  The millimeter-wave radar sensor maintains stable performance under adverse
environmental conditions, making it a promising solution for all-weather
perception tasks, such as outdoor mobile robotics. However, the radar point
clouds are relatively sparse and contain massive ghost points, which greatly
limits the development of mmWave radar technology. In this paper, we propose a
novel point cloud super-resolution approach for 3D mmWave radar data, named
Radar-diffusion. Our approach employs the diffusion model defined by
mean-reverting stochastic differential equations(SDE). Using our proposed new
objective function with supervision from corresponding LiDAR point clouds, our
approach efficiently handles radar ghost points and enhances the sparse mmWave
radar point clouds to dense LiDAR-like point clouds. We evaluate our approach
on two different datasets, and the experimental results show that our method
outperforms the state-of-the-art baseline methods in 3D radar super-resolution
tasks. Furthermore, we demonstrate that our enhanced radar point cloud is
capable of downstream radar point-based registration tasks.
