UnrealCV: Connecting Computer Vision to Unreal Engine (1609.01326v1)

Abstract: Computer graphics can not only generate synthetic images and ground truth but it also offers the possibility of constructing virtual worlds in which: (i) an agent can perceive, navigate, and take actions guided by AI algorithms, (ii) properties of the worlds can be modified (e.g., material and reflectance), (iii) physical simulations can be performed, and (iv) algorithms can be learnt and evaluated. But creating realistic virtual worlds is not easy. The game industry, however, has spent a lot of effort creating 3D worlds, which a player can interact with. So researchers can build on these resources to create virtual worlds, provided we can access and modify the internal data structures of the games. To enable this we created an open-source plugin UnrealCV (http://unrealcv.github.io) for a popular game engine Unreal Engine 4 (UE4). We show two applications: (i) a proof of concept image dataset, and (ii) linking Caffe with the virtual world to test deep network algorithms.

• The paper introduces UnrealCV, an open-source UE4 plugin that bridges gaming technology and computer vision by enabling synthetic data generation.
• It employs a flexible, socket-based architecture that allows seamless integration with external tools like Python, MATLAB, and Caffe.
• Case studies demonstrate that minor environment adjustments significantly impact model performance, revealing potential biases in dataset training.

UnrealCV: Connecting Computer Vision to Unreal Engine

The paper "UnrealCV: Connecting Computer Vision to Unreal Engine" by Weichao Qiu and Alan Yuille presents the UnrealCV tool, an open-source plugin designed to interface the Unreal Engine 4 (UE4) with computer vision tasks. The UnrealCV plugin capitalizes on the sophisticated, realistic 3D virtual worlds developed by the gaming industry to facilitate computer vision research which requires large quantities of synthetically generated data and diverse, interactive environments. UnrealCV, when integrated into UE4, allows researchers to create virtual worlds where internal data structures are accessible and modifiable, providing a rich framework for generating ground truth data, simulating agent behaviors, and testing AI algorithms.

Motivation and Context

The efficacy of deep learning models in computer vision is heavily influenced by the availability and quality of training datasets. Constructing these datasets manually through annotation is resource-intensive, which motivates the use of synthetic datasets for training and testing algorithms. The gaming industry, notably through platforms like UE4, provides robust tools and realistic environments that can simplify the creation of these synthetic datasets. UnrealCV is positioned within this context, offering a means to exploit these virtual worlds created by UE4 without necessitating significant modifications to proprietary game codebases.

Features and Architecture of UnrealCV

UnrealCV extends the natural capabilities of UE4 to control world properties programmatically and access internal game data critical to generating synthetic visuals and corresponding metadata like depth maps, object masks, and more. It employs an architecture that includes an UnrealCV server embedded as a plugin within UE4, and an UnrealCV client, which interfaces with external programs like Caffe. This architecture is agnostic to the programming language due to its use of sockets for communication, providing flexibility across platforms and environments.

One of UnrealCV's strengths is its extensibility. Command structures are hierarchically modular, enabling seamless augmentation without disrupting existing functionalities. Ease of use is another focal point, as UnrealCV allows researchers to engage with its features with minimal prerequisite understanding of UE4 due to the provision of precompiled binaries and accessible client integration with scripts in Python or MATLAB.

Applications and Implications

The authors demonstrate UnrealCV's utility through case studies focusing on generating synthetic image datasets and diagnosing the performance of deep networks within a controlled virtual environment. They present how UnrealCV facilitates varying conditions—such as camera positions or object properties—to evaluate algorithm robustness systematically. In one paper, the detection rates of Faster-RCNN varied significantly with small changes in viewpoint and scene configuration, highlighting potential biases inherent in model training.

The broader implication of UnrealCV is its potential to democratize access to high-quality synthetic datasets and virtual environments without the prohibitive costs associated with bespoke virtual world creation. It serves as a practical bridge between the high resource requirements of traditional computer vision research and the tools readily available in the gaming sector.

Future Directions

Looking forward, the field of virtual worlds for computer vision using tools like UnrealCV poses interesting challenges, such as enhancing the diversity and realism of synthetic environments, improving physical simulations, and reducing the domain gap between synthetic and real-world data. Advances in virtual reality and gaming are anticipated to bolster the contents and tools available for these tasks, with UnrealCV well positioned to leverage such trends given its open-source nature and strong integration capabilities within UE4.

In conclusion, UnrealCV offers a valuable toolkit for the computer vision research community, facilitating innovative uses of gaming technology resources for synthetic data generation and algorithm testing. This paper contributes significantly to bridging the gap between state-of-the-art real-time rendering in the gaming industry and its potential applications in advanced AI and robotics research.