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The key characteristics are the following techniques that exploit 3D positional knowledge for point cloud matching: 1) An architecture that disentangles point cloud representation into feature space and 3D position space. 2) A position encoding method that explicitly reveals 3D relative distance information through the dot product of vectors. 3) A repositioning technique that modifies the crosspoint-cloud relative positions. Ablation studies demonstrate the effectiveness of the above techniques. In rigid cases, Lepard combined with RANSAC and ICP demonstrates state-of-the-art registration recall of 93.9% / 71.3% on the 3DMatch / 3DLoMatch. In deformable cases, Lepard achieves +27.1% / +34.8% higher non-rigid feature matching recall than the prior art on our newly constructed 4DMatch / 4DLoMatch benchmark.","url_abs":"https://arxiv.org/abs/2111.12591v2","url_pdf":"https://arxiv.org/pdf/2111.12591v2.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":"lepard-learning-partial-point-cloud-matching","repo_url":"https://github.com/rabbityl/lepard","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-feature-matching","task_name":"3D Feature Matching"},{"task_slug":"3d-point-cloud-matching","task_name":"3D Point Cloud Matching"},{"task_slug":"partial-point-cloud-matching","task_name":"Partial Point Cloud Matching"},{"task_slug":"point-cloud-registration","task_name":"Point Cloud Registration"},{"task_slug":null,"task_name":"Position"}],"methods":[],"datasets_introduced":[{"slug":"4dmatch","name":"4DMatch","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/partial-point-cloud-matching-on-4dmatch","task":"Partial Point Cloud Matching","dataset":"4DMatch","model":"Li and Harada (θc=0.05)","rank_in_archive_order":1,"of":9,"metrics":{"IR":"80.9","NFMR":"83.9"},"uses_additional_data":false},{"leaderboard":"/sota/partial-point-cloud-matching-on-4dmatch","task":"Partial Point Cloud Matching","dataset":"4DMatch","model":"Li and Harada (θc=0.1)","rank_in_archive_order":2,"of":9,"metrics":{"IR":"82.7","NFMR":"83.7"},"uses_additional_data":false},{"leaderboard":"/sota/partial-point-cloud-matching-on-4dmatch","task":"Partial Point Cloud Matching","dataset":"4DMatch","model":"Li and Harada (θc=0.2)","rank_in_archive_order":3,"of":9,"metrics":{"IR":"85.4","NFMR":"82.2"},"uses_additional_data":false},{"leaderboard":"/sota/partial-point-cloud-matching-on-4dmatch","task":"Partial Point Cloud Matching","dataset":"4DMatch","model":"Predator (5000)","rank_in_archive_order":4,"of":9,"metrics":{"IR":"59.3","NFMR":"56.8"},"uses_additional_data":false},{"leaderboard":"/sota/partial-point-cloud-matching-on-4dmatch","task":"Partial Point Cloud Matching","dataset":"4DMatch","model":"Predator (3000)","rank_in_archive_order":5,"of":9,"metrics":{"IR":"60.4","NFMR":"56.4"},"uses_additional_data":false},{"leaderboard":"/sota/partial-point-cloud-matching-on-4dmatch","task":"Partial Point Cloud Matching","dataset":"4DMatch","model":"D3Feat (5000)","rank_in_archive_order":6,"of":9,"metrics":{"IR":"55.3","NFMR":"56.1"},"uses_additional_data":false},{"leaderboard":"/sota/partial-point-cloud-matching-on-4dmatch","task":"Partial Point Cloud Matching","dataset":"4DMatch","model":"D3Feat (3000)","rank_in_archive_order":7,"of":9,"metrics":{"IR":"54.7","NFMR":"55.5"},"uses_additional_data":false},{"leaderboard":"/sota/partial-point-cloud-matching-on-4dmatch","task":"Partial Point Cloud Matching","dataset":"4DMatch","model":"Predator (1000)","rank_in_archive_order":8,"of":9,"metrics":{"IR":"60","NFMR":"53.3"},"uses_additional_data":false},{"leaderboard":"/sota/partial-point-cloud-matching-on-4dmatch","task":"Partial Point Cloud Matching","dataset":"4DMatch","model":"D3Feat (1000)","rank_in_archive_order":9,"of":9,"metrics":{"IR":"52.7","NFMR":"51.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2111.12591","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.12591"}},"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. 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