  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.
