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Neural network-based detection of virtual environment anomalies

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Abstract

The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.

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Notes

  1. http://www.gamespot.com/.

  2. The early stopping strategy involves stopping training as soon as the validation error increases for a specified number of iterations [8].

  3. http://creators.xna.com/en-US/education/starterkits/.

  4. In computer graphics, the technique of displacing vertices according to the values sampled from a texture is known as displacement mapping. Here, a method similar to the displacement mapping technique was used for introducing bugs to the original geometry. In particular, a texture of white Gaussian noise was used for displacing the vertices.

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Authors and Affiliations

  1. Science and Engineering Faculty, Queensland University of Technology, Brisbane, 4000, Australia

    Alfredo Nantes, Ross Brown & Frederic Maire

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  1. Alfredo Nantes
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  2. Ross Brown
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  3. Frederic Maire
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Correspondence to Alfredo Nantes.

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Nantes, A., Brown, R. & Maire, F. Neural network-based detection of virtual environment anomalies. Neural Comput & Applic 23, 1711–1728 (2013). https://doi.org/10.1007/s00521-012-1132-x

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