PointDifformer: Robust Point Cloud Registration With Neural Diffusion and Transformer

• URL: http://arxiv.org/abs/2404.14034v1
• Date: Mon, 22 Apr 2024 09:50:12 GMT
• Title: PointDifformer: Robust Point Cloud Registration With Neural Diffusion and Transformer
• Authors: Rui She, Qiyu Kang, Sijie Wang, Wee Peng Tay, Kai Zhao, Yang Song, Tianyu Geng, Yi Xu, Diego Navarro Navarro, Andreas Hartmannsgruber,
• Abstract summary: Point cloud registration is a fundamental technique in 3-D computer vision with applications in graphics, autonomous driving, and robotics. We propose a robust point cloud registration approach that leverages graph neural partial differential equations (PDEs) and heat kernel signatures. 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.
• Score: 31.02661827570958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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