Pairwise Registration

Pairwise registration aims to find the optimal transformation aligning two partially overlapping datasets, such as 3D point clouds or digital surface models. Current research focuses on improving robustness to noise, outliers, and limited overlap, employing techniques like gradient-SDF, motion averaging, and neural network-based approaches for feature extraction and overlap estimation. These advancements are crucial for applications ranging from computer-assisted surgery, where precise bone alignment is vital, to large-scale 3D scene reconstruction using satellite imagery or neural radiance fields, enabling more accurate and efficient processing of complex datasets. The development of faster and more accurate registration methods is driving progress in various fields requiring precise 3D model alignment.