This paper presents an end-to-end differentiable algorithm for robust and detail-preserving surface normal estimation on unstructured point-clouds. We utilize graph neural networks to iteratively parameterize an adaptive anisotropic kernel that produces point weights for weighted least-squares plane fitting in local neighborhoods. The approach retains the interpretability and efficiency of traditional sequential plane fitting while benefiting from adaptation to data set statistics through deep learning. This results in a state-of-the-art surface normal estimator that is robust to noise, outliers and point density variation, preserves sharp features through anisotropic kernels and equivariance through a local quaternion-based spatial transformer. Contrary to previous deep learning methods, the proposed approach does not require any hand-crafted features or preprocessing. It improves on the state-of-the-art results while being more than two orders of magnitude faster and more parameter efficient.

中文翻译：

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深度迭代表面法线估计

本文提出了一种端到端的可微算法，用于对非结构化点云进行鲁棒且保留细节的表面法线估计。我们利用图神经网络迭代参数化自适应各向异性内核，该内核为局部邻域中的加权最小二乘平面拟合生成点权重。该方法保留了传统顺序平面拟合的可解释性和效率，同时受益于通过深度学习对数据集统计的适应。这产生了最先进的表面法线估计器，它对噪声、异常值和点密度变化具有鲁棒性，通过各向异性内核保留清晰特征，并通过基于局部四元数的空间变换器保持等方差。与之前的深度学习方法相反，所提出的方法不需要任何手工制作的特征或预处理。它改进了最先进的结果，同时速度提高了两个数量级以上，参数效率更高。