The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Adaptively sampled particle fluids
Open accessAbstract
We present novel adaptive sampling algorithms for particle-based fluid simulation. We introduce a sampling condition based on geometric local feature size that allows focusing computational resources in geometrically complex regions, while reducing the number of particles deep inside the fluid or near thick flat surfaces. Further performance gains are achieved by varying the sampling density according to visual importance. In addition, we propose a novel fluid surface definition based on approximate particle-to-surface distances that are carried along with the particles and updated appropriately. The resulting surface reconstruction method has several advantages over existing methods, including stability under particle resampling and suitability for representing smooth flat surfaces. We demonstrate how our adaptive sampling and distance-based surface reconstruction algorithms lead to significant improvements in time and memory as compared to single resolution particle simulations, without significantly affecting the fluid flow behavior.
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Cites
Marching cubes: A high resolution 3D surface construction algorithm
Lorensen, W. E., & Cline, H. E. (1987) · Proceedings of the 14th annual conference on Computer graphics and interactive techniques · doi:10.1145/37401.37422 · Cited by 10,104
Level Set Methods and Fast Marching Methods
Sethian, J. A. (1999) · Medical Entomology and Zoology · Cited by 3,616
Smoothed particle hydrodynamics
Monaghan, J. J. (2005) · Reports on Progress in Physics · doi:10.1088/0034-4885/68/8/r01 · Cited by 2,754
Particle-Based Fluid Simulation for Interactive Applications
Müller, M., Charypar, D., & Groß, M. H. (2003) · doi:10.2312/sca03/154-159 · Cited by 999
A new Voronoi-based surface reconstruction algorithm
Amenta, N., Bern, M., & Kamvysselis, M. (1998) · Proceedings of the 25th annual conference on Computer graphics and interactive techniques - SIGGRAPH '98 · doi:10.1145/280814.280947 · Cited by 901
Realistic Animation of Liquids
Foster, N., & Metaxas, D. (1996) · Graphical Models and Image Processing · doi:10.1006/gmip.1996.0039 · Cited by 674
Zhu, Y., & Bridson, R. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073298 · Cited by 648
Adaptively sampled distance fields: a general representation of shape for computer graphics
Frisken, S. F., Perry, R. N., Rockwood, A. P., & Jones, T. R. (2000) · Proceedings of the 27th annual conference on Computer graphics and interactive techniques - SIGGRAPH '00 · doi:10.1145/344779.344899 · Cited by 577
Smoothed Particles: A new paradigm for animating highly deformable bodies
Desbrun, M., & Gascuel, M.-P. (1996) · Eurographics · doi:10.1007/978-3-7091-7486-9_5 · Cited by 416
Level Set Methods and Fast Marching Methods
Suba�i�, M. (2003) · Journal of Computing and Information Technology · doi:10.2498/cit.2003.01.07 · Cited by 294
Cited by
Predictive-corrective incompressible SPH
Solenthaler, B., & Pajarola, R. (2009) · ACM Transactions on Graphics · doi:10.1145/1531326.1531346 · Cited by 362
Metadata
- Authors
- Adams, B., Pauly, M., Keiser, R., & Guibas, L. J.
- Venue
- ACM SIGGRAPH 2007 papers
- Publisher
- ACM
- Published
- 2007-07-29
- Pages
- 48
- OpenAlex
- W2136220107
- Cited by
- 264
- References
- 21
- FWCI
- 19.64
- Type
- conference paper
- Language
- en
- Open access
- green
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