  Realistic vehicle sensor simulation is an important element in developing
autonomous driving. As physics-based implementations of visual sensors like
LiDAR are complex in practice, data-based approaches promise solutions. Using
pairs of camera images and LiDAR scans from real test drives, GANs can be
trained to translate between them. For this process, we contribute two
additions. First, we exploit the camera images, acquiring segmentation data and
dense depth maps as additional input for training. Second, we test the
performance of the LiDAR simulation by testing how well an object detection
network generalizes between real and synthetic point clouds to enable
evaluation without ground truth point clouds. Combining both, we simulate LiDAR
point clouds and demonstrate their realism.
