CMG-Net: Robust Normal Estimation for Point Clouds via Chamfer Normal Distance and Multi-Scale Geometry

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.

Authors

• Yingrui Wu MAIS, Institute of Automation, Chinese Academy of Sciences University of Chinese Academy of Sciences, Beijing, China
• Mingyang Zhao Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong, China
• Keqiang Li SenseTime Research, Shanghai, China
• Weize Quan Institute of Automation, Chinese Academy of Sciences
• Tianqi Yu School of Electronic and Information Engineering, Soochow University, Suzhou, China
• Jianfeng Yang School of Electronic and Information Engineering, Soochow University, Suzhou, China
• Xiaohong Jia Lanzhou Jiaotong University
• Dong-Ming Yan Institute of Automation, Chinese Academy of Sciences

Keywords

• CV: 3D Computer Vision
• CV: Scene Analysis & Understanding