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 this http URL
Another recent effort similar to ResearchDoom is Unreal CV @cite_5 . This engine can also extract metadata similar to ResearchDoom and, by building on the Unreal engine, can potentially be applicable to a huge variety of modern video games. ResearchDoom is much simpler than Unreal CV as it applies to a single and relatively old game. While Doom incorporates several limitations, such as the fact that the camera can only rotate around the vertical axis, the data is nevertheless fairly complex. Furthermore, Doom provides a relatively restricted and consistent world form which data can be extracted for experiments. We leverage the latter fact to define and provide abundant pre-computed data with detailed metadata as well as with annotations using the Microsoft Coco format.
@cite_5
2517064749
UnrealCV is a project to help computer vision researchers build virtual worlds using Unreal Engine 4 (UE4). It extends UE4 with a plugin by providing (1) A set of UnrealCV commands to interact with the virtual world. (2) Communication between UE4 and an external program, such as Caffe. UnrealCV can be used in two ways. The first one is using a compiled game binary with UnrealCV embedded. This is as simple as running a game, no knowledge of Unreal Engine is required. The second is installing UnrealCV plugin to Unreal Engine 4 (UE4) and use the editor of UE4 to build a new virtual world. UnrealCV is an open-source software under the MIT license. Since the initial release in September 2016, it has gathered an active community of users, including students and researchers.