{"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/robust-outlier-rejection-for-3d-registration","title":"Robust Outlier Rejection for 3D Registration with Variational Bayes","arxiv_id":"2304.01514","date":"2023-04-04","proceeding":"CVPR 2023 1","authors":["Haobo Jiang","Zheng Dang","Zhen Wei","Jin Xie","Jian Yang","Mathieu Salzmann"],"abstract":"Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier feature representations. In this paper, we develop a novel variational non-local network-based outlier rejection framework for robust alignment. By reformulating the non-local feature learning with variational Bayesian inference, the Bayesian-driven long-range dependencies can be modeled to aggregate discriminative geometric context information for inlier/outlier distinction. Specifically, to achieve such Bayesian-driven contextual dependencies, each query/key/value component in our non-local network predicts a prior feature distribution and a posterior one. Embedded with the inlier/outlier label, the posterior feature distribution is label-dependent and discriminative. Thus, pushing the prior to be close to the discriminative posterior in the training step enables the features sampled from this prior at test time to model high-quality long-range dependencies. Notably, to achieve effective posterior feature guidance, a specific probabilistic graphical model is designed over our non-local model, which lets us derive a variational low bound as our optimization objective for model training. Finally, we propose a voting-based inlier searching strategy to cluster the high-quality hypothetical inliers for transformation estimation. Extensive experiments on 3DMatch, 3DLoMatch, and KITTI datasets verify the effectiveness of our method.","url_abs":"https://arxiv.org/abs/2304.01514v1","url_pdf":"https://arxiv.org/pdf/2304.01514v1.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":"robust-outlier-rejection-for-3d-registration","repo_url":"https://github.com/jiang-hb/vbreg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2304.01514","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.01514"}},"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":"deterministic:regex_extraction","url":"https://github.com/Jiang-HB/VBReg","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_draft_wrong":1,"ran_fixture":1,"ran_violates":1,"unverified":5},"by_repo_kind":{"official":{"samples":8,"ran":3,"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":"6d80592fd6d47b2d","entry":"add_argument_group","repo":"Jiang-HB/VBReg","repo_kind":"official","path":"config.py","file_url":"https://github.com/Jiang-HB/VBReg/blob/HEAD/config.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6d80592fd6d47b2d"}},{"code_sha256_prefix":"69f64c231bba4224","entry":"knn","repo":"Jiang-HB/VBReg","repo_kind":"official","path":"models/common.py","file_url":"https://github.com/Jiang-HB/VBReg/blob/HEAD/models/common.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":"69f64c231bba4224"}},{"code_sha256_prefix":"248284f69adfeaad","entry":"str2bool","repo":"Jiang-HB/VBReg","repo_kind":"official","path":"config.py","file_url":"https://github.com/Jiang-HB/VBReg/blob/HEAD/config.py","link_basis":"harvester_set","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"248284f69adfeaad"}},{"code_sha256_prefix":"e3721a7be4cab736","entry":"collate_fn","repo":"Jiang-HB/VBReg","repo_kind":"official","path":"datasets/dataloader.py","file_url":"https://github.com/Jiang-HB/VBReg/blob/HEAD/datasets/dataloader.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":"e3721a7be4cab736"}},{"code_sha256_prefix":"838a215876e8ac3b","entry":"get_dataloader","repo":"Jiang-HB/VBReg","repo_kind":"official","path":"datasets/dataloader.py","file_url":"https://github.com/Jiang-HB/VBReg/blob/HEAD/datasets/dataloader.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":"838a215876e8ac3b"}},{"code_sha256_prefix":"8387ea2cee3b7fe2","entry":"load_data","repo":"Jiang-HB/VBReg","repo_kind":"official","path":"datasets/ThreeDMatch.py","file_url":"https://github.com/Jiang-HB/VBReg/blob/HEAD/datasets/ThreeDMatch.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":"8387ea2cee3b7fe2"}},{"code_sha256_prefix":"9832193a0c7cb463","entry":"load_obj","repo":"Jiang-HB/VBReg","repo_kind":"official","path":"datasets/ThreeDMatch.py","file_url":"https://github.com/Jiang-HB/VBReg/blob/HEAD/datasets/ThreeDMatch.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":"9832193a0c7cb463"}},{"code_sha256_prefix":"c4f095a505b61bcd","entry":"rigid_transform_3d","repo":"Jiang-HB/VBReg","repo_kind":"official","path":"models/common.py","file_url":"https://github.com/Jiang-HB/VBReg/blob/HEAD/models/common.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":"c4f095a505b61bcd"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}