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 this https URL

该论文提出了一种准确且强大的方法来从点云中估计法线。通过提出 Chamfer 法线距离这一新的度量标准，我们解决了之前直接最小化注释和预测法线之间偏差所导致的方向不一致问题，并提高了网络训练和鲁棒性。此外，我们设计了一种创新的架构，包括多尺度局部特征聚合和分级几何信息融合。这种设计使网络能够更有效地捕捉复杂的几何细节，并减轻尺度选择中的歧义。广泛的实验证明，我们的方法在合成和真实数据集上均取得了最先进的性能，尤其是在受噪声污染的场景中。我们的实现可在此 https URL 上获得。