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.

翻译：我们提出了一种名为SHS-Net的新方法，通过学习有向超曲面实现点云的定向法向量估计，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。几乎所有现有方法都通过两阶段流程估计定向法向量，即无向法向量估计和法向量定向，且每个步骤均由独立算法实现。然而，先前的方法对参数设置敏感，导致在处理含噪声、密度变化和复杂几何形状的点云时效果不佳。本工作中，我们引入了由多层感知机参数化的有向超曲面，以端到端方式学习从点云估计定向法向量。有向超曲面在高维特征空间中隐式学习，其中聚合了局部与全局信息。具体而言，我们引入了局部编码模块和形状编码模块，分别将三维点云编码为局部隐码和全局隐码。随后，提出注意力加权法向量预测模块作为解码器，以局部与全局隐码为输入预测定向法向量。实验结果表明，在广泛使用的基准数据集上，我们的SHS-Net在无向和定向法向量估计任务中均优于现有最优方法。