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.

翻译：本文提出了一种精确且鲁棒的点云法向估计方法。不同于以往直接最小化标注与预测法向偏差、易导致方向不一致的方法，我们首次提出了一种名为Chamfer法向距离的新指标来解决该问题。该指标不仅缓解了上述挑战，还有助于网络训练，并显著增强了网络对噪声的鲁棒性。随后，我们设计了一种创新架构，包含多尺度局部特征聚合与层级几何信息融合。该设计使网络能够更有效地捕捉复杂的几何细节，并缓解尺度选择的模糊性。大量实验表明，我们的方法在合成数据集和真实世界数据集上均达到了最先进的性能，尤其在受噪声干扰的场景中表现突出。我们的实现代码已开源：https://github.com/YingruiWoo/CMG-Net_Pytorch。