{"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/multiway-point-cloud-mosaicking-with","title":"Multiway Point Cloud Mosaicking with Diffusion and Global Optimization","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Shengze Jin","Iro Armeni","Marc Pollefeys","Daniel Barath"],"abstract":"    We introduce a novel framework for multiway point cloud mosaicking (named Wednesday) designed to co-align sets of partially overlapping point clouds -- typically obtained from 3D scanners or moving RGB-D cameras -- into a unified coordinate system. At the core of our approach is ODIN a learned pairwise registration algorithm that iteratively identifies overlaps and refines attention scores employing a diffusion-based process for denoising pairwise correlation matrices to enhance matching accuracy. Further steps include constructing a pose graph from all point clouds performing rotation averaging a novel robust algorithm for re-estimating translations optimally in terms of consensus maximization and translation optimization. Finally the point cloud rotations and positions are optimized jointly by a diffusion-based approach. Tested on four diverse large-scale datasets our method achieves state-of-the-art pairwise and multiway registration results by a large margin on all benchmarks. Our code and models are available at https://github.com/jinsz/Multiway-Point-Cloud-Mosaicking-with-Diffusion-and-Global-Optimization.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Jin_Multiway_Point_Cloud_Mosaicking_with_Diffusion_and_Global_Optimization_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Jin_Multiway_Point_Cloud_Mosaicking_with_Diffusion_and_Global_Optimization_CVPR_2024_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":"multiway-point-cloud-mosaicking-with","repo_url":"https://github.com/jinsz/multiway-point-cloud-mosaicking-with-diffusion-and-global-optimization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"global-optimization","task_name":"global-optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}