Real-time mesh simplification using the GPU

Christopher Decoro, Natalya Tatarchuk
3D Application Research Group, AMD / Princeton University
In I3D ’07: Proceedings of the 2007 symposium on Interactive 3D graphics and games (2007), pp. 161-166.


   title={Real-time mesh simplification using the GPU},

   author={DeCoro, C. and Tatarchuk, N.},

   booktitle={Proceedings of the 2007 symposium on Interactive 3D graphics and games},





Download Download (PDF)   View View   Source Source   



Recent advances in real-time rendering have allowed the GPU implementation of traditionally CPU-restricted algorithms, often with performance increases of an order of magnitude or greater. Such gains are achieved by leveraging the large-scale parallelism of the GPU towards applications that are well-suited for these streaming architectures. By contrast, mesh simplification has traditionally been viewed as a non-interactive process not readily amenable to GPU acceleration. We demonstrate how it becomes practical for real-time use through our method, and that the use of the GPU even for offline simplification leads to significant increases in performance. Our approach for mesh decimation adopts a vertex-clustering method to the GPU by taking advantage of a new addition to the rendering pipeline – the geometry shader stage. We present a novel general-purpose data structure designed for streaming architectures called the probabilistic octree, which allows for much of the flexibility of offline implementations, including sparse encoding and variable level-of-detail. We demonstrate successful use of this data structure in our GPU implementation of mesh simplification. We can generate adaptive levels of detail by applying non-linear warping functions to the cluster map in order to improve resulting simplification quality. Our GPU-accelerated approach enables simultaneous construction of multiple levels of detail and out-of-core simplification of extremely large polygonal meshes.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: