Fakultät Informatik

A Generic and Scalable Pipeline for Tetrahedral Grid Rendering

 Joachim Georgii, Rüdiger Westermann

Computer Graphics and Visualization Group, Technische Universität München, Germany


Recent advances in algorithms and graphics hardware have opened the possibility to render tetrahedral grids at inter- active rates on commodity PCs. This paper extends on this work in that it presents a direct volume rendering method for such grids which supports both current and upcoming graphics hardware architectures, large and deformable grids, as well as different rendering options. At the core of our method is the idea to perform the sampling of tetrahedral elements along the view rays entirely in local barycentric co- ordinates. Then, sampling requires minimum GPU memory and texture access operations, and it maps efficiently onto a feed-forward pipeline of multiple stages performing com- putation and geometry construction. We propose to spawn rendered elements from one single vertex. This makes the method amenable to upcoming Direct3D 10 graphics hard- ware which allows to create geometry on the GPU. By only modifying the algorithm slightly it can be used to render per- pixel iso-surfaces and to perform tetrahedral cell projection. As our method neither requires any pre-processing nor an intermediate grid representation it can efficiently deal with dynamic and large 3D meshes.

Direct volume rendering of the deformable visible human data set. The tetrahedral mesh consists of 3.8 million elements, and it is textured with a 512x512x302 3D texture map

Associated publications

A Generic and Scalable Pipeline for GPU Tetrahedral Grid Rendering
J. Georgii, R. Westermann, IEEE Transactions on Visualization and Computer Graphics (Proceedings of  IEEE Visualization 2006) [Bibtex]


accompanying video of the paper [30 MB AVI]



Bachelor and Master thesis in the following areas:
- A remote rendering system for point cloud data (in collaboration with industry)

- Deep learning for improved weather forecasting

- Learning trajectory clustering using neural network
- Learning Level-of-Detail representations for point clouds

- In collaboration with partners from industry, we have a number of thesis topics available in the area of point-based rendering, geo-localization using public data, scene fusion from different viewpoints. If you are interested, please contact  westermann(at)tum.de


- One PhD position on   Turbulence Visualization is available at the Computer Graphics & Visualization group.