GenRadar: Self-Supervised Probabilistic Camera Synthesis Based on Radar Frequencies

Abstract

Autonomous systems require a continuous and dependable environment perception for navigation and decision-making, which is best achieved by combining different sensor types. Radar continues to function robustly in compromised circumstances in which cameras become impaired, guaranteeing a steady inflow of information. Yet, camera images provide a more intuitive and readily applicable impression of the world. This work combines the complementary strengths of both sensor types in a unique self-learning fusion approach for a probabilistic scene reconstruction in adverse surrounding conditions. After reducing the memory requirements of both high-dimensional measurements through a decoupled stochastic self-supervised compression technique, the proposed algorithm exploits similarities and establishes correspondences between both domains at different feature levels during training. Then, at inference time, relying exclusively on radio frequencies, the model successively predicts camera constituents in an autoregressive and self-contained process. These discrete tokens are finally transformed back into an instructive view of the respective surrounding, allowing to visually perceive potential dangers for important tasks downstream.

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

“…The decoder generates a radar intensity map in the polar grid conditioned on the encoded feature and random noise. Generative models can also be used in cross-modality data generation, for example GAN-based LiDAR-to-radar generation [104], GAN-based radar-to-image generation [105], and VAE-based radar-to-image generation [106].…”