research-article

GVDB: raytracing sparse voxel database structures on the GPU

Author:

Rama Karl HoetzleinAuthors Info & Claims

HPG '16: Proceedings of High Performance Graphics

Pages 109 - 117

Published: 20 June 2016 Publication History

Abstract

Simulation and rendering of sparse volumetric data have different constraints and solutions depending on the application area. Generating precise simulations and understanding very large data are problems in scientific visualization, whereas convincing simulations and realistic visuals are challenges in motion pictures. Both require volumes with dynamic topology, very large domains, and efficient high quality rendering. We present the GPU voxel database structure, GVDB, based on the voxel database topology of Museth [Mus13], as a method for efficient GPU-based compute and raytracing on a sparse hierarchy of grids. GVDB introduces an indexed memory pooling design for dynamic topology, and a novel hierarchical traversal for efficient raytracing on the GPU. Examples are provided for ray sampling of volumetric data, rendering of isosurfaces with multiple scattering, and raytracing of level sets. We demonstrate that GVDB can give large performance improvements over CPU methods with identical quality.

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Cited By

Lin DWyman CYuksel C(2021)Fast volume rendering with spatiotemporal reservoir resamplingACM Transactions on Graphics10.1145/3478513.348049940:6(1-18)Online publication date: 10-Dec-2021
https://dl.acm.org/doi/10.1145/3478513.3480499
Gao MWang XWu KPradhana ASifakis EYuksel CJiang C(2018)GPU optimization of material point methodsACM Transactions on Graphics10.1145/3272127.327504437:6(1-12)Online publication date: 4-Dec-2018
https://dl.acm.org/doi/10.1145/3272127.3275044

Index Terms

GVDB: raytracing sparse voxel database structures on the GPU
1. Computing methodologies
  1. Computer graphics
    1. Rendering
      1. Ray tracing

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Information

Published In

cover image ACM Conferences

HPG '16: Proceedings of High Performance Graphics

June 2016

186 pages

ISBN:9783038680086

General Chairs:
David Luebke
NVIDIA Research
,
Steve Molnar
NVIDIA

Sponsors

SAMSUNG: SAMSUNG
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
AMD 64 Opteron
Disney Research: Disney Research
NVIDIA
Intel: Intel
EUROGRAPHICS: The European Association for Computer Graphics

Publisher

Eurographics Association

Goslar, Germany

Publication History

Published: 20 June 2016

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Research-article

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HPG '16

Sponsor:

SAMSUNG
SIGGRAPH
Disney Research
Intel
EUROGRAPHICS

HPG '16: High Performance Graphics 2016

June 20 - 22, 2016

Dublin, Ireland

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Overall Acceptance Rate 15 of 44 submissions, 34%

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Cited By

Lin DWyman CYuksel C(2021)Fast volume rendering with spatiotemporal reservoir resamplingACM Transactions on Graphics10.1145/3478513.348049940:6(1-18)Online publication date: 10-Dec-2021
https://dl.acm.org/doi/10.1145/3478513.3480499
Gao MWang XWu KPradhana ASifakis EYuksel CJiang C(2018)GPU optimization of material point methodsACM Transactions on Graphics10.1145/3272127.327504437:6(1-12)Online publication date: 4-Dec-2018
https://dl.acm.org/doi/10.1145/3272127.3275044

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