The RobotriX: An Extremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and Interactions
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http://hdl.handle.net/10045/113500
Title: | The RobotriX: An Extremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and Interactions |
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Authors: | Garcia-Garcia, Alberto | Martínez González, Pablo | Oprea, Sergiu | Castro-Vargas, John Alejandro | Jover-Álvarez, Álvaro | Orts-Escolano, Sergio | Garcia-Rodriguez, Jose |
Right's holder: | Universidad de Alicante |
Research Group/s: | Arquitecturas Inteligentes Aplicadas (AIA) | Robótica y Visión Tridimensional (RoViT) |
Center, Department or Service: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Keywords: | Indoor Dataset | Photorealism | Deep Learning | Robotics |
Knowledge Area: | Arquitectura y Tecnología de Computadores |
Issue Date: | 2018 |
Publisher: | IEEE |
Abstract: | 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 using UnrealCV to generate raw data and ground truth labels. By taking this approach we were able to generate a dataset of 38 semantic classes across 512 sequences 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. |
Sponsor: | Instituto Universitario de Investigación en Informática (Grupo de Interés 3D Perception Lab). This work has been also funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. This work has also been supported by a Spanish national grant for PhD studies FPU15/04516 and by the University of Alicante project GRE16-19 and by the Valencian Government project GV/2018/022. Experiments were made possible by a generous hardware donation from NVIDIA. |
URI: | https://github.com/3dperceptionlab/therobotrix | http://hdl.handle.net/10045/113500 |
DOI: | 10.1109/IROS.2018.8594495 |
Language: | eng |
Type: | dataset |
Rights: | © Universitat d'Alacant / Universidad de Alicante |
Peer Review: | no |
Publisher version: | https://doi.org/10.1109/IROS.2018.8594495 |
Appears in Collections: | Registro de Programas de Ordenador y Bases de Datos |
Files in This Item:
File | Description | Size | Format | |
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GitHub-3dperceptionlab_therobotrix.pdf | 1,2 MB | Adobe PDF | Open Preview | |
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