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.

该研究提出了一种基于图神经网络和自适应各向异性核的表面法线估计算法，不需要任何手动特征或预处理，优于现有深度学习方法，在保留尖锐特征和空间等变性的同时，速度和参数效率均为同类算法的两个数量级。