An improved adjacency data structure for fast triangle stripping

To speed up the rendering of polygonal meshes, triangle strips are commonly used to reduce the number of vertices sent to the graphics subsystem by exploiting the fact that adjacent triangles share an edge. In this paper, we present an improved adjacency data structure for fast triangle stripping algorithms. There are three major contributions: first, the data structure can be created quickly and robustly from any indexed face set; second, its cache-friendly layout is specifically designed to efficiently answer common stripping queries, such as neighbor finding and least-degree triangle finding, in constant time; third, the stripping algorithm operates in-place, since strips are created by simply relinking pointers. An implementation of a stripping algorithm shows a significant speed-up compared to other implementations. Our implementation is publicly available as part of OpenSG [9].

Iterative stripification of a triangle mesh: focus on data structures

In this paper we describe the data structure and some implementation details of the tunneling algorithm for generating a set of triangle strips from a mesh of triangles. The algorithm uses a simple topological operation on the dual graph of the mesh, to generate an initial stripification and iteratively rearrange and decrease the number of strips. Our method is a major improvement of a proposed one originally devised for both static and continuous level-of-detail (CLOD) meshes and retains this feature. The usage of a dynamical identification strategy for the strips allows us to drastically reduce the length of the searching paths in the graph needed for the rearrangement and produce loop-free triangle strips without any further controls and post-processing, while requiring a more sophisticated implementation to manage the search and undo operations

The study of embedded 3D graphics acceleration–geometry engineer algorithm and VLSI design

计算机图形学主要以硬件设备的发展为基础，以应用需求为巨大的驱动力，两者相互存在相互影响。由于软件水平的提高，计算机图形的绘制越来越依赖于图形硬件的发展。另一方面，近年来由于导航仪、手机等消费性电子产品上对于三维绘图应用需求的大量增加，如何在嵌入式系统上设计一个低成本的三维绘图加速硬件成为一个重要的议题。嵌入式3D图形加速可以分为几何引擎和渲染引擎两个部分，本文主要对3D图形加速方案中的几何引擎的几何变换、光照和裁剪等核心算法进行了基础性研究。 本文首先回顾了3D图形加速的研究背景、3D图形加速的研究意义、应用领域和当前国内外研究现状，对几何引擎的核心算法从基本原理和算法上进行了较为详细的介绍...Computer Graphics is mainly based on the development of hardware devices, the driving force is the application requirements in which there is an interaction with each other.As the improvement of software, computer graphic drawing depends on the development of graphic hardware. On the other hand, in recent years,because of the increasing demand of 3D graphics applications for navigation, mobile pho...学位：工学硕士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2332007115216

Memory sharing for interactive ray tracing on clusters

ManuscriptWe present recent results in the application of distributed shared memory to image parallel ray tracing on clusters. Image parallel rendering is traditionally limited to scenes that are small enough to be replicated in the memory of each node, because any processor may require access to any piece of the scene. We solve this problem by making all of a cluster's memory available through software distributed shared memory layers. With gigabit ethernet connections, this mechanism is sufficiently fast for interactive rendering of multi-gigabyte datasets. Object- and page-based distributed shared memories are compared, and optimizations for efficient memory use are discussed

AlSub: Fully Parallel and Modular Subdivision

In recent years, mesh subdivision---the process of forging smooth free-form surfaces from coarse polygonal meshes---has become an indispensable production instrument. Although subdivision performance is crucial during simulation, animation and rendering, state-of-the-art approaches still rely on serial implementations for complex parts of the subdivision process. Therefore, they often fail to harness the power of modern parallel devices, like the graphics processing unit (GPU), for large parts of the algorithm and must resort to time-consuming serial preprocessing. In this paper, we show that a complete parallelization of the subdivision process for modern architectures is possible. Building on sparse matrix linear algebra, we show how to structure the complete subdivision process into a sequence of algebra operations. By restructuring and grouping these operations, we adapt the process for different use cases, such as regular subdivision of dynamic meshes, uniform subdivision for immutable topology, and feature-adaptive subdivision for efficient rendering of animated models. As the same machinery is used for all use cases, identical subdivision results are achieved in all parts of the production pipeline. As a second contribution, we show how these linear algebra formulations can effectively be translated into efficient GPU kernels. Applying our strategies to $\sqrt{3}$, Loop and Catmull-Clark subdivision shows significant speedups of our approach compared to state-of-the-art solutions, while we completely avoid serial preprocessing.Comment: Changed structure Added content Improved description