Research
I am a Research Scientist at NVIDIA Research (My NVIDIA research page). I joined NVIDIA in 2011 after obtaining my Ph.D. degree from Grenoble University at INRIA in France (thesis document here). My research interests include real-time realistic rendering, global illumination, alternative geometric and material representations (voxel-based), ray-tracing, anti-aliasing techniques, distributed rendering, as well as out-of-core data management. My predominant research direction focuses on the use of pre-filtered geometric representations for the efficient anti-aliased rendering of detailed scenes and complex objects, as well as global illumination effects. My most impactful contributions are the GigaVoxels rendering pipeline and the GIVoxels/VXGI voxel-based indirect illumination technique, with several hardware implications in the NVIDIA Maxwell architecture.
Publications
aggregate anti-aliasing cache cloud rendering clouds rendering cone-tracing deferred depth-of-field filtering GigaVoxels Global Illumination gpu hypertextures Lighting octree out-of-core pre-filtering ray-casting ray-tracing Real-Time real-time rendering rendering shading soft shadows sparse visibility volumes Voxel voxelization voxels
2011 | |
 | GigaVoxels: A Voxel-Based Rendering Pipeline For Efficient Exploration Of Large And Detailed Scenes Crassin, Cyril Grenoble University, 2011. (PhD Thesis) (Abstract | Links | BibTeX | Tags: Voxel, Global Illumination, Real-Time, rendering, out-of-core, gpu, ray-tracing, cone-tracing, octree) @phdthesis{Cra11, name = {GigaVoxels: A Voxel-Based Rendering Pipeline For Efficient Exploration Of Large And Detailed Scenes}, author = {Crassin, Cyril}, url = {http://maverick.inria.fr/Membres/Cyril.Crassin/thesis/CCrassinThesis_EN_Web.pdf, Thesis http://maverick.inria.fr/Publications/2011/Cra11/, INRIA Publication Page}, year = {2011}, date = {2011-07-12}, school = {Grenoble University}, abstract = {In this thesis, we present a new approach to efficiently render large scenes and detailed objects in real-time. Our approach is based on a new volumetric pre-filtered geometry representation and an associated voxel-based approximate cone tracing that allows an accurate and high performance rendering with high quality filtering of highly detailed geometry. In order to bring this voxel representation as a standard real-time rendering primitive, we propose a new GPU-based approach designed to entirely scale to the rendering of very large volumetric datasets. Our system achieves real-time rendering performance for several billion voxels. 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. Our solution is based on an adaptive hierarchical data representation depending on the current view and occlusion information, coupled to an efficient ray-casting rendering algorithm. We introduce a new GPU cache mechanism providing a very efficient paging of data in video memory and implemented as a very efficient data-parallel process. This cache is coupled with a data production pipeline able to dynamically load or produce voxel data directly on the GPU. One key element of our method is to guide data production and caching in video memory directly based on data requests and usage information emitted directly during rendering. We demonstrate our approach with several applications. We also show how our pre-filtered geometry model and approximate cone tracing can be used to very efficiciently achieve blurry effects and real-time indirect lighting.}, keywords = {Voxel, Global Illumination, Real-Time, rendering, out-of-core, gpu, ray-tracing, cone-tracing, octree} } In this thesis, we present a new approach to efficiently render large scenes and detailed objects in real-time. Our approach is based on a new volumetric pre-filtered geometry representation and an associated voxel-based approximate cone tracing that allows an accurate and high performance rendering with high quality filtering of highly detailed geometry. In order to bring this voxel representation as a standard real-time rendering primitive, we propose a new GPU-based approach designed to entirely scale to the rendering of very large volumetric datasets. Our system achieves real-time rendering performance for several billion voxels. 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. Our solution is based on an adaptive hierarchical data representation depending on the current view and occlusion information, coupled to an efficient ray-casting rendering algorithm. We introduce a new GPU cache mechanism providing a very efficient paging of data in video memory and implemented as a very efficient data-parallel process. This cache is coupled with a data production pipeline able to dynamically load or produce voxel data directly on the GPU. One key element of our method is to guide data production and caching in video memory directly based on data requests and usage information emitted directly during rendering. We demonstrate our approach with several applications. We also show how our pre-filtered geometry model and approximate cone tracing can be used to very efficiciently achieve blurry effects and real-time indirect lighting. |
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 | |
 | 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} } |
 | 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. |