A unified information theory framework for viewpoint selection and mesh saliency

Abstract

Viewpoint selection is an emerging area in computer graphics with applications in fields such as scene exploration, image-based modeling, and volume visualization. For example, best view selection algorithms are used to obtain the minimum number of views (or images) in order to understand or model an object or scene better. In this talk, we present a unified framework for viewpoint selection and mesh saliency computation based on the definition of an information channel between a set of viewpoints (input) and the set of polygons of an object (output). The mutual information of this channel is shown to be a powerful tool to deal with viewpoint selection, object exploration and viewpoint-based saliency. In addition, in order to take into account perceptual criteria in our viewpoint selection approach, viewpoint mutual information is extended by incorporating importance factors such as saliency and stability. The information channel can also be extended to deal with volume visualization. Finally, we present the application of viewpoint mutual information to mesh simplification.