{"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/reliable-inlier-evaluation-for-unsupervised","title":"Reliable Inlier Evaluation for Unsupervised Point Cloud Registration","arxiv_id":"2202.11292","date":"2022-02-23","proceeding":null,"authors":["Yaqi Shen","Le Hui","Haobo Jiang","Jin Xie","Jian Yang"],"abstract":"Unsupervised point cloud registration algorithm usually suffers from the unsatisfied registration precision in the partially overlapping problem due to the lack of effective inlier evaluation. In this paper, we propose a neighborhood consensus based reliable inlier evaluation method for robust unsupervised point cloud registration. It is expected to capture the discriminative geometric difference between the source neighborhood and the corresponding pseudo target neighborhood for effective inlier distinction. Specifically, our model consists of a matching map refinement module and an inlier evaluation module. In our matching map refinement module, we improve the point-wise matching map estimation by integrating the matching scores of neighbors into it. The aggregated neighborhood information potentially facilitates the discriminative map construction so that high-quality correspondences can be provided for generating the pseudo target point cloud. Based on the observation that the outlier has the significant structure-wise difference between its source neighborhood and corresponding pseudo target neighborhood while this difference for inlier is small, the inlier evaluation module exploits this difference to score the inlier confidence for each estimated correspondence. In particular, we construct an effective graph representation for capturing this geometric difference between the neighborhoods. Finally, with the learned correspondences and the corresponding inlier confidence, we use the weighted SVD algorithm for transformation estimation. Under the unsupervised setting, we exploit the Huber function based global alignment loss, the local neighborhood consensus loss, and spatial consistency loss for model optimization. The experimental results on extensive datasets demonstrate that our unsupervised point cloud registration method can yield comparable performance.","url_abs":"https://arxiv.org/abs/2202.11292v1","url_pdf":"https://arxiv.org/pdf/2202.11292v1.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":"reliable-inlier-evaluation-for-unsupervised","repo_url":"https://github.com/supersyq/rienet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"model-optimization","task_name":"Model Optimization"},{"task_slug":"point-cloud-registration","task_name":"Point Cloud Registration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2202.11292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.11292"}},"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/supersyq/rienet","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_fixture":1,"ran":1,"unverified":10},"by_repo_kind":{"official":{"samples":12,"ran":2,"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":"35d6887261f3e988","entry":"knn","repo":"supersyq/rienet","repo_kind":"official","path":"utils.py","file_url":"https://github.com/supersyq/rienet/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"35d6887261f3e988"}},{"code_sha256_prefix":"838e2078ad9224da","entry":"quat2mat","repo":"supersyq/rienet","repo_kind":"official","path":"util.py","file_url":"https://github.com/supersyq/rienet/blob/HEAD/util.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"838e2078ad9224da"}},{"code_sha256_prefix":"3f15da07b66d960e","entry":"farthest_subsample_points","repo":"supersyq/rienet","repo_kind":"official","path":"data_modelnet40.py","file_url":"https://github.com/supersyq/rienet/blob/HEAD/data_modelnet40.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":"3f15da07b66d960e"}},{"code_sha256_prefix":"11f1fc28e78be11c","entry":"get_graph_feature","repo":"supersyq/rienet","repo_kind":"official","path":"utils.py","file_url":"https://github.com/supersyq/rienet/blob/HEAD/utils.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":"11f1fc28e78be11c"}},{"code_sha256_prefix":"4db7368a77694af8","entry":"jitter_pcd","repo":"supersyq/rienet","repo_kind":"official","path":"data_icl.py","file_url":"https://github.com/supersyq/rienet/blob/HEAD/data_icl.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":"4db7368a77694af8"}},{"code_sha256_prefix":"50f07ba76d757466","entry":"jitter_pointcloud","repo":"supersyq/rienet","repo_kind":"official","path":"data_modelnet40.py","file_url":"https://github.com/supersyq/rienet/blob/HEAD/data_modelnet40.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":"50f07ba76d757466"}},{"code_sha256_prefix":"166cfaf582a0252e","entry":"load_data","repo":"supersyq/rienet","repo_kind":"official","path":"data_modelnet40.py","file_url":"https://github.com/supersyq/rienet/blob/HEAD/data_modelnet40.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":"166cfaf582a0252e"}},{"code_sha256_prefix":"955ebc1518a36aa7","entry":"npmat2euler","repo":"supersyq/rienet","repo_kind":"official","path":"util.py","file_url":"https://github.com/supersyq/rienet/blob/HEAD/util.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":"955ebc1518a36aa7"}},{"code_sha256_prefix":"26187fdb33d4f6ed","entry":"pairwise_distance_batch","repo":"supersyq/rienet","repo_kind":"official","path":"utils.py","file_url":"https://github.com/supersyq/rienet/blob/HEAD/utils.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":"26187fdb33d4f6ed"}},{"code_sha256_prefix":"b3efafb434123af6","entry":"random_pose","repo":"supersyq/rienet","repo_kind":"official","path":"data_icl.py","file_url":"https://github.com/supersyq/rienet/blob/HEAD/data_icl.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b3efafb434123af6"}},{"code_sha256_prefix":"d50438ea5f0c80b2","entry":"random_rotation","repo":"supersyq/rienet","repo_kind":"official","path":"data_icl.py","file_url":"https://github.com/supersyq/rienet/blob/HEAD/data_icl.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d50438ea5f0c80b2"}},{"code_sha256_prefix":"421c2d3aa126a3e8","entry":"transform_point_cloud","repo":"supersyq/rienet","repo_kind":"official","path":"util.py","file_url":"https://github.com/supersyq/rienet/blob/HEAD/util.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":"421c2d3aa126a3e8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}