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A GPU-based Approach for Massive Model Rendering with Frame-to-Frame Coherence

机译:基于GPU的具有帧间一致性的大规模模型渲染方法

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摘要

Rendering massive 3D models in real-time has long been recognized as a very challenging problem because of the limited computational power and memory space available in a workstation. Most existing rendering techniques, especially level of detail (LOD) processing, have suffered from their sequential execution natures. We present a GPU-based approach which enables interactive rendering of large 3D models with hundreds of millions of triangles. Our work contributes to the massive rendering research in two ways. First, we present a simple and efficient mesh simplification algorithm towards GPU architecture. Second, we propose a novel GPU out-of-core approach that adopts a frame-to-frame coherence scheme in order to minimize the high communication cost between CPU and GPU. Our results show that the parallel algorithm of mesh simplification and the GPU out-of-core approach significantly improve the overall rendering performance.
机译:由于工作站中可用的计算能力和内存空间有限,实时渲染大型3D模型一直被认为是一个非常具有挑战性的问题。大多数现有的渲染技术,尤其是细节级别(LOD)处理,都受其顺序执行特性的影响。我们提出了一种基于GPU的方法,该方法可以交互式渲染具有数亿个三角形的大型3D模型。我们的工作通过两种方式为大规模渲染研究做出了贡献。首先,我们针对GPU架构提出了一种简单有效的网格简化算法。其次,我们提出了一种新颖的GPU内核外方法,该方法采用了帧到帧的一致性方案,以最大程度地减少CPU和GPU之间的高通信成本。我们的结果表明,网格简化的并行算法和GPU外核方法显着提高了整体渲染性能。

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