Introduction

WoodScape comprises four surround-view cameras and nine tasks, including segmentation, depth estimation, 3D bounding box detection, and a novel soiling detection. Semantic annotation of 40 classes at the instance level is provided for over 10,000 images. With WoodScape, we would like to encourage the community to adapt computer vision models for the fisheye camera instead of using naive rectification.

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9
tasks

10k
images

40
classes

Interested in using the same sensors used for recording WoodScape? Check Valeo Mobility Kit.

Teaser

teaser.mp4

Citation

S. Yogamani et al., "WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving," 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 9307-9317, doi: 10.1109/ICCV.2019.00940.


@InProceedings{Yogamani_2019_ICCV,

author = {Yogamani, Senthil and Hughes, Ciaran and Horgan, Jonathan and Sistu, Ganesh and Varley, Padraig and O'Dea, Derek and Uricar, Michal and Milz, Stefan and Simon, Martin and Amende, Karl and Witt, Christian and Rashed, Hazem and Chennupati, Sumanth and Nayak, Sanjaya and Mansoor, Saquib and Perrotton, Xavier and Perez, Patrick},

title = {WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving},

booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},

month = {October},

year = {2019},

doi = {10.1109/ICCV.2019.00940}

}