{"id":1021203,"date":"2024-04-02T07:01:25","date_gmt":"2024-04-02T14:01:25","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=1021203"},"modified":"2024-05-14T14:59:15","modified_gmt":"2024-05-14T21:59:15","slug":"multiway-point-cloud-mosaicking-with-diffusion-and-global-optimization","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/multiway-point-cloud-mosaicking-with-diffusion-and-global-optimization\/","title":{"rendered":"Multiway Point Cloud Mosaicking with Diffusion and Global Optimization"},"content":{"rendered":"

We introduce a novel framework for multiway point cloud mosaicking (named Wednesday), designed to co-align sets of partially overlapping point clouds \u2013 typically obtained from 3D scanners or moving RGB-D cameras \u2013 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\u00a0https:\/\/github.com\/jinsz\/Multiway-Point-Cloud-Mosaicking-with-Diffusion-and-Global-Optimization<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

We introduce a novel framework for multiway point cloud mosaicking (named Wednesday), designed to co-align sets of partially overlapping point clouds \u2013 typically obtained from 3D scanners or moving RGB-D cameras \u2013 into a unified coordinate system. At the core of our approach is ODIN, a learned pairwise registration algorithm that iteratively identifies overlaps and 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