De-snowing LiDAR Point Clouds With Intensity and Spatial-Temporal Features
Boyang Li, Jieling Li, Gang Chen, Hejun Wu, Kai Huang
Abstract
Point clouds from 3D light detection and ranging (LiDAR) are widely used. Noise caused by falling snow reduces the availability of point clouds. Due to the sparseness of LiDAR point clouds and the fact that the snow point clouds are easily affected by multi factors such as wind or snowfall conditions, it is difficult to accurately remove the snow while preserving the details of the point clouds. To solve the problem, this paper presents a de-snowing approach combining the intensity and spatial-temporal features. An intensity-based filter firstly removes the snow. Then a repairing method restores the non-snow points based on the spatial-temporal features. Experimental results demonstrate that our approach outperforms existing work in the literature and performs the least damage to the point clouds in different snowfall scenarios.
BibTeX
@inproceedings{icra2022_desnowinglidarpo,
title = {De-snowing LiDAR Point Clouds With Intensity and Spatial-Temporal Features},
author = {Boyang Li and Jieling Li and Gang Chen and Hejun Wu and Kai Huang},
booktitle = {ICRA 2022},
year = {2022}
}