Efficient low-overlapping point cloud registration based on two-stage network learning

This letter introduces an efficient registration method for large-scale point clouds with low overlap, which adopts a two-stage transformation learning process. In the first stage, we integrate a point-voxel encoding structure with an attention mechanism to predict rotation and point-wise importance weight. In the second stage, we employ a PointNet-based structure to estimate translation using optimized data. By adopting this two-stage procedure, our model can effectively learn rotation and translation separately, thereby increasing flexibility. We conducted extensive experiments on two large-scale indoor datasets (3Dmatch and S3DIS) and an outdoor dataset (KITTI odometry). The results demonstrate that our method outperforms traditional and deep learning-based approaches, achieving state-of-the-art performance in registration. Furthermore, our method exhibits superior generalization capabilities, highlighting its effectiveness in various registration scenarios.