An experimental approach to predicting saliency for simplified polygonal models

Abstract

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.