{"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/dv-matcher-deformation-based-non-rigid-point","title":"DV-Matcher: Deformation-based Non-rigid Point Cloud Matching Guided by Pre-trained Visual Features","arxiv_id":null,"date":"2025-01-01","proceeding":"CVPR 2025 1","authors":["Zhangquan Chen","Puhua Jiang","Ruqi Huang"],"abstract":"    In this paper, we present DV-Matcher, a novel learning-based framework for estimating dense correspondences between non-rigidly deformable point clouds. Learning directly from unstructured point clouds without meshing or manual labelling, our framework delivers high-quality dense correspondences, which is of significant practical utility in point cloud processing. Our key contributions are two-fold: First, we propose a scheme to inject prior knowledge from pre-trained vision models into geometric feature learning, which effectively complements the local nature of geometric features with global and semantic information; Second, we propose a novel deformation-based module to promote the extrinsic alignment induced by the learned correspondences, which effectively enhances the feature learning. Experimental results show that our method achieves state-of-the-art results in matching non-rigid point clouds in both near-isometric and heterogeneous shape collection as well as more realistic partial and noisy data. Our code is available at https://github.com/rqhuang88/DV-Matcher.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2025/html/Chen_DV-Matcher_Deformation-based_Non-rigid_Point_Cloud_Matching_Guided_by_Pre-trained_Visual_CVPR_2025_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2025/papers/Chen_DV-Matcher_Deformation-based_Non-rigid_Point_Cloud_Matching_Guided_by_Pre-trained_Visual_CVPR_2025_paper.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":"dv-matcher-deformation-based-non-rigid-point","repo_url":"https://github.com/rqhuang88/dv-matcher","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}