LiSnowNet: Real-time Snow Removal for LiDAR Point Cloud

TL;DR

LiSnowNet is a fast, deep learning-based method for real-time snow removal from LiDAR point clouds, significantly improving de-noising efficiency and enabling better mapping in snowy conditions for autonomous vehicles.

Contribution

The paper introduces LiSnowNet, a novel deep convolutional neural network that performs unsupervised snow removal from LiDAR data at real-time speeds, outperforming existing methods in efficiency and effectiveness.

Findings

52× faster than previous methods

Superior de-noising performance

Enables accurate mapping in snowy weather

Abstract

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.