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.

使用图神经偏微分方程和热核签名的鲁棒点云配准方法，在三维计算机视觉中具有应用于图形、自动驾驶和机器人技术等领域的基础技术。实验结果表明，该方法不仅在点云配准方面取得了最先进的性能，而且对于加性噪声或三维形状扰动具有更好的鲁棒性。

PointDifformer：基于神经扩散和Transformer的鲁棒点云配准