Fast Simultaneous Gravitational Alignment of Multiple Point Sets
This addresses the scalability and robustness challenges in point set alignment for applications like 3D reconstruction or medical imaging, though it is incremental as it builds on existing methods.
The paper tackles the problem of simultaneous rigid alignment of multiple unordered point sets without bias, proposing a method that is robust to noise and missing data and scales to large sets (e.g., 10^5 points), outperforming baselines in accuracy and runtime.
The problem of simultaneous rigid alignment of multiple unordered point sets which is unbiased towards any of the inputs has recently attracted increasing interest, and several reliable methods have been newly proposed. While being remarkably robust towards noise and clustered outliers, current approaches require sophisticated initialisation schemes and do not scale well to large point sets. This paper proposes a new resilient technique for simultaneous registration of multiple point sets by interpreting the latter as particle swarms rigidly moving in the mutually induced force fields. Thanks to the improved simulation with altered physical laws and acceleration of globally multiply-linked point interactions with a 2^D-tree (D is the space dimensionality), our Multi-Body Gravitational Approach (MBGA) is robust to noise and missing data while supporting more massive point sets than previous methods (with 10^5 points and more). In various experimental settings, MBGA is shown to outperform several baseline point set alignment approaches in terms of accuracy and runtime. We make our source code available for the community to facilitate the reproducibility of the results.