Local feature guidance framework for robust 3D point cloud registration.

3D point cloud registration is a basic task in computer vision. In recent years, various of learning-based methods have been proposed to solve this problem. These methods effectively overcome the original problem of over-reliance on initial conditions and enhance the ability of obtaining the corresponding relationship. However, few methods pay enough attention to local features and tend to cause some mismatches. Therefore, this paper proposes two networks to extract local features sufficiently. To obtain a more accurate correspondence relationship between the point clouds, we propose a feature weight allocation network (FWANet), in which the expression ability of feature is enhanced using the proposed significant feature extraction module. Besides that, we utilize an interference elimination module to remove the interference points and enhance the internal correlation of point clouds. We also propose a spatial structural generation network (SSGNet), which fully utilizes the spatial location information to determine the spatial correspondence and generate a reliable connection after concatenating multi-dimensional features. At last, a complete feature space can be effectively captured after combining our FWANet with SSGNet together. We conducted multiple experiments on ModelNet40 datasets and achieved excellent results. Experimental results on four types of data demonstrate the superiority of our algorithm against the state-of-the-art ones. Our code will be available at https://github.com/liu-zikang/registration as soon as the paper is accepted. [ABSTRACT FROM AUTHOR]