UnrealROX+: An Improved Tool for Acquiring Synthetic Data from Virtual 3D Environments

04/23/2021
by   Pablo Martinez-Gonzalez, et al.
1

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.

READ FULL TEXT

page 1

page 3

page 5

page 7

research
10/16/2018

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

Data-driven algorithms have surpassed traditional techniques in almost e...
research
07/21/2023

ParGANDA: Making Synthetic Pedestrians A Reality For Object Detection

Object detection is the key technique to a number of Computer Vision app...
research
10/17/2017

Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications

We present an overview and evaluation of a new, systematic approach for ...
research
05/28/2021

NViSII: A Scriptable Tool for Photorealistic Image Generation

We present a Python-based renderer built on NVIDIA's OptiX ray tracing e...
research
08/08/2023

PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning

Synthetic image datasets offer unmatched advantages for designing and ev...
research
05/31/2022

Hands-Up: Leveraging Synthetic Data for Hands-On-Wheel Detection

Over the past few years there has been major progress in the field of sy...
research
09/06/2020

Rain rendering for evaluating and improving robustness to bad weather

Rain fills the atmosphere with water particles, which breaks the common ...

Please sign up or login with your details

Forgot password? Click here to reset