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Enter the RobotriX, an extremely photorealistic indoor dataset designed toenable the application of deep learning techniques to a wide variety of roboticvision problems. The RobotriX consists of hyperrealistic indoor scenes whichare explored by robot agents which also interact with objects in a visuallyrealistic manner in that simulated world. Photorealistic scenes and robots arerendered by Unreal Engine into a virtual reality headset which captures gaze sothat a human operator can move the robot and use controllers for the robotichands; scene information is dumped on a per-frame basis so that it can bereproduced offline to generate raw data and ground truth labels. By taking thisapproach, we were able to generate a dataset of 38 semantic classes totaling 8Mstills recorded at +60 frames per second with full HD resolution. For eachframe, RGB-D and 3D information is provided with full annotations in bothspaces. Thanks to the high quality and quantity of both raw information andannotations, the RobotriX will serve as a new milestone for investigating 2Dand 3D robotic vision tasks with large-scale data-driven techniques.

The RobotriX: An eXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and Interactions