This paper introduces a robust normal vector estimator for point cloud data. It can handle sharp features as well as smooth areas. Our method is based on the inclusion of a robust estimator into a Principal Component Analysis in the neighborhood of the studied point, so that it can detect and reject outliers automatically during the estimation. A projection process ensures robustness against noise. Two automatic initializations are computed, leading to independent optimizations making the algorithm robust to neighborhood anisotropy around sharp features. An evaluation has been carried out in which the algorithm is compared to state-of-the-art methods. The results show that it is more robust against low and/or non-uniform samplings, high noise levels and outliers. Moreover, our algorithm is fast relative to existing methods handling sharp features. The code is available on the website: https://projet.liris.cnrs.fr/pcr/, and integrated in the platform: https://github.com/MEPP-team/MEPP2.

本文介绍了一种针对点云数据的鲁棒法向矢量估计器。它可以处理鲜明的特征以及平滑的区域。我们的方法基于在研究点附近的主成分分析中加入鲁棒估计器，以便在估计过程中可以自动检测和剔除异常值。投影过程确保了抗噪声的鲁棒性。计算了两个自动初始化，从而导致了独立的优化，从而使该算法对于围绕尖锐特征的邻域各向异性具有鲁棒性。已经进行了评估，其中将该算法与最新技术方法进行了比较。结果表明，它对于低和/或非均匀采样，高噪声水平和离群值具有更强的鲁棒性。而且，我们的算法相对于处理尖锐特征的现有方法而言是快速的。