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Data-driven algorithms have surpassed traditional techniques in almost everyaspect in robotic vision problems. Such algorithms need vast amounts of qualitydata to be able to work properly after their training process. Gathering andannotating that sheer amount of data in the real world is a time-consuming anderror-prone task. Those problems limit scale and quality. Synthetic datageneration has become increasingly popular since it is faster to generate andautomatic to annotate. However, most of the current datasets and environmentslack realism, interactions, and details from the real world. UnrealROX is anenvironment built over Unreal Engine 4 which aims to reduce that reality gap byleveraging hyperrealistic indoor scenes that are explored by robot agents whichalso interact with objects in a visually realistic manner in that simulatedworld. Photorealistic scenes and robots are rendered by Unreal Engine into avirtual reality headset which captures gaze so that a human operator can movethe robot and use controllers for the robotic hands; scene information isdumped on a per-frame basis so that it can be reproduced offline to generateraw data and ground truth annotations. This virtual reality environment enablesrobotic vision researchers to generate realistic and visually plausible datawith full ground truth for a wide variety of problems such as class andinstance semantic segmentation, object detection, depth estimation, visualgrasping, and navigation.

UnrealROX: An eXtremely Photorealistic Virtual Reality Environment for Robotics Simulations and Synthetic Data Generation