User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse




Conference Paper

GigaVoxels: Ray-guided Streaming for Efficient and Detailed Voxel Rendering


Eisemann,  Elmar
Max Planck Society;

There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available

Crassin, C., Neyret, F., Lefebvre, S., & Eisemann, E. (2009). GigaVoxels: Ray-guided Streaming for Efficient and Detailed Voxel Rendering. In ACM Symposium on Interactive 3D Graphics and Games (i3D) (pp. 15-22). New York, USA: ACM.

Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-19AF-C
We propose a new approach to efficiently render large volumetric data sets. The system achieves interactive to real-time rendering performance for several billion voxels. Our solution is based on an adaptive data representation depending on the current view and occlusion information, coupled to an efficient ray-casting rendering algorithm. One key element of our method is to guide data production and streaming directly based on information extracted during rendering. Our data structure exploits the fact that in CG scenes, details are often concentrated on the interface between free space and clusters of density and shows that volumetric models might become a valuable alternative as a rendering primitive for real-time applications. In this spirit, we allow a quality/performance trade-off and exploit temporal coherence. We also introduce a mipmapping-like process that allows for an increased display rate and better quality through high quality filtering. To further enrich the data set, we create additional details through a variety of procedural methods. We demonstrate our approach in several scenarios, like the exploration of a 3D scan ($8192^3$ resolution), of hypertextured meshes ($16384^3$ virtual resolution), or of a fractal (theoretically infinite resolution). All examples are rendered on current generation hardware at 20-90 fps and respect the limited GPU memory budget.