Global frequency-aware multi-scale feature learning for point cloud normal estimation

Estimating accurate surface normals from point clouds remains a core challenge in three-dimensional (3D) computer vision due to irregular sampling and the difficulty of modeling global geometric context. In this paper, we propose a frequency-domain learning framework that addresses these issues by preserving global information throughout the feature extraction process. Specifically, we introduce a Fourier-Based Multi-Branch Patch Refinement Module at the data level to enhance patch representation with spectral cues, and a Fourier-Based Feature Refinement Layer to integrate local and global geometric features. A multi-scale fusion strategy is further adopted to ensure hierarchical consistency across resolutions. Compared to existing spatial-domain strategies, our method improves global context awareness by incorporating frequency-domain information, effectively mitigating the loss of global features commonly introduced during early-stage local convolutional operations. Experimental results demonstrate consistent performance improvements over prior methods, with gains of 1. 0% on the Point Cloud Property Network (PCPNet) dataset, which is a benchmark for learning local 3D shape properties from raw point clouds, 0. 76% on the Famous Shape dataset (FamousShape), which consists of several well-known 3D mesh models such as the Utah Teapot and Stanford Bunny, and 0. 65% on the Scene Meshes dataset with Annotations (SceneNN), which is a richly annotated collection of indoor 3D scenes.

Authors

• Wei Jin Research Centre for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
• Jun Zhou University of Electronic Science and Technology of China
• Nannan Li
• Xiuping Liu

Keywords

• Point clouds
• Normal estimation
• Fourier convolution
• Multi-scale feature extraction