Research over the last decade has built a solid mathematical foundation for representation and analysis of 3D meshes in graphics and geometric modeling. Much of this work however does not explicitly incorporate models of low-level human visual attention. In this paper we introduce the idea of mesh saliency as a measure of regional importance for graphics meshes. Our notion of saliency is inspired by low-level human visual system cues. We define mesh saliency in a scale-dependent manner using a center-surround operator on Gaussian-weighted mean curvatures. We observe that such a definition of mesh saliency is able to capture what most would classify as visually interesting regions on a mesh. The human-perceptioninspired importance measure computed by our mesh saliency operator results in more visually pleasing results in processing and viewing of 3D meshes, compared to using a purely geometric measure of shape, such as curvature. We discuss how mesh saliency can be incorporated in graphics applications such as mesh simplification and viewpoint selection and present examples that show visually appealing results from using mesh saliency.

Research over the last decade has built a solid mathematical foundation for representation and analysis of 3D meshes in graphics and geometric modeling. Much of this work however does not explicitly incorporate models of low-level human visual attention. In this paper we introduce the idea of mesh saliency as a measure of regional importance for graphics meshes. Our notion of saliency is inspired by low-level human visual system cues. We define mesh saliency in a scale-dependent manner using a center-surround operator on Gaussian-weighted mean curvatures. We observe that such a definition of mesh saliency is able to capture what most would classify as visually interesting regions on a mesh. The human-perceptioninspired importance measure computed by our mesh saliency operator results in more visually pleasing results in processing and viewing of 3D meshes, compared to using a purely geometric measure of shape, such as curvature. We discuss how mesh saliency can be incorporated in graphics applications such as mesh simplification and viewpoint selection and present examples that show visually appealing results from using mesh saliency.

Virtual reality displays, such as head-mounted displays (HMD), afford us a superior spatial awareness by leveraging our vestibular and proprioceptive senses, as compared to traditional desktop displays. Since classical times, people have used memory palaces as a spatial mnemonic to help remember information by organizing it spatially and associating it with salient features in that environment. In this paper, we explore whether using virtual memory palaces in a head-mounted display with head-tracking (HMD condition) would allow a user to better recall information than when using a traditional desktop display with a mouse-based interaction (desktop condition). We found that virtual memory palaces in HMD condition provide a superior memory recall ability compared to the desktop condition. We believe this is a first step in using virtual environments for creating more memorable experiences that enhance productivity through better recall of large amounts of information organized using the idea of virtual memory palaces.

We propose the idea of simplification envelopes for generating a hierarchy of level-of-detail approximations for a given polygonal model. Our approach guarantees that all points of an approximation are within a user-specifiable distance from the original model and that all points of the original model are within a distance from the approximation. Simplification envelopes provide a general framework within which a large collection of existing simplification algorithms can run. We demonstrate this technique in conjunction with two algorithms, one local, the other global. The local algorithm provides a fast method for generating approximations to large input meshes (at least hundreds of thousands of triangles). The global algorithm provides the opportunity to avoid local "minima" and possibly achieve better simplifications as a result.Each approximation attempts to minimize the total number of polygons required to satisfy the above constraint. The key advantages of our approach are:General technique providing guaranteed error bounds for genus-preserving simplification Automation of both the simplification process and the selection of appropriate viewing distances Prevention of self-intersection Preservation of sharp features Allows variation of approximation distance across different portions of a model