Automotive Radar Point Cloud Parametric Density Estimation using Camera Images

The scarcity of automotive radar datasets is a hindrance in the adoption of machine learning-based radar solutions for assisted and autonomous driving systems. In contrast, there is a larger availability of camera images under different driving environments. In this paper, we develop a radar point cloud data emulation methodology to estimate the radar point cloud probability density function (PDF) corresponding to co-centered camera images. Our method uses convolutional neural networks (CNNs) trained on camera images and available radar point cloud data to predict a set of Gaussian mixture model (GMM) parameters. The resulting parametric PDF model can then be used to emulate radar point clouds for a given scene, and also to produce a high-density radar point cloud dataset. Qualitative and quantitative evaluation of the proposed method on the RADIal dataset shows promising results in synthesizing realistic radar point clouds from the corresponding camera images.