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
this https URL

提出了一种新的多通道点云镶嵌框架，其设计用于将部分重叠的点云集合（通常来自 3D 扫描仪或移动 RGB-D 相机）对齐到一个统一的坐标系统；核心算法为 ODIN，它使用扩散过程对相关矩阵进行去噪，并通过迭代识别重叠部分和优化注意力得分来提高匹配精度；进一步的步骤包括构建姿态图，进行旋转平均，使用鲁棒算法重新估计平移以实现共识最大化和优化平移，并最后通过扩散过程联合优化点云的旋转和位置；在四个多样化且大规模的数据集上测试，该方法在所有基准测试中以很大的优势达到了最先进的两两和多通道注册结果。