Denoising Algorithm Based on Local Distance Weighted Statistics for Photon Counting Lidar Point Data

Abstract

The background noise of the photon counting laser point cloud data is large, and the distribution of point cloud density is uneven. In this paper, a point cloud denoising algorithm based on local distance weighted statistics is proposed to solve the problem that the noise point and non-noise are difficult to distinguish when the density of point cloud is low. By adding the weight function, the density difference between noise points and non-noise points is increased when the density of point cloud is low. This method can effectively remove the noise points and thus extract a continuous and complete effective point cloud. Besides, this paper compered the proposed method with the traditional point cloud denoising algorithm based on local distance statistics to verify the efficiency of the proposed algorithm. The results show that the algorithm in this paper has more advantages in dealing with the uneven density of laser point cloud.