topology-caching for dynamic particle volume raycasting jens orthmann, maik keller and andreas kolb,...
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![Page 1: Topology-Caching for Dynamic Particle Volume Raycasting Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen](https://reader035.vdocuments.mx/reader035/viewer/2022062421/56649c895503460f94941d69/html5/thumbnails/1.jpg)
Topology-Caching for Dynamic Particle Volume Raycasting
Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen
![Page 2: Topology-Caching for Dynamic Particle Volume Raycasting Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen](https://reader035.vdocuments.mx/reader035/viewer/2022062421/56649c895503460f94941d69/html5/thumbnails/2.jpg)
2Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen
Outline
MotivationRecent TechniquesGPU Raycasting System
Node-CacheInfluence-CacheSlab-Cache
Video & ResultsConclusion
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3Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen
Motivation
Real-time particle-based flow simulations:Particles carry physical flow properties like density, concentration, etc.Rendering color-coded sprites is insufficient.
A =6
A = 7df dsgfA = 7
![Page 4: Topology-Caching for Dynamic Particle Volume Raycasting Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen](https://reader035.vdocuments.mx/reader035/viewer/2022062421/56649c895503460f94941d69/html5/thumbnails/4.jpg)
4Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen
Recent Techniques
W. van der Laan et. al., 2009: “Screen Space Fluid Rendering with Curvature Flow“
Image-based Rendering Surface reconstruction Fast: 64K around 55-20 fps No Volume-Rendering
Splatting Standard for particles Fast: 200K around 43 fps Blurred images
P. Schlegel et al., 2009: “Layered Volume Splatting“
Texture-based Raycasting High quality Large datasets up to 42M Requires pre-computation
Fraedrich et. al., to appear: “Efficient High-Quality Volume Rendering of SPH Data“
![Page 5: Topology-Caching for Dynamic Particle Volume Raycasting Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen](https://reader035.vdocuments.mx/reader035/viewer/2022062421/56649c895503460f94941d69/html5/thumbnails/5.jpg)
Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen
GPU Volume Raycasting
In each frame:K. Zhou et al., 2010: “Data Parallel Octrees for Surface Reconstruction“
5
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6Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen
Tree Traversal: Node Cache
Assumption: the packet’s extend is smaller than the size of a node
Implication: Node pre-fetchingNeighbor traversal
J .Wilhelms et al., 1992: “Octrees for faster isosurfacegeneration“
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7Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen
Sampling: Influence Cache
Assumption: field reconstruction works with particles in the local neighborhood:
Implication: redundant particle assignment.
P. Koumoutsakos et al., 2008: “Flow Simulation using Particles“
G. Guennebaud et al., 2008: “Dynamic Sampling and Renderingof Algebraic Point Set Surfaces“
![Page 8: Topology-Caching for Dynamic Particle Volume Raycasting Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen](https://reader035.vdocuments.mx/reader035/viewer/2022062421/56649c895503460f94941d69/html5/thumbnails/8.jpg)
8Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen
Sampling: Influence Cache
Problem: Undersampling at higher distances.
Solution: Buttom-up merging of particles.
[Nyquist Theorem]
W. Hong et al., 2008: “Adaptive particles for incompressible fluid simulation“
M. Zwicker et al., 2003: “EWA Splatting“
![Page 9: Topology-Caching for Dynamic Particle Volume Raycasting Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen](https://reader035.vdocuments.mx/reader035/viewer/2022062421/56649c895503460f94941d69/html5/thumbnails/9.jpg)
9Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen
Compositing: Slab-Cache
Problem: same particles are sampled multiple times (gradients).
Solution: Slab-based front-to-back compositingParticles scatter to several slices at once
J. Mensmann et al., 2010: “An advanced volume raycasting technique using GPU stream processing”
![Page 10: Topology-Caching for Dynamic Particle Volume Raycasting Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen](https://reader035.vdocuments.mx/reader035/viewer/2022062421/56649c895503460f94941d69/html5/thumbnails/10.jpg)
10Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen
Compositing: Slab-Cache
[Recently] Observation: too many samples in distant regions
Solution: adaptive step size with opacity correction Fraedrich et al., to appear: “Efficient High-Quality Volume Rendering of SPH Data“
![Page 11: Topology-Caching for Dynamic Particle Volume Raycasting Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen](https://reader035.vdocuments.mx/reader035/viewer/2022062421/56649c895503460f94941d69/html5/thumbnails/11.jpg)
11Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen
Results
Errors of the packet traversal:
[Recently] Adaptive Steps, 8x8 Packets, CUDA on GTX 400 [unoptimized]:
60k 130k 250k 500k0
50
100
150
200
250
300
350
400
450
13 17 23 2730 4060
110
60100
130
400
TreeEm/AdsGradient
Number of Particles
Mill
iseco
nd
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12Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen
Conclusion
Raycasting Pipeline: three optimization strategies[Per Slab] Node-Cache
[Per Node] Influence-Cache
[Per Slab] Slab-Cache
Future WorkOptimization for GTX 400 (Occupancy)Automatic transfer-functionsSplitting kernel into several distinct steps
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13Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen
Thank you
Thank you for your attention