Neighborhood Multi-compound Transformer for point cloud registration

Point cloud registration is a critical issue in 3D reconstruction and computer vision, particularly challenging in cases of low overlap and different datasets, where algorithm generalization and robustness are pressing challenges. In this paper, we propose a point cloud registration algorithm called Neighborhood Multi-compound Transformer (NMCT). To capture local information, we introduce Neighborhood Position Encoding for the first time. By employing a nearest neighbor approach to select spatial points, this encoding enhances the algorithm's ability to extract relevant local feature information and local coordinate information from dispersed points within the point cloud. Furthermore, NMCT utilizes the Multi-compound Transformer as the interaction module for point cloud information. In this module, the Spatial Transformer phase engages in local-global fusion learning based on Neighborhood Position Encoding, facilitating the extraction of internal features within the point cloud. The Temporal Transformer phase, based on Neighborhood Position Encoding, performs local position-local feature interaction, achieving local and global interaction between two point cloud. The combination of these two phases enables NMCT to better address the complexity and diversity of point cloud data. The algorithm is extensively tested on different datasets (3DMatch, ModelNet, KITTI, MVP-RG), demonstrating outstanding generalization and robustness.