Data-Parallel Decompression of Triangle Mesh Topology

We propose a lossless, single‐rate triangle mesh topology codec tailored for fast data‐parallel GPU decompression. Our compression scheme coherently orders generalized triangle strips in memory. To unpack generalized triangle strips efficiently, we propose a novel parallel and scalable algorithm. We...

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Veröffentlicht in:Computer graphics forum Jg. 31; H. 8; S. 2541 - 2553
Hauptverfasser: Meyer, Quirin, Keinert, Benjamin, Sußner, Gerd, Stamminger, Marc
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford, UK Blackwell Publishing Ltd 01.12.2012
Schlagworte:
Algorithms
Analysis
Benchmarks
Coherence
Compressing
Computer graphics
Data compression
Digital imaging
Finite element method
I.3.1 [Computer Graphics]: Hardware Architecture-Parallel processing
I.3.6 [Computer Graphics]: Methodology and Techniques-Graphics data structures and data types
Image processing systems
Mathematical models
Strip
Studies
Topology
Triangles
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
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Zusammenfassung:We propose a lossless, single‐rate triangle mesh topology codec tailored for fast data‐parallel GPU decompression. Our compression scheme coherently orders generalized triangle strips in memory. To unpack generalized triangle strips efficiently, we propose a novel parallel and scalable algorithm. We order vertices coherently to further improve our compression scheme. We use a variable bit‐length code for additional compression benefits, for which we propose a scalable data‐parallel decompression algorithm. For a set of standard benchmark models, we obtain (min: 3.7, med: 4.6, max: 7.6) bits per triangle. Our CUDA decompression requires only about 15% of the time it takes to render the model even with a simple shader. We propose a lossless, single‐rate triangle mesh topology codec tailored for fast data‐parallel GPU decompression. Our compression scheme coherently orders generalized triangle strips in memory. To unpack generalized triangle strips efficiently, we propose a novel parallel and scalable algorithm. We order vertices coherently to further improve our compression scheme. We use a variable bit‐length code for additional compression benefits, for which we propose a scalable data‐parallel decompression algorithm. For a set of standard benchmark models, we obtain (min: 3.7, med: 4.6, max: 7.6) bits per triangle. Our CUDA decompression requires only about 15% of the time it takes to render the model even with a simple shader.
Bibliographie:ark:/67375/WNG-3TRNQH1X-5
istex:D64FC3FC25972CB223E06F3D5517213E82E5D355
ArticleID:CGF3221
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ISSN:0167-7055
1467-8659
DOI:10.1111/j.1467-8659.2012.03221.x