In this paper, we consider the problem of determining feature saliency for three-dimensional (3D) objects and describe a series of experiments that examined if salient features exist and can be predicted in advance. We attempt to determine salient features by using an eye-tracking device to capture human gaze data and then investigate if the visual fidelity of simplified polygonal models can be improved by emphasizing the detail of salient features identified in this way. To try to evaluate the visual fidelity of the simplified models, a set of naming time, matching time, and forced-choice preference experiments were carried out. We found that perceptually weighted simplification led to a significant increase in visual fidelity for the lower levels of detail (LOD) of the natural objects, but that for the man-made artifacts the opposite was true. We, therefore, conclude that visually prominent features may be predicted in this way for natural objects, but our results show that saliency prediction for synthetic objects is more difficult, perhaps because it is more strongly affected by task. As a further step we carried out some confirmation experiments to examine if the prominent features found during the saliency experiment were actually the features focused upon during the naming, matching, and forced-choice preference tasks. Results demonstrated that the heads of natural objects received a significant amount of attention, especially during the naming task. We hope that our results will lead to new insights into the nature of saliency in 3D graphics.

In this paper, we consider the problem of determining feature saliency for 3D objects and describe a series of experiments that examined if salient features exist and can be predicted in advance. We attempt to determine salient features by using an eye-tracking device to capture human gaze data and then investigate if the visual fidelity of simplified polygonal models can be improved by emphasizing the detail of salient features identified in this way. To try to evaluate the visual fidelity of models simplified using both metrics, a set of naming time, matching time and forced-choice preference experiments were carried out. We found that our perceptually weighted metric led to a significant increase in visual fidelity for the lower levels of detail (LOD) of the natural objects, but that for the man-made artifacts the opposite was true. We therefore conclude that visually prominent features may be predicted in this way for natural objects, but our results show that saliency prediction for synthetic objects is more difficult, perhaps because it is more strongly affected by task. We hope that our results will lead to new insights into the nature of saliency in 3D graphics.

In this chapter, we will discuss the usefulness of eye-tracking for computer applications that attempt to render simulations at interactive rates. Some recent research that uses this technology is presented. While eyetracking has been relatively well-explored for the purposes of accelerating real-time rendering, it has not been very much considered with respect to the actual simulation process. In fact, perceptual issues relating to physically-based simulation have been largely neglected in the field of computer graphics. To evaluate simulations, we explore the relationship between eye-movements and dynamic events, such as collisions. We will describe some gaze-contingent systems that we and others have developed that exploit eye-tracking to enhance the perceived realism of images and simulations. Some experiments that were carried out to evaluate the feasibility of this approach are also presented.

In this paper, we describe a framework for comparing task performance in real and virtual environments. Realistic graphics, rear projection, haptics and rapid prototyping are used to match the virtual scene to the real scene. We describe some preliminary placement tasks which were evaluated using eye-tracking and discuss our future plans for this framework.

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