In this short note we introduce ResearchDoom, an implementation of the Doom first-person shooter that can extract detailed metadata from the game. We also introduce the CocoDoom dataset, a collection of pre-recorded data extracted from Doom gaming sessions along with annotations in the MS Coco format. ResearchDoom and CocoDoom can be used to train and evaluate a variety of computer vision methods such as object recognition, detection and segmentation at the level of instances and categories, tracking, ego-motion estimation, monocular depth estimation and scene segmentation. The code and data are available at http://www.robots.ox.ac.uk/~vgg/research/researchdoom.
@article{arxiv.1610.02431,
title = {ResearchDoom and CocoDoom: Learning Computer Vision with Games},
author = {A. Mahendran and H. Bilen and J. F. Henriques and A. Vedaldi},
journal= {arXiv preprint arXiv:1610.02431},
year = {2016}
}