2019/05/04 by Senthil Yogamani, Ciaran Hughes, Yogamani, Senthil +31 · 22 citations
Computer Science · Mathematics · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Advanced Vision and Imaging #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Robotics (cs.RO) #cs.AI #cs.CV #cs.LG #cs.RO #stat.ML
paper · pdf · doi:10.48550/arxiv.1905.01489
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
openalex publication_date 2019/05/04 · openalex created_date 2020/10/01 · arxiv created 2021/07/02 · arxiv updated 2021/07/06 · openalex updated_date 2026/07/28
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.