MS-Transformer: Masked and Sparse Transformer for Point Cloud Registration.

In this paper, we propose a masked and sparse transformer to address the problem of point cloud registration with low overlap. The mask mechanism reduces the overall data, increasing the corresponding point ratio in the overlap region, while also reducing the computational cost to accelerate the algorithm's execution speed. Moreover, we combine spatial position encoding and sparse self-attention to establish relationships within the source point cloud, as well as the relationships and attention scores between the source and target point clouds. This approach is specifically designed for the task of point cloud registration. Finally, we search for the maximum overlap area by matching the spatial consistency between points and calculate the 3D transformation matrix to complete the registration process. Our method achieves an improvement in the inlier ratio and performs well on the 3DMatch and 3DLoMatch datasets, demonstrating high registration efficiency.