A Curvature-Guided Fast and Robust Normal Estimation for Point Clouds

Abstract

Accurate normal estimation is a fundamental task in 3D geometry processing, with wide-ranging applications in computer vision, robotics, and computer graphics. However, existing globally consistent normal estimation (GCNO) methods are often limited by reduced accuracy and high computational cost when applied to complex models. To address these challenges, we propose a fast and robust point cloud normal estimation method guided by curvature information. The proposed method integrates curvature as a geometric prior into a global winding-number-based optimization formulation, effectively enhancing normal orientation consistency while preserving sharp geometric features. Furthermore, to improve computational efficiency, we introduce a PCA-based visibility-aware initialization strategy. This strategy adaptively adjusts the initial normal directions by leveraging the local geometric distribution of points, thereby enhancing the consistency of initial normal orientations. Experimental results demonstrate that, compared to the state-of-the-art GCNO method, the proposed approach significantly improves both the accuracy and efficiency of normal estimation. This work provides an effective and precise solution for achieving globally consistent normal estimation in point clouds.