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

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 the 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 propose a GPU-based streaming approach which adopt a frame-to-frame coherence scheme in order to minimize the high communication cost between CPU and GPU. Second, we present a simple and effcient mesh simplification algorithm towards GPU architecture. Our results show that GPU-based streaming approach and the parallel mesh simplification algorithm significantly improve the overall rendering performance.

Name Department College
Yong Cao Computer Science College of Engineering
Chao Peng Computer Science College of Engineering
Collaborative Colleges: 
Rendered 3D Models