Iteratively weighted principal component analysis and orientation consistency for normal estimation in point cloud

In this paper, we present a method to robustly estimate normal of unorganised point clouds, namely Iterative Weighted Principal Component Analysis (IWPCA). Since the neighbourhood of a point in a smooth region can be well approximated by a plane, the classical Principal Component Analysis (PCA) is a widely used approach for normal estimation. Iterations are applied and bilateral spatial normal weights are introduced in each iteration for the local plane fitting to enhance the reliability of the PCA results. Minimal Spanning Tree (MST) is used to reorient flipped normals. We demonstrate the effectiveness and robustness of the proposed method on a variety of examples. © 2020 Inderscience Enterprises Ltd.