FAHNet: Accurate and Robust Normal Estimation for Point Clouds via Frequency-Aware Hierarchical Geometry
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Point cloud normal estimation underpins many 3D vision and graphics applications. Precise normal estimation in regions of sharp curvature and high-frequency variation remains a major bottleneck; existing learning-based methods still struggle to isolate fine geometry details under noise and uneven sampling. We present FAHNet, a novel frequency-aware hierarchical network that precisely tackles those challenges. Our Frequency-Aware Hierarchical Geometry (FAHG) feature extraction module selectively amplifies and merges cross-scale cues, ensuring that both fine-grained local features and sharp structures are faithfully represented. Crucially, a dedicated Frequency-Aware geometry enhancement (FA) branch intensifies sensitivity to abrupt normal transitions and sharp features, preventing the common over-smoothing limitation. Extensive experiments on synthetic benchmarks (PCPNet, FamousShape) and real-world scans (SceneNN) demonstrate that FAHNet outperforms state-of-the-art approaches in normal estimation accuracy. Ablation studies further quantify the contribution of each component, and downstream surface reconstruction results validate the practical impact of our design.
Description
CCS Concepts: Computing methodologies → Point-based models; Parametric curve and surface models
@article{10.1111:cgf.70264,
journal = {Computer Graphics Forum},
title = {{FAHNet: Accurate and Robust Normal Estimation for Point Clouds via Frequency-Aware Hierarchical Geometry}},
author = {Wang, Chengwei and Wu, Wenming and Fei, Yue and Zhang, Gaofeng and Zheng, Liping},
year = {2025},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.70264}
}