 | Dynamic Sparse Voxel Octrees for Next-Gen Real-Time Rendering Crassin, Cyril SIGGRAPH 2012 Course : Beyond Programmable Shading, ACM SIGGRAPH, 2012. (Inproceeding) (Abstract | Links | BibTeX | Tags: Voxel, Real-Time, rendering, gpu, depth-of-field, soft shadows, cone-tracing, octree, VCT) @inproceedings{Cra12, name = {Dynamic Sparse Voxel Octrees for Next-Gen Real-Time Rendering}, author = {Cyril Crassin}, url = {http://www.icare3d.org/research/publications/Cra12/04_crassinVoxels_bps2012.pdf, Slides as PDF http://bps12.idav.ucdavis.edu/, Beyond Programmable Shading SIGGRAPH 2012 Webpage}, year = {2012}, date = {2012-08-07}, booktitle = {SIGGRAPH 2012 Course : Beyond Programmable Shading}, publisher = {ACM SIGGRAPH}, abstract = {Sparse Voxel Octrees have gained a growing interest in the industry over the last few years. In this course, I show how SVOs allow building and storing a multi-resolution pre-filtered representation of a scene's geometry. I also introduce the Voxel Cone Tracing (VCT) technique, which can be used to efficiently integrate visibility and evaluate light transport inside a scene, by replacing massive oversampling inside a cone-shaped footprint, by only one single ray casted inside the pre-filtered voxel representation (adapting the geometric resolution to the sampling resolution). Finally, I show multiple use-cases of such techniques for real-time applications : Rendering massive and highly detailed scenes without any aliasing artifacts, highly controllable multi-bounces global illumination, simulating depth-of-field effects, approximated soft shadows, fully procedural content generation... }, keywords = {Voxel, Real-Time, rendering, gpu, depth-of-field, soft shadows, cone-tracing, octree, VCT} }
Sparse Voxel Octrees have gained a growing interest in the industry over the last few years. In this course, I show how SVOs allow building and storing a multi-resolution pre-filtered representation of a scene's geometry. I also introduce the Voxel Cone Tracing (VCT) technique, which can be used to efficiently integrate visibility and evaluate light transport inside a scene, by replacing massive oversampling inside a cone-shaped footprint, by only one single ray casted inside the pre-filtered voxel representation (adapting the geometric resolution to the sampling resolution). Finally, I show multiple use-cases of such techniques for real-time applications : Rendering massive and highly detailed scenes without any aliasing artifacts, highly controllable multi-bounces global illumination, simulating depth-of-field effects, approximated soft shadows, fully procedural content generation...
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 | Beyond Triangles : GigaVoxels Effects In Video Games Crassin, Cyril; Neyret, Fabrice; Lefebvre, Sylvain; Sainz, Miguel; Eisemann, Elmar SIGGRAPH 2009 : Technical Talk + Poster (Best Poster Award Finalist), ACM SIGGRAPH, 2009. (Inproceeding) (Links | BibTeX | Tags: out-of-core, gpu, voxels, sparse, ray-tracing, depth-of-field, soft shadows) @inproceedings{CNLSE09, name = {Beyond Triangles : GigaVoxels Effects In Video Games}, author = {Crassin, Cyril and Neyret, Fabrice and Lefebvre, Sylvain and Sainz, Miguel and Eisemann, Elmar}, url = {http://artis.imag.fr/Publications/2009/CNLSE09, Talk INRIA Webpage http://maverick.inria.fr/Publications/2009/CNLSE09/GigaVoxels_Siggraph09_Slides.pdf, Slides PDF http://maverick.inria.fr/Publications/2009/CNLSE09/gigavoxels_siggraph09_talk.pdf, Sketch PDF}, year = {2009}, date = {2009-08-01}, booktitle = {SIGGRAPH 2009 : Technical Talk + Poster (Best Poster Award Finalist)}, publisher = {ACM SIGGRAPH}, keywords = {out-of-core, gpu, voxels, sparse, ray-tracing, depth-of-field, soft shadows} }
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 | 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. |