{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/researchdoom-and-cocodoom-learning-computer","title":"ResearchDoom and CocoDoom: Learning Computer Vision with Games","arxiv_id":"1610.02431","date":"2016-10-07","proceeding":null,"authors":["A. Mahendran","H. Bilen","J. F. Henriques","A. Vedaldi"],"abstract":"In this short note we introduce ResearchDoom, an implementation of the Doom\nfirst-person shooter that can extract detailed metadata from the game. We also\nintroduce the CocoDoom dataset, a collection of pre-recorded data extracted\nfrom Doom gaming sessions along with annotations in the MS Coco format.\nResearchDoom and CocoDoom can be used to train and evaluate a variety of\ncomputer vision methods such as object recognition, detection and segmentation\nat the level of instances and categories, tracking, ego-motion estimation,\nmonocular depth estimation and scene segmentation. The code and data are\navailable at http://www.robots.ox.ac.uk/~vgg/research/researchdoom.","url_abs":"http://arxiv.org/abs/1610.02431v1","url_pdf":"http://arxiv.org/pdf/1610.02431v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"},{"task_slug":"motion-estimation","task_name":"Motion Estimation"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"scene-segmentation","task_name":"Scene Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"cocodoom","name":"CocoDoom","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1610.02431","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}