  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.
