Towards Realistic LiDAR Intensity Simulation in Snowy Weather Using Physics-Informed Learning

Simulating realistic LiDAR intensity is essential for autonomous driving, particularly under snow conditions, where current methods fail to capture complex LiDAR-to-atmosphere interactions. This paper introduces a CycleGAN framework guided by physics, which incorporates the principles of LiDAR intensity attenuation in snowy weather, significantly narrowing the simulation-to-reality gap. The model was evaluated using an open-source real snow dataset and an open-source simulated dataset, demonstrating its ability to replicate real-world intensity patterns with high accuracy, as indicated by metrics like Structural Similarity Index Measure (SSIM), Kullback-Leibler (KL) Divergence, etc. In the downstream semantic segmentation task, models trained on the enhanced data outperformed those trained on baseline datasets, underscoring the framework's effectiveness in improving LiDAR data realism and robustness in snow-weather autonomous driving scenarios.