# WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving

## TL;DR

WoodScape is the first comprehensive multi-task dataset for fisheye automotive images, supporting various vision tasks to advance autonomous driving research with fisheye cameras.

## Contribution

It introduces the first extensive public fisheye automotive dataset with multi-task annotations, facilitating development of vision algorithms tailored for fisheye imagery.

## Key findings

- Provides over 10,000 images with semantic annotations for 40 classes.
- Includes annotations for depth, detection, and soiling over 100,000 images.
- Encourages development of fisheye-specific computer vision models.

## Abstract

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.