  In this paper, we present a novel framework to project automotive radar
range-Doppler (RD) spectrum into camera image. The utilized warping operation
is designed to be fully differentiable, which allows error backpropagation
through the operation. This enables the training of neural networks (NN)
operating exclusively on RD spectrum by utilizing labels provided from camera
vision models. As the warping operation relies on accurate scene flow,
additionally, we present a novel scene flow estimation algorithm fed from
camera, lidar and radar, enabling us to improve the accuracy of the warping
operation. We demonstrate the framework in multiple applications like
direction-of-arrival (DoA) estimation, target detection, semantic segmentation
and estimation of radar power from camera data. Extensive evaluations have been
carried out for the DoA application and suggest superior quality for NN based
estimators compared to classical estimators. The novel scene flow estimation
approach is benchmarked against state-of-the-art scene flow algorithms and
outperforms them by roughly a third.
