LiSnowNet: Real-time Snow Removal for LiDAR Point Cloud
This addresses a critical challenge for autonomous vehicles by enabling efficient and effective de-noising of point clouds corrupted by snowfall, though it is incremental as it builds on existing de-noising approaches with a new method.
The paper tackles the problem of real-time snow removal from LiDAR point clouds in self-driving vehicles, introducing LiSnowNet, which runs 52× faster than state-of-the-art methods while achieving superior de-noising performance.
LiDARs have been widely adopted to modern self-driving vehicles, providing 3D information of the scene and surrounding objects. However, adverser weather conditions still pose significant challenges to LiDARs since point clouds captured during snowfall can easily be corrupted. The resulting noisy point clouds degrade downstream tasks such as mapping. Existing works in de-noising point clouds corrupted by snow are based on nearest-neighbor search, and thus do not scale well with modern LiDARs which usually capture $100k$ or more points at 10Hz. In this paper, we introduce an unsupervised de-noising algorithm, LiSnowNet, running 52$\times$ faster than the state-of-the-art methods while achieving superior performance in de-noising. Unlike previous methods, the proposed algorithm is based on a deep convolutional neural network and can be easily deployed to hardware accelerators such as GPUs. In addition, we demonstrate how to use the proposed method for mapping even with corrupted point clouds.