School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China.
Sichuan Province Informationization Application Support Software Engineering Technology Research Center, Chengdu 610103, China.
Sensors (Basel). 2023 Mar 20;23(6):3292. doi: 10.3390/s23063292.
This paper introduces a robust normal estimation method for point cloud data that can handle both smooth and sharp features. Our method is based on the inclusion of neighborhood recognition into the normal mollification process in the neighborhood of the current point: First, the point cloud surfaces are assigned normals via a normal estimator of robust location (NERL), which guarantees the reliability of the smooth region normals, and then a robust feature point recognition method is proposed to identify points around sharp features accurately. Furthermore, Gaussian maps and clustering are adopted for feature points to seek a rough isotropic neighborhood for the first-stage normal mollification. In order to further deal with non-uniform sampling or various complex scenes efficiently, the second-stage normal mollification based on residual is proposed. The proposed method was experimentally validated on synthetic and real-world datasets and compared to state-of-the-art methods.
本文介绍了一种稳健的点云数据正态估计方法，该方法既可以处理平滑特征，也可以处理锐利特征。我们的方法基于将邻域识别纳入当前点邻域的正态平滑过程中：首先，通过稳健位置估计器（NERL）对点云表面进行法线分配，这保证了平滑区域法线的可靠性，然后提出了一种稳健特征点识别方法，以准确识别锐利特征周围的点。此外，采用高斯图和聚类方法对特征点进行处理，以寻找第一阶段正态平滑的大致各向同性邻域。为了进一步有效地处理非均匀采样或各种复杂场景，提出了基于残差的第二阶段正态平滑。该方法在合成和真实数据集上进行了实验验证，并与最先进的方法进行了比较。
