Structure-to-Intensity Diffusion for Adverse-Weather LiDAR Generation

Peiyang Ni, Longyu Yang, Lu Zhang, Kuniaki Saito, Yap-Peng Tan, Fumin Shen, Heng Tao Shen, Xiaofeng Zhu, Ping Hu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 35904-35914

Adverse-weather LiDAR point cloud generation is challenged by complex weather-induced degradations. These degradations affect geometry and reflectance in fundamentally different ways, making joint modeling difficult and ambiguous, especially when diverse real-world training data is limited. To address this, we propose Structure-to-Intensity Diffusion (SiD), a diffusion-based framework that explicitly factorizes the denoising process at each time step: it first reconstructs the geometric structure, then conditions reflectance intensity denoising on the estimated structure. This structure-conditioned design decomposes the joint distribution, reduces modeling ambiguity, and leads to point clouds that are both geometrically coherent and radiometrically realistic. To mitigate data scarcity, we introduce Real-Prior Weather Simulation (RPWS), a degradation module that leverages real-world sensor statistics to synthesize physically plausible adverse-weather point clouds from clear scans. Extensive experiments demonstrate that, with similar model complexity, our approach outperforms the previous state-of-the-art in generating adverse-weather LiDAR scans with both structural and radiometric properties more closely aligned with real-world data.