Snow corrupts LiDAR point clouds with scattered noise points and false objects, posing a serious threat to the perception of autonomous driving systems. Existing effective point cloud de-snow methods are mainly based on outlier filters that rigidly remove isolated points. There are deep-learning and algorithm-based weather models that can handle adverse conditions such as rain and fog, but snow conditions are rarely considered. In this study, we propose a LiDAR point cloud translation model based on refined generative adversarial networks (GANs) that is not only able to de-noise snow in point clouds but also to generate fake snow points on clear data. Our model is trained on depth image representations of point clouds from unpaired datasets, with a customized loss function for grayscale depth images that can maintain scale consistency. A pixel-wise discriminator structure is designed to improve the de-snowing effect around the ego vehicle. The proposed model expresses a better feature capture on snow in LiDAR point clouds, and experiment results show high-quality snow removal performance on both the scattered and clustered snow points, as well as satisfactory fake snow generation on clear road point clouds.
LiDAR Point Cloud Translation Between Snow and Clear Conditions Using Depth Images and GANs
2023-06-04
10754187 byte
Conference paper
Electronic Resource
English
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