Robust point cloud registration using Hough voting-based correspondence outlier rejection

In this paper, we present a novel method for point cloud registration in large-scale 3D scenes. Our approach is accurate and robust, and does not rely on unrealistic assumptions. We address the challenges posed by scanning equipment like LiDAR, which often produce point clouds with dense properties. Additionally, our method is effective even in scenes with low overlap rates, specifically less than 30%. Our approach begins by computing overlap region -based correspondences. This involves extracting deep geometric features from point cloud pairs, which is especially beneficial in enhancing registration performance in cases with low overlap ratios. We then construct efficient triplets that vote in the 6D Hough space, representing the transformation parameters. This process involves creating a quartet from overlap region -based correspondences and then forming a final triplet following a sampling process. To mitigate ambiguity during training, we use similarity values of the triplet as features of each vote when configuring votes for network input. Our framework incorporates the architecture of the Fully Convolutional Geometric Features (FCGF) network, augmented with a transformer's attention mechanism, to reduce noise in the voting process. The final stage involves identifying the consensus of correspondence in the Hough space using a binning approach, which enables us to predict the final transformation parameters. Our method has demonstrated stateof-the-art performance on indoor datasets, including high overlap ratio data like 3DMatch and low overlap ratio data like 3DLoMatch. It has also shown comparable performance to leading methods on outdoor datasets like KITTI.