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UnrealROX+: An Improved Tool for Acquiring Synthetic Data from Virtual 3D Environments
arXiv - CS - Graphics Pub Date : 2021-04-23 , DOI: arxiv-2104.11776
Pablo Martinez-Gonzalez, Sergiu Oprea, John Alejandro Castro-Vargas, Alberto Garcia-Garcia, Sergio Orts-Escolano, Jose Garcia-Rodriguez, Markus Vincze

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.

中文翻译:

UnrealROX +:一种改进的工具,用于从虚拟3D环境中获取合成数据

近年来,合成数据的生成对于馈送数据驱动的算法已经变得至关重要,该算法在几乎每个计算机视觉问题中都超过了传统技术。在现实世界中,收集和标记这些需要大量数据的模型所需的数据量可能变得不可行且容易出错,而合成数据使我们可以生成具有像素完美注释的大量数据。但是,大多数合成数据集的渲染图像缺乏足够的真实感。在这种情况下,UnrealROX生成工具于2019年推出,可通过基于尖端视频游戏引擎Unreal Engine的高效管道以高分辨率和帧速率生成高度逼真的数据。UnrealROX使机器人视觉研究人员能够生成具有完整地面真实性的现实且视觉上合理的数据,以解决各种问题,例如类和实例的语义分割,对象检测,深度估计,视觉抓取和导航。然而,它的工作流程非常紧密,无法从机载机载摄像头生成图像序列,因此很难为其他目的生成数据。在这项工作中,我们介绍UnrealROX +,它是UnrealROX的改进版本,其解耦且易于使用的数据采集系统允许以更加灵活和可自定义的方式快速设计和生成数据。而且,它被打包为Unreal插件,这使它更适合与现有的Unreal项目一起使用,
更新日期:2021-04-27
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