WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving
This dataset addresses a gap for researchers and developers in autonomous driving by providing a resource to adapt models for fisheye cameras, though it is incremental as it builds on existing dataset efforts.
The authors tackled the lack of public datasets for evaluating computer vision algorithms on fisheye images in autonomous driving by releasing WoodScape, a multi-task, multi-camera dataset with over 100,000 annotated images across nine tasks, including semantic segmentation for 40 classes on over 10,000 images.
Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications. In spite of their prevalence, there are few public datasets for detailed evaluation of computer vision algorithms on fisheye images. We release the first extensive fisheye automotive dataset, WoodScape, named after Robert Wood who invented the fisheye camera in 1906. WoodScape comprises of four surround view cameras and nine tasks including segmentation, depth estimation, 3D bounding box detection and soiling detection. Semantic annotation of 40 classes at the instance level is provided for over 10,000 images and annotation for other tasks are provided for over 100,000 images. With WoodScape, we would like to encourage the community to adapt computer vision models for fisheye camera instead of using naive rectification.