  Existing normal estimation methods for point clouds are often less robust to
severe noise and complex geometric structures. Also, they usually ignore the
contributions of different neighbouring points during normal estimation, which
leads to less accurate results. In this paper, we introduce a weighted normal
estimation method for 3D point cloud data. We innovate in two key points: 1) we
develop a novel weighted normal regression technique that predicts point-wise
weights from local point patches and use them for robust, feature-preserving
normal regression; 2) we propose to conduct contrastive learning between point
patches and the corresponding ground-truth normals of the patches' central
points as a pre-training process to facilitate normal regression. Comprehensive
experiments demonstrate that our method can robustly handle noisy and complex
point clouds, achieving state-of-the-art performance on both synthetic and
real-world datasets.
