UnrealROX+: An Improved Tool for Acquiring Synthetic Data from Virtual 3D Environments
Empreu sempre aquest identificador per citar o enllaçar aquest ítem
http://hdl.handle.net/10045/114587
Títol: | UnrealROX+: An Improved Tool for Acquiring Synthetic Data from Virtual 3D Environments |
---|---|
Autors: | Martínez González, Pablo | Oprea, Sergiu | Castro-Vargas, John Alejandro | Garcia-Garcia, Alberto | Orts-Escolano, Sergio | Garcia-Rodriguez, Jose | Vincze, Markus |
Titular/s del dret: | Universidad de Alicante |
Grups d'investigació o GITE: | 3D Perception Lab |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Paraules clau: | Synthetic Data | Data Generation | Simulation | Deep Learning |
Àrees de coneixement: | Ciencia de la Computación e Inteligencia Artificial |
Data de creació: | 2020 |
Data de publicació: | 2021 |
Resum: | Synthetic data generation has become essential in last years for feeding data-driven algorithms, which surpassed traditional techniques performance in almost every computer vision problem. Gathering and labelling the amount of data needed for these data-hungry models in the real world may become unfeasible and error-prone, while synthetic data give us the possibility of generating huge amounts of data with pixel-perfect annotations. However, most synthetic datasets lack from enough realism in their rendered images. In that context UnrealROX generation tool was presented in 2019, allowing to generate highly realistic data, at high resolutions and framerates, with an efficient pipeline based on Unreal Engine, a cutting-edge videogame engine. UnrealROX enabled robotic vision researchers to generate realistic and visually plausible data with full ground truth for a wide variety of problems such as class and instance semantic segmentation, object detection, depth estimation, visual grasping, and navigation. Nevertheless, its workflow was very tied to generate image sequences from a robotic on-board camera, making hard to generate data for other purposes. In this work, we present UnrealROX+, an improved version of UnrealROX where its decoupled and easy-to-use data acquisition system allows to quickly design and generate data in a much more flexible and customizable way. Moreover, it is packaged as an Unreal plug-in, which makes it more comfortable to use with already existing Unreal projects, and it also includes new features such as generating albedo or a Python API for interacting with the virtual environment from Deep Learning frameworks. |
Patrocinadors: | Spanish Government PID2019-104818RB-I00 grant for the MoDeaAS project, supported with Feder funds. This work has also been supported by Spanish national grants for PhD studies FPU17/00166,ACIF/2018/197 and UAFPU2019-13. Experiments were made possible by a generous hardware donation from NVIDIA. |
URI: | http://hdl.handle.net/10045/114587 |
Idioma: | eng |
Tipus: | software |
Drets: | © Universitat d'Alacant / Universidad de Alicante. Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) |
Revisió científica: | no |
Versió de l'editor: | https://arxiv.org/abs/2104.11776 |
Apareix a la col·lecció: | Registro de Programas de Ordenador y Bases de Datos |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
---|---|---|---|---|
UnrealRox+.pdf | https://github.com/3dperceptionlab/unrealrox-plus#unrealrox | 640,41 kB | Adobe PDF | Obrir Vista prèvia |
Aquest ítem està subjecte a una llicència de Creative Commons Llicència Creative Commons