Generative Adversarial Synthesis of Radar Point Cloud Scenes

Abstract:
For the validation and verification of automotive radars, datasets of realistic traffic scenarios are required, which, how-ever, are laborious to acquire. In this paper, we introduce radar scene synthesis using GANs as an alternative to the real dataset acquisition and simulation-based approaches. We train a PointNet++ based GAN model to generate realistic radar point cloud scenes and use a binary classifier to evaluate the performance of scenes generated using this model against a test set of real scenes. We demonstrate that our GAN model achieves similar performance (~87%) to the real scenes test set.