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An intrinsic algorithm for parallel Poisson disk sampling on arbitrary surfaces.

Xiang Ying1, Shi-Qing Xin, Qian Sun

  • 1School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, BLK N4, Singapore 639798, Singapore. ying0008@ntu.edu.sg

IEEE Transactions on Visualization and Computer Graphics
|July 13, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel intrinsic algorithm for parallel Poisson disk sampling on arbitrary surfaces. It ensures uniform, unbiased distribution without partitioning, enabling efficient surface sampling in any dimension.

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Area of Science:

  • Computer Graphics
  • Computational Geometry

Background:

  • Poisson disk sampling is crucial for visual computing due to its spatial and spectral properties.
  • Generating Poisson disks on complex surfaces is challenging compared to Euclidean space.

Purpose of the Study:

  • To develop an intrinsic, parallel algorithm for Poisson disk sampling on arbitrary surfaces.
  • To overcome limitations of existing methods that partition surfaces or use spatial data structures.

Main Methods:

  • An intrinsic algorithm assigning unique, unbiased random priorities to sample candidates.
  • Parallel processing of candidates by multiple threads, resolving conflicts via priority values.
  • Method is independent of the embedding space, allowing sampling on surfaces in IR(n).

Main Results:

  • Guarantees uniformly and randomly distributed Poisson disks without bias.
  • Achieves adaptive sampling by manipulating spatially varying density functions.
  • First intrinsic, parallel, and accurate algorithm for surface Poisson disk sampling.

Conclusions:

  • The proposed intrinsic parallel algorithm offers an efficient and accurate solution for Poisson disk sampling on surfaces.
  • The method's independence from embedding space broadens its applicability to various surface types and dimensions.
  • Enables adaptive sampling, enhancing flexibility for different visual computing applications.