  Surround-view cameras are a primary sensor for automated driving, used for
near-field perception. It is one of the most commonly used sensors in
commercial vehicles primarily used for parking visualization and automated
parking. Four fisheye cameras with a 190{\deg} field of view cover the
360{\deg} around the vehicle. Due to its high radial distortion, the standard
algorithms do not extend easily. Previously, we released the first public
fisheye surround-view dataset named WoodScape. In this work, we release a
synthetic version of the surround-view dataset, covering many of its weaknesses
and extending it. Firstly, it is not possible to obtain ground truth for
pixel-wise optical flow and depth. Secondly, WoodScape did not have all four
cameras annotated simultaneously in order to sample diverse frames. However,
this means that multi-camera algorithms cannot be designed to obtain a unified
output in birds-eye space, which is enabled in the new dataset. We implemented
surround-view fisheye geometric projections in CARLA Simulator matching
WoodScape's configuration and created SynWoodScape. We release 80k images from
the synthetic dataset with annotations for 10+ tasks. We also release the
baseline code and supporting scripts.
