  This work presents an accurate and robust method for estimating normals from
point clouds. In contrast to predecessor approaches that minimize the
deviations between the annotated and the predicted normals directly, leading to
direction inconsistency, we first propose a new metric termed Chamfer Normal
Distance to address this issue. This not only mitigates the challenge but also
facilitates network training and substantially enhances the network robustness
against noise. Subsequently, we devise an innovative architecture that
encompasses Multi-scale Local Feature Aggregation and Hierarchical Geometric
Information Fusion. This design empowers the network to capture intricate
geometric details more effectively and alleviate the ambiguity in scale
selection. Extensive experiments demonstrate that our method achieves the
state-of-the-art performance on both synthetic and real-world datasets,
particularly in scenarios contaminated by noise. Our implementation is
available at https://github.com/YingruiWoo/CMG-Net_Pytorch.
