SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving
This provides a resource for developing multi-camera algorithms in autonomous driving, but it is incremental as it extends an existing dataset.
The authors tackled the lack of ground truth for pixel-wise optical flow and depth, as well as simultaneous multi-camera annotations, in existing fisheye surround-view datasets for autonomous driving by releasing SynWoodScape, a synthetic dataset with 80k images and annotations for over 10 tasks.
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° field of view cover the 360° 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.