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Fast Generation of Poisson-Disk Samples on Mesh Surfaces by Progressive Sample Projection

Published:24 August 2018Publication History
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Abstract

Generating well-distributed Poisson-disk samples with a blue noise power spectrum on 3D meshes is required by a wide range of applications in computer graphics. We introduce a novel method called Progressive Sample Projection that can generate massive Poisson-disk samples on mesh surfaces in very short time by projecting blue noise sample patterns from 2D planar space onto meshes. This parallel scheme can exploit full parallelism of GPU without deep recursion or atomic operations, which are often required by other methods. Compared with state-of-the-art methods, the effective generation rate of our method can be 2x to orders of magnitude faster, while still preserving good sample quality. This method is also progressive with memory usage bounded, thus being flexible for both performance and quality demanding work. The implementation is straightforward and easy to understand. It can be easily applied to adaptive sampling as well.

References

  1. Michael Balzer, Thomas Schlömer, and Oliver Deussen. 2009. Capacity-constrained Point Distributions: A Variant of Lloyd's Method. ACM Trans. Graph. 28, 3, Article 86 (July 2009), 8 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. John Bowers, Rui Wang, Li-Yi Wei, and David Maletz. 2010. Parallel Poisson Disk Sampling with Spectrum Analysis on Surfaces. ACM Trans. Graph. 29, 6, Article 166 (Dec. 2010), 10 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. D. Cline, S. Jeschke, K. White, A. Razdan, and P. Wonka. 2009. Dart Throwing on Surfaces. In Proceedings of the Twentieth Eurographics Conference on Rendering (EGSR'09). Eurographics Association, Goslar Germany, Germany, 1217--1226. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Michael F. Cohen, Jonathan Shade, Stefan Hiller, and Oliver Deussen. 2003. Wang Tiles for Image and Texture Generation. ACM Trans. Graph. 22, 3 (July 2003), 287--294. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Robert L. Cook. 1986. Stochastic Sampling in Computer Graphics. ACM Trans. Graph. 5, 1 (Jan. 1986), 51--72. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. M. Corsini, P. Cignoni, and R. Scopigno. 2012. Efficient and Flexible Sampling with Blue Noise Properties of Triangular Meshes. IEEE Transactions on Visualization and Computer Graphics 18, 6 (June 2012), 914--924. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Mark A. Z. Dippé and Erling Henry Wold. 1985. Antialiasing Through Stochastic Sampling. SIGGRAPH Comput. Graph. 19, 3 (July 1985), 69--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Fredo Durand. 2011. A frequency analysis of Monte-Carlo and other numerical integration schemes. Tech. Rep. MIT-CSAILTR-2011-052 (2011). http://hdl.handle.net/1721.1/67677Google ScholarGoogle Scholar
  9. Mohamed S. Ebeida, Scott A. Mitchell, Anjul Patney, Andrew A. Davidson, and John D. Owens. 2012. A Simple Algorithm for Maximal Poisson-Disk Sampling in High Dimensions. Comput. Graph. Forum 31, 2pt4 (May 2012), 785--794. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Manuel N. Gamito and Steve C. Maddock. 2009. Accurate Multidimensional Poisson-disk Sampling. ACM Trans. Graph. 29, 1, Article 8 (Dec. 2009), 19 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Simon Green. 2010. Particle simulation using cuda. NVIDIA whitepaper 6 (2010), 121--128.Google ScholarGoogle Scholar
  12. Jianwei Guo, Dong-Ming Yan, Xiaohong Jia, and Xiaopeng Zhang. 2015. Efficient Maximal Poisson-disk Sampling and Remeshing on Surfaces. Comput. Graph. 46, C (Feb. 2015), 72--79. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Cheuk Yiu Ip, M. Adil Yalçin, David Luebke, and Amitabh Varshney. 2013. PixelPie: Maximal Poisson-disk Sampling with Rasterization. In Proceedings of the 5th High-Performance Graphics Conference (HPG '13). ACM, New York, NY, USA, 17--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Min Jiang, Yahan Zhou, Rui Wang, Richard Southern, and Jian Jun Zhang. 2015. Blue Noise Sampling Using an SPH-based Method. ACM Trans. Graph. 34, 6, Article 211 (Oct. 2015), 11 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Nima Khademi Kalantari and Pradeep Sen. 2012. Fast Generation of Approximate Blue Noise Point Sets. Comput. Graph. Forum 31, 4 (June 2012), 1529--1535. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Johannes Kopf, Daniel Cohen-Or, Oliver Deussen, and Dani Lischinski. 2006. Recursive Wang Tiles for Real-time Blue Noise. ACM Trans. Graph. 25, 3 (July 2006), 509--518. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Ares Lagae and Philip Dutré. 2005. A Procedural Object Distribution Function. ACM Trans. Graph. 24, 4 (Oct. 2005), 1442--1461. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Ares Lagae and Philip Dutre. 2008. A Comparison of Methods for Generating Poisson Disk Distributions. Computer Graphics Forum (2008).Google ScholarGoogle Scholar
  19. Steven G. Parker, James Bigler, Andreas Dietrich, Heiko Friedrich, Jared Hoberock, David Luebke, David McAllister, Morgan McGuire, Keith Morley, Austin Robison, and Martin Stich. 2010. OptiX: A General Purpose Ray Tracing Engine. ACM Trans. Graph. 29, 4, Article 66 (July 2010), 13 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Adrien Pilleboue, Gurprit Singh, David Coeurjolly, Michael Kazhdan, and Victor Ostromoukhov. 2015. Variance Analysis for Monte Carlo Integration. ACM Trans. Graph. 34, 4, Article 124 (July 2015), 14 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Bernhard Reinert, Tobias Ritschel, Hans-Peter Seidel, and Iliyan Georgiev. 2016. Projective Blue-Noise Sampling. Comput. Graph. Forum 35, 1 (Feb. 2016), 285--295. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Kartic Subr and Jan Kautz. 2013. Fourier Analysis of Stochastic Sampling Strategies for Assessing Bias and Variance in Integration. ACM Trans. Graph. 32, 4, Article 128 (July 2013), 12 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Tong Wang and Reiji Suda. 2017. Fast Maximal Poisson-disk Sampling by Randomized Tiling. In Proceedings of High Performance Graphics (HPG '17). ACM, New York, NY, USA, Article 16, 10 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Li-Yi Wei. 2008. Parallel Poisson Disk Sampling. ACM Trans. Graph. 27, 3, Article 20 (Aug. 2008), 9 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Li-Yi Wei and Rui Wang. 2011. Differential Domain Analysis for Non-uniform Sampling. ACM Trans. Graph. 30, 4, Article 50 (July 2011), 10 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Dong-Ming Yan, Jian-Wei Guo, Bin Wang, Xiao-Peng Zhang, and Peter Wonka. 2015. A survey of blue-noise sampling and its applications. Journal of Computer Science and Technology 30, 3 (2015), 439--452.Google ScholarGoogle ScholarCross RefCross Ref
  27. Dong-Ming Yan and Peter Wonka. 2013. Gap Processing for Adaptive Maximal Poisson-disk Sampling. ACM Trans. Graph. 32, 5, Article 148 (Oct. 2013), 15 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. X. Ying, S. Q. Xin, Q. Sun, and Y. He. 2013. An Intrinsic Algorithm for Parallel Poisson Disk Sampling on Arbitrary Surfaces. IEEE Transactions on Visualization and Computer Graphics 19, 9 (Sept 2013), 1425--1437. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Cem Yuksel. 2015. Sample Elimination for Generating Poisson Disk Sample Sets. Comput. Graph. Forum 34, 2 (May 2015), 25--32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Sen Zhang, Jianwei Guo, Hui Zhang, Xiaohong Jia, Dong-Ming Yan, Junhai Yong, and Peter Wonka. 2016. Capacity Constrained Blue-noise Sampling on Surfaces. Comput. Graph. 55, C (April 2016), 44--54. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image Proceedings of the ACM on Computer Graphics and Interactive Techniques
          Proceedings of the ACM on Computer Graphics and Interactive Techniques  Volume 1, Issue 2
          August 2018
          223 pages
          EISSN:2577-6193
          DOI:10.1145/3273023
          Issue’s Table of Contents

          Copyright © 2018 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 24 August 2018
          Published in pacmcgit Volume 1, Issue 2

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