A novel method for registration of MLS and stereo reconstructed point clouds

Cross-source point cloud registration is a prerequisite for effectively leveraging the complementary information of multiple 3D sensors. However, existing point cloud registration methods have primarily focused on the registration of mono-source point clouds and typically fail to register cross-source data with varying noise patterns and capture characteristics. In this paper, we present a new algorithm for cross-source point cloud registration between MLS point clouds and stereo-reconstructed point clouds. Our method has two key designs. Firstly, we design a novel descriptor with in-plane rotation-equivariance by leveraging the accessible gravity prior, yielding strong descriptiveness, better robustness, and improved efficiency. Secondly, based on the noise pattern of stereo-reconstructed point clouds, a novel disparity-weighted correspondence scoring strategy is proposed to strengthen the registration accuracy. In comparison to existing registration baselines, our method achieves a 32.6% higher Registration Recall on cross-source datasets of KITTI and KITTI-360 and a 23.1% higher Registration Recall on mono-source datasets of KITTI. Notably, our method also outperforms RANSAC-based methods in terms of computational efficiency with a 10× ~ 70× speedup. The source code and datasets have been available at https://github.com/WHU-USI3DV/MSReg.