Level-of-Detail Triangle Strips for Deforming Meshes

Applications such as video games or movies often contain deforming meshes. The most-commonly used representation of these types of meshes consists in dense polygonal models. Such a large amount of geometry can be efficiently managed by applying level-of-detail techniques and specific solutions have been developed in this field. However, these solutions do not offer a high performance in real-time applications. We thus introduce a multiresolution scheme for deforming meshes. It enables us to obtain different approximations over all the frames of an animation. Moreover, we provide an efficient connectivity coding by means of triangle strips as well as a flexible framework adapted to the GPU pipeline. Our approach enables real-time performance and, at the same time, provides accurate approximations

Dynamic Scene Graph: Enabling Scaling, Positioning, and Navigation in the Universe

In this work, we address the challenge of seamlessly visualizing astronomical data exhibiting huge scale differences in distance, size, and resolution. One of the difficulties is accurate, fast, and dynamic positioning and navigation to enable scaling over orders of magnitude, far beyond the precision of floating point arithmetic. To this end we propose a method that utilizes a dynamically assigned frame of reference to provide the highest possible numerical precision for all salient objects in a scene graph. This makes it possible to smoothly navigate and interactively render, for example, surface structures on Mars and the Milky Way simultaneously. Our work is based on an analysis of tracking and quantification of the propagation of precision errors through the computer graphics pipeline using interval arithmetic. Furthermore, we identify sources of precision degradation, leading to incorrect object positions in screen-space and z-fighting. Our proposed method operates without near and far planes while maintaining high depth precision through the use of floating point depth buffers. By providing interoperability with order-independent transparency algorithms, direct volume rendering, and stereoscopy, our approach is well suited for scientific visualization. We provide the mathematical background, a thorough description of the method, and a reference implementation.Funding Agencies|Swedish e-Science Research Center (SeRC); NASA [NNX16AB93A]; Moore-Sloan Data Science Environment at NYU; NSF [CNS-1229185, CCF-1533564, CNS-1544753]</p

Streaming surface sampling using Gaussian ε-nets

We propose a robust, feature preserving and user-steerable mesh sampling algorithm, based on the one-to-many mapping of a regular sampling of the Gaussian sphere onto a given manifold surface. Most of the operations are local, and no global information is maintained. For this reason, our algorithm is amenable to a parallel or streaming implementation and is most suitable in situations when it is not possible to hold all the input data in memory at the same time. Using ε-nets, we analyze the sampling method and propose solutions to avoid shortcomings inherent to all localized sampling methods. Further, as a byproduct of our sampling algorithm, a shape approximation is produced. Finally, we demonstrate a streaming implementation that handles large meshes with a small memory footprint