ResearchDoom and CocoDoom: Learning Computer Vision with Games

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 forma...

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Bibliographic Details
Published in:arXiv.org 2016-10
Main Authors: Mahendran, A, Bilen, H, Henriques, J F, Vedaldi, A
Format: Article
Language:English
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Computer vision
Motion simulation
Object recognition
Segmentation
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Summary: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.
ISSN:2331-8422