Robust point cloud normal estimation via neighborhood reconstruction

This article presents a novel method for estimating normals on unorganized point clouds that preserves sharp features. Many existing methods are unable to reliably estimate normals for points around sharp features since the neighborhood employed for the normal estimation would enclose points belonging to different surface patches across the sharp feature. To address this challenging issue, a neighborhood reconstruction-based normal estimation method is developed to find a proper neighborhood for points around sharp features. A robust statistics-based method is proposed to identify points that near sharp features and classify the points into two categories: edge points and non-edge points. Two specific neighborhood reconstruction strategies are designed for these two types of points to generate a neighborhood clear of sharp features. The normal of the current point can thus be reliably estimated by principal component analysis using the generated isotropic neighborhood. Numerous case studies have been carried out to compare the reliability and robustness of the proposed method against various existing methods. The experiment results show that the proposed approach performs better than the state-of-the-art methods most of the time and offers an ideal compromise between precision, speed, and robustness.