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.
@article{arxiv.1905.01489,
title = {WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving},
author = {Senthil Yogamani and Ciaran Hughes and Jonathan Horgan and Ganesh Sistu and Padraig Varley and Derek O'Dea and Michal Uricar and Stefan Milz and Martin Simon and Karl Amende and Christian Witt and Hazem Rashed and Sumanth Chennupati and Sanjaya Nayak and Saquib Mansoor and Xavier Perroton and Patrick Perez},
journal= {arXiv preprint arXiv:1905.01489},
year = {2021}
}
Comments
Accepted for Oral Presentation at IEEE International Conference on Computer Vision (ICCV) 2019. Please refer to our website https://woodscape.valeo.com and https://github.com/valeoai/woodscape for release status and updates