Perceptually Modulated Level of Detail for Virtual Environments

Abstract

This thesis presents a generic and principled solution for optimising the visual complexity of any arbitrary computer-generated virtual environment (VE). This is performed with the ultimate goal of reducing the inherent latencies of current virtual reality (VR) technology. Effectively, we wish to remove extraneous detail from an environment which the user cannot perceive, and thus modulate the graphical complexity of a VE with little or no perceptual artifacts.

The work proceeds by investigating contemporary models and theories of visual perception and then applying these to the field of real-time computer graphics. Subsequently, a technique is devised to assess the perceptual content of a computer-generated image in terms of spatial frequency (c/deg), and a model of contrast sensitivity is formulated to describe a user's ability to perceive detail under various conditions in terms of this metric. This allows us to base the level of detail (LOD) of each object in a VE on a measure of the degree of spatial detail which the user can perceive at any instant (taking into consideration the size of an object, its angular velocity, and the degree to which it exists in the peripheral field). Additionally, a generic polygon simplification framework is presented to complement the use of perceptually modulated LOD.

The efficient implementation of this perceptual model is discussed and a prototype system is evaluated through a suite of experiments. These include a number of low-level psychophysical studies (to evaluate the accuracy of the model), a task performance study (to evaluate the effects of the model on the user), and an analysis of system performance gain (to evaluate the effects of the model on the system). The results show that for the test application chosen, the frame rate of the simulation was manifestly improved (by four to five-fold) with no perceivable drop in image fidelity. As a result, users were able to perform the given wayfinding task more proficiently and rapidly.

Finally, conclusions are drawn on the application and utility of perceptually-based optimisations; both in reference to this work, and in the wider context.