WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving

By Senthil Kumar Yogamani, Ciarán Hughes, Jonathan Horgan, Ganesh Sistu, P. Varley, D. O'Dea, Michal Uřičář, Stefan Milz, Martin Simon, Karl Amende and 7 more. 2019

341 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

Segment Anything (2023, methodology): “-20 0 +20 +40 IoU delta at 1 center point GTEA [34, 63] TrashCan [52] DRAM [24] PIDRay [104] Cityscapes [25] WoodScape [112] IBD [17] EgoHOS [113] Plittersdorf [46] VISOR [28, 27] NDISPark [22, 23] Hypersim [86] OVIS [81] ADE20K [117] iShape [111] ZeroWaste-f [6] STREETS [91] LVIS [44] NDD20 [100] T”
Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100 (2020, background): “This has been recently achieved for autonomous driving [17,18,19,20,21,22,23,24] and scene understanding [25,26].”
Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, neutral): “into a multi-task model [8], [9].”
nuScenes: A Multimodal Dataset for Autonomous Driving (2019, background): “Similar to [84], nuScenes is a holistic scene understanding benchmark for AVs.”
SoilingNet: Soiling Detection on Automotive Surround-View Cameras (2019, background): “duced in our WoodScape dataset paper [15].”
Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review (2021, background): “Other commonly employed cameras in AVs for perception of the surroundings include fisheye cameras [46-48].”
SAM 2: Segment Anything in Images and Videos (2024, neutral): “…(Haucke et al., 2022), STREETS (Snyder & Do, 2019), TimberSeg (Fortin et al., 2022), TrashCan (Hong et al., 2020), VISOR (Darkhalil et al., 2022; Damen et al., 2022), WoodScape (Yogamani et al., 2019), PIDRay (Wang et al., 2021a), ZeroWaste-f (Bashkirova et al., 2022), and IBD (Chen et al., 2022).”
Synthetic Data for Deep Learning (2019, background): “[674] present WoodScape, a multi-camera fisheye dataset for autonomous driving that concentrates on getting 360° sensing around a vehicle through panoramic fisheye images with a large field of view.”
Is Context-Aware CNN Ready for the Surroundings? Panoramic Semantic Segmentation in the Wild (2021, background): “While there are a few wide-angle databases such as TorontoCity [51] and WoodScape [52], their scene variety and annotation density are far lower than pinhole benchmark datasets like Cityscapes [5], Mapillary Vistas [6], IDD [53] and BDD [54], which suppose”