Several Feature Measurements Of Mesh Surface And Its Applications

Mesh surfaces are widely used in computer graphics and geometric modeling. There are more and more mesh applications based on metrics of perceptual importance with the rapid growth in the number and quantity of mesh surfaces. All indicates that we could incorporate insights from human perception into mesh processing. Purely geometric measures of shape such as curvature is very limited, and could not capture all visually interesting regions on a mesh. So it is of great significance to propose some good metrics of perceptual importance.In this paper, we firstly review two feature measurements that mesh saliency and mesh rectilinearity. Mesh saliency is a measure of regional importance that could be incorporated into mesh simplification and viewpoint selection. Mesh rectilinearity is a shape descriptor with direct meanings that could be used in shape retrieval query. Then, we introduce some basic concepts of mesh surface, mesh simplification, and we also present the surface simplification algorithm using quadric error metrics(QEM) in detail. At last, a new mesh saliency and mesh sphericity are proposed, and an improved mesh simplification algorithm is developed based on the new mesh saliency.In this paper, we prove the validity of the new mesh saliency compared with purely curvature based metric based on some specific examples. At last, we conduct some experiments on the improved mesh simplification algorithm contrast to the original algorithm, and illustrate the effectiveness of the improved algorithm. Some experiments are also carried out to validate the rationality and effectiveness of our spherical measurement. Furthermore, these two measurements share some of the same properties:the measure value is in the range of0-1;the measure is invariant under scale, rotation and translation.