  Semantic Scene Completion (SSC) is pivotal in autonomous driving perception,
frequently confronted with the complexities of weather and illumination
changes. The long-term strategy involves fusing multi-modal information to
bolster the system's robustness. Radar, increasingly utilized for 3D target
detection, is gradually replacing LiDAR in autonomous driving applications,
offering a robust sensing alternative. In this paper, we focus on the potential
of 3D radar in semantic scene completion, pioneering cross-modal refinement
techniques for improved robustness against weather and illumination changes,
and enhancing SSC performance.Regarding model architecture, we propose a
three-stage tight fusion approach on BEV to realize a fusion framework for
point clouds and images. Based on this foundation, we designed three
cross-modal distillation modules-CMRD, BRD, and PDD. Our approach enhances the
performance in both radar-only (R-LiCROcc) and radar-camera (RC-LiCROcc)
settings by distilling to them the rich semantic and structural information of
the fused features of LiDAR and camera. Finally, our LC-Fusion (teacher model),
R-LiCROcc and RC-LiCROcc achieve the best performance on the nuScenes-Occupancy
dataset, with mIOU exceeding the baseline by 22.9%, 44.1%, and 15.5%,
respectively. The project page is available at
https://hr-zju.github.io/LiCROcc/.
