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.
Abstract (translated)
点云配准是3D计算机视觉中的一个基本技术，应用于图形学、自动驾驶和机器人领域。然而，在具有噪声或扰动的环境下，配准任务可能会变得困难。我们提出了一种鲁棒的点云配准方法，它利用图神经 partial differential equations (PDEs) 和热核签名。我们的方法首先使用图神经 PDE 模块从点云中提取高维特征，通过聚合来自3D点邻域的信息来增强特征表示的鲁棒性。然后，我们将热核签名纳入关注机制，以高效地获得相应的关键点。最后，使用带可学习权重的单值分解（SVD）模块预测两个点云之间的变换。在3D点云数据集的实证实验中，我们的方法不仅实现了点云配准的尖端性能，还表现出了对添加噪声或3D形状扰动的鲁棒性更好。
URL
https://arxiv.org/abs/2404.14034
