Mesh resizing based on hierarchical saliency detection

Mesh saliency is a perception-inspired metric for regional importance which is helpful to many aspects of mesh processing. However, existing mesh saliency cannot be used in mesh resizing directly because of the neglect of resizing direction. In this paper, we propose a region descriptor based on its vulnerability to a resizing direction, and use this descriptor to compute the region's saliency based on its contrast to neighboring regions. In order to avoid being misled by repeated small-scale features on the mesh, we put forward a hierarchical method for saliency computing. We build a hierarchical coarse-to-fine segmentations of the input mesh, and evaluate the saliency value on different levels of segmentations. Finally these saliency values are integrated into one saliency map after applying non-linear suppression. Equipped with the saliency map, a framework for non-homogeneous mesh resizing is presented. We regard every edge as a spring, and scale the mesh by stretching the edge. Based on the salience value, we build a global energy function on the mesh. Experiments show that our resizing method based on hierarchical saliency analysis can produce visually appealing results.