2010 |
 | Efficient Rendering of Highly Detailed Volumetric Scenes with GigaVoxels Crassin, Cyril; Neyret, Fabrice; Sainz, Miguel; Eisemann, Elmar GPU Pro, Page(s): 643--676, A K Peters, 2010. (Inbook) (Abstract | Links | BibTeX | Tags: Voxel, Real-Time, rendering, out-of-core, filtering, gpu) @inbook{CNSE10,
name = {Efficient Rendering of Highly Detailed Volumetric Scenes with GigaVoxels},
author = {Crassin, Cyril and Neyret, Fabrice and Sainz, Miguel and Eisemann, Elmar},
url = {http://artis.imag.fr/Publications/2010/CNSE10, INRIA Publication Page
},
year = {2010},
date = {2010-07-01},
booktitle = {GPU Pro},
pages = {643--676},
publisher = {A K Peters},
abstract = {GigaVoxels is a voxel-based rendering pipeline that makes the display of very large volumetric datasets very efficient. It is adapted to memory bound environments and it is designed for the data-parallel architecture of the GPU. It is capable of rendering objects at a detail level that matches the screen resolution and interactively adapts to the current point of view. Invisible parts are never even considered for contribution to the final image.
As a result, the algorithm obtains interactive to real-time framerates and demonstrates the use of extreme amounts of voxels in rendering, which is applicable in many different contexts. This is also confirmed by many game developers who seriously consider voxels as a potential standard primitive in video games. We will also show in this chapter that voxels are already powerful primitives that, for some rendering tasks, achieve higher performance than triangle-based representations.},
keywords = {Voxel, Real-Time, rendering, out-of-core, filtering, gpu}
}
GigaVoxels is a voxel-based rendering pipeline that makes the display of very large volumetric datasets very efficient. It is adapted to memory bound environments and it is designed for the data-parallel architecture of the GPU. It is capable of rendering objects at a detail level that matches the screen resolution and interactively adapts to the current point of view. Invisible parts are never even considered for contribution to the final image.
As a result, the algorithm obtains interactive to real-time framerates and demonstrates the use of extreme amounts of voxels in rendering, which is applicable in many different contexts. This is also confirmed by many game developers who seriously consider voxels as a potential standard primitive in video games. We will also show in this chapter that voxels are already powerful primitives that, for some rendering tasks, achieve higher performance than triangle-based representations. |
2009 |
 | Building with Bricks: Cuda-based Gigavoxel Rendering Crassin, Cyril; Neyret, Fabrice; Eisemann, Elmar Intel Visual Computing Research Conference, 2009. (Inproceeding) (Abstract | Links | BibTeX | Tags: Voxel, out-of-core, filtering, voxelization, gpu, ray-tracing, depth-of-field, real-time rendering, ray-casting, octree, GigaVoxels, cache) @inproceedings{CNE09,
name = {Building with Bricks: Cuda-based Gigavoxel Rendering},
author = {Crassin, Cyril and Neyret, Fabrice and Eisemann, Elmar},
url = {http://artis.imag.fr/Publications/2009/CNE09, INRIA Webpage
/research/publications/CNE09/IntelConf_Final.pdf, Paper Authors Version},
year = {2009},
date = {2009-03-01},
booktitle = {Intel Visual Computing Research Conference},
journal = {Intel Visual Computing Research Conference},
abstract = {For a long time, triangles have been considered the state-of-sthe-art primitive for fast interactive applications.
Only recently, with the dawn of programmability of graphics cards, different representations emerged. Especially for complex entities, triangles have difficulties in representing convincing details and faithful approximations quickly become costly. In this work we investigate Voxels. Voxels can represent very rich and detailed objects and are of crucial importance in medical contexts. Nonetheless, one major downside is their significant memory consumption. Here, we propose an out-of-core method to deal with large volumes in real-time. Only little CPU interaction is needed which shifts the workload towards the GPU. This makes the use of large voxel data sets even easier than the, usually complicated, triangle-based LOD mechanisms that often rely on the CPU. This simplicity might even foreshadow the use of volume data, in game contexts. The latter we underline by presenting very efficient algorithms to approximate standard effects, such as soft shadows, or depth of field.},
keywords = {Voxel, out-of-core, filtering, voxelization, gpu, ray-tracing, depth-of-field, real-time rendering, ray-casting, octree, GigaVoxels, cache}
}
For a long time, triangles have been considered the state-of-sthe-art primitive for fast interactive applications.
Only recently, with the dawn of programmability of graphics cards, different representations emerged. Especially for complex entities, triangles have difficulties in representing convincing details and faithful approximations quickly become costly. In this work we investigate Voxels. Voxels can represent very rich and detailed objects and are of crucial importance in medical contexts. Nonetheless, one major downside is their significant memory consumption. Here, we propose an out-of-core method to deal with large volumes in real-time. Only little CPU interaction is needed which shifts the workload towards the GPU. This makes the use of large voxel data sets even easier than the, usually complicated, triangle-based LOD mechanisms that often rely on the CPU. This simplicity might even foreshadow the use of volume data, in game contexts. The latter we underline by presenting very efficient algorithms to approximate standard effects, such as soft shadows, or depth of field. |
 | GigaVoxels : Ray-Guided Streaming for Efficient and Detailed Voxel Rendering Crassin, Cyril; Neyret, Fabrice; Lefebvre, Sylvain; Eisemann, Elmar ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D), ACM, 2009. (Inproceeding) (Abstract | Links | BibTeX | Tags: Voxel, rendering, out-of-core, filtering, gpu, real-time rendering, ray-casting, octree, cache) @inproceedings{CNLE09,
name = {GigaVoxels : Ray-Guided Streaming for Efficient and Detailed Voxel Rendering},
author = {Crassin, Cyril and Neyret, Fabrice and Lefebvre, Sylvain and Eisemann, Elmar},
url = {http://artis.imag.fr/Publications/2009/CNLE09, INRIA Paper Page
http://maverick.inria.fr/Publications/2009/CNLE09/CNLE09.pdf, Paper authors version},
year = {2009},
date = {2009-02-01},
booktitle = {ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D)},
publisher = {ACM},
abstract = {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 (81923 resolution), of hypertextured meshes (163843 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.},
keywords = {Voxel, rendering, out-of-core, filtering, gpu, real-time rendering, ray-casting, octree, cache}
}
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 (81923 resolution), of hypertextured meshes (163843 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. |
