the Breaking Bad dataset and achieve superior performance compared to state-of-the-art methods. Our method also generalizes well to diverse fracture modes, objects, and unseen instances. To the best of our knowledge, this is the first learning-based method designed specifically for 3D fracture assembly over multiple pieces. Our code is available at https://jiaxin-lu.github.io/Jigsaw/.","url_abs":"https://arxiv.org/abs/2305.17975v2","url_pdf":"https://arxiv.org/pdf/2305.17975v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"jigsaw-learning-to-assemble-multiple-1","repo_url":"https://github.com/jiaxin-lu/jigsaw","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[{"method_slug":"align","method_name":"ALIGN"},{"method_slug":"jigsaw","method_name":"Jigsaw"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2305.17975","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.17975"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jiaxin-lu/jigsaw","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":4},"by_repo_kind":{"listed":{"samples":4,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"b55e5f7c2a95ad4a","entry":"build_affinity","repo":"jiaxin-lu/jigsaw","repo_kind":"listed","path":"model/jigsaw/affinity_layer.py","file_url":"https://github.com/jiaxin-lu/jigsaw/blob/HEAD/model/jigsaw/affinity_layer.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b55e5f7c2a95ad4a"}},{"code_sha256_prefix":"c0eb34ee5d36ac48","entry":"knn_and_group","repo":"jiaxin-lu/jigsaw","repo_kind":"listed","path":"model/jigsaw/attention_layer.py","file_url":"https://github.com/jiaxin-lu/jigsaw/blob/HEAD/model/jigsaw/attention_layer.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c0eb34ee5d36ac48"}},{"code_sha256_prefix":"f3aa9a72722d4971","entry":"permutation_loss","repo":"jiaxin-lu/jigsaw","repo_kind":"listed","path":"utils/loss.py","file_url":"https://github.com/jiaxin-lu/jigsaw/blob/HEAD/utils/loss.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f3aa9a72722d4971"}},{"code_sha256_prefix":"e0941a2d5b3eb8f4","entry":"rigid_loss","repo":"jiaxin-lu/jigsaw","repo_kind":"listed","path":"utils/loss.py","file_url":"https://github.com/jiaxin-lu/jigsaw/blob/HEAD/utils/loss.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e0941a2d5b3eb8f4"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}