PointDifformer: robust point cloud registration with neural diffusion and transformer

• Main Authors: She, Rui, Kang, Qiyu, Wang, Sijie, Tay, Wee Peng, Zhao, Kai, Song, Yang, Geng, Tianyu, Xu, Yi, Navarro, Diego Navarro, Hartmannsgruber, Andreas
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Computer and Information Science; Graph neural network; Heat kernel signature
• Online Access: https://hdl.handle.net/10356/175418
• Institution: Nanyang Technological University

Point cloud registration is a fundamental technique in 3-D computer vision with applications in graphics, autonomous driving, and robotics. However, registration tasks under challenging conditions, under which noise or perturbations are prevalent, can be difficult. We propose a robust point cloud registration approach that leverages graph neural partial differential equations (PDEs) and heat kernel signatures. Our method first uses graph neural PDE modules to extract high-dimensional features from point clouds by aggregating information from the 3-D point neighborhood, thereby enhancing the robustness of the feature representations. Then, we incorporate heat kernel signatures into an attention mechanism to efficiently obtain corresponding keypoints. Finally, a singular value decomposition (SVD) module with learnable weights is used to predict the transformation between two point clouds. Empirical experiments on a 3-D point cloud dataset demonstrate that our approach not only achieves state-of-the-art performance for point cloud registration but also exhibits better robustness to additive noise or 3-D shape perturbations.