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ResearchDoom and CocoDoom: Learning Computer Vision with Games
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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.
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Cited by 1 Pith paper
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Enhancing Reinforcement Learning in 3D Environments through Semantic Segmentation: A Case Study in ViZDoom
Semantic-segmentation masks can replace RGB input to ViZDoom RL agents with comparable performance and far lower buffer memory, and adding them as an extra channel improves frag scores.
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