  We propose a novel normal estimation method called HSurf-Net, which can
accurately predict normals from point clouds with noise and density variations.
Previous methods focus on learning point weights to fit neighborhoods into a
geometric surface approximated by a polynomial function with a predefined
order, based on which normals are estimated. However, fitting surfaces
explicitly from raw point clouds suffers from overfitting or underfitting
issues caused by inappropriate polynomial orders and outliers, which
significantly limits the performance of existing methods. To address these
issues, we introduce hyper surface fitting to implicitly learn hyper surfaces,
which are represented by multi-layer perceptron (MLP) layers that take point
features as input and output surface patterns in a high dimensional feature
space. We introduce a novel space transformation module, which consists of a
sequence of local aggregation layers and global shift layers, to learn an
optimal feature space, and a relative position encoding module to effectively
convert point clouds into the learned feature space. Our model learns hyper
surfaces from the noise-less features and directly predicts normal vectors. We
jointly optimize the MLP weights and module parameters in a data-driven manner
to make the model adaptively find the most suitable surface pattern for various
points. Experimental results show that our HSurf-Net achieves the
state-of-the-art performance on the synthetic shape dataset, the real-world
indoor and outdoor scene datasets. The code, data and pretrained models are
publicly available.
