UnrealROX+: An Improved Tool for Acquiring Synthetic Data from Virtual 3D Environments. (arXiv:2104.11776v1 [cs.CV])

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.

Source: https://arxiv.org/abs/2104.11776

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