Multiway Point Cloud Mosaicking with Diffusion and Global Optimization
<|reference_start|>Multiway Point Cloud Mosaicking with Diffusion and Global Optimization: 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.<|reference_end|>
@article{jin2024multiway,
    title={Multiway Point Cloud Mosaicking with Diffusion and Global Optimization},
    author={Shengze Jin (Department of Computer Science, ETH Zurich, Switzerland),
  Iro Armeni (Department of Civil and Environmental Engineering, Stanford
  University), Marc Pollefeys (Department of Computer Science, ETH Zurich,
  Switzerland and Microsoft Mixed Reality & AI Lab, Zurich, Switzerland) and
  Daniel Barath (Department of Computer Science, ETH Zurich, Switzerland)},
    journal={arXiv preprint arXiv:2404.00429},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00429},
    primaryClass={cs.CV}
}