Normal estimation on 3D point clouds is a fundamental problem in 3D vision and graphics. Current methods often show limited accuracy in predicting normals at sharp features (e.g., edges and corners) and less robustness to noise. In this paper, we propose a novel normal estimation method for point clouds. It consists of two phases: (a) feature encoding which learns representations of local patches, and (b) normal estimation that takes the learned representation as input and regresses the normal vector. We are motivated that local patches on isotropic and anisotropic surfaces have similar or distinct normals, and that separable features or representations can be learned to facilitate normal estimation. To realise this, we first construct triplets of local patches on 3D point cloud data, and design a triplet network with a triplet loss for feature encoding. We then design a simple network with several MLPs and a loss function to regress the normal vector. Despite having a smaller network size compared to most other methods, experimental results show that our method preserves sharp features and achieves better normal estimation results on CAD-like shapes.

翻译：三维点云法线估计是三维视觉与图形学中的基础问题。当前方法在预测尖锐特征（如边缘和角点）处的法线时通常精度有限，且对噪声的鲁棒性不足。本文提出一种新颖的点云法线估计方法，包含两个阶段：（a）特征编码阶段，学习局部块的表征；（b）法线估计阶段，将学习到的表征作为输入并回归法线向量。我们基于以下动机：各向同性与各向异性曲面上的局部块具有相似或相异的法线，因此可学习可分离的特征或表征以促进法线估计。为此，我们首先在三维点云数据上构建局部块三元组，设计带三元组损失的三元组网络进行特征编码；随后设计包含多个多层感知机（MLP）与损失函数的简单网络用于法线向量回归。尽管网络尺寸小于多数现有方法，实验结果表明，本方法能够保留尖锐特征，并在CAD类形状上取得更优的法线估计结果。