1 research outputs found
The RobotriX: An eXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and Interactions
Enter the RobotriX, an extremely photorealistic indoor dataset designed to
enable the application of deep learning techniques to a wide variety of robotic
vision problems. The RobotriX consists of hyperrealistic indoor scenes which
are explored by robot agents which also interact with objects in a visually
realistic manner in that simulated world. Photorealistic scenes and robots are
rendered by Unreal Engine into a virtual reality headset which captures gaze so
that a human operator can move the robot and use controllers for the robotic
hands; scene information is dumped on a per-frame basis so that it can be
reproduced offline to generate raw data and ground truth labels. By taking this
approach, we were able to generate a dataset of 38 semantic classes totaling 8M
stills recorded at +60 frames per second with full HD resolution. For each
frame, RGB-D and 3D information is provided with full annotations in both
spaces. Thanks to the high quality and quantity of both raw information and
annotations, the RobotriX will serve as a new milestone for investigating 2D
and 3D robotic vision tasks with large-scale data-driven techniques