We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clouds. Almost all existing methods estimate oriented normals through a two-stage pipeline, i.e., unoriented normal estimation and normal orientation, and each step is implemented by a separate algorithm. However, previous methods are sensitive to parameter settings, resulting in poor results from point clouds with noise, density variations and complex geometries. In this work, we introduce signed hyper surfaces (SHS), which are parameterized by multi-layer perceptron (MLP) layers, to learn to estimate oriented normals from point clouds in an end-to-end manner. The signed hyper surfaces are implicitly learned in a high-dimensional feature space where the local and global information is aggregated. Specifically, we introduce a patch encoding module and a shape encoding module to encode a 3D point cloud into a local latent code and a global latent code, respectively. Then, an attention-weighted normal prediction module is proposed as a decoder, which takes the local and global latent codes as input to predict oriented normals. Experimental results show that our SHS-Net outperforms the state-of-the-art methods in both unoriented and oriented normal estimation on the widely used benchmarks. The code, data and pretrained models are publicly available.

翻译：我们提出了一种名为SHS-Net的新方法，通过学习符号超曲面实现点云的法向定向估计，该方法能够从各种点云中准确预测具有全局一致方向的法向量。现有方法几乎均采用两阶段流程估计定向法向，即无向法向估计与法向定向，且每个步骤由独立算法实现。然而，先前方法对参数设置敏感，导致在含有噪声、密度变化及复杂几何结构的点云上效果不佳。本研究引入由多层感知机参数化的符号超平面，以端到端方式学习从点云估计定向法向。符号超曲面在高维特征空间中隐式学习，其中局部与全局信息得以聚合。具体而言，我们提出补丁编码模块与形状编码模块，分别将三维点云编码为局部隐变量与全局隐变量。随后，设计基于注意力加权的法向预测模块作为解码器，以局部与全局隐变量为输入预测定向法向。实验结果表明，在广泛使用的基准数据集上，SHS-Net在无向与定向法向估计任务中均优于现有最优方法。代码、数据及预训练模型已公开发布。