Low-Discrepancy Blue Noise Sampling

Abdalla Ahmed; Hélène Perrier; David Coeurjolly; Victor Ostromoukhov; Jianwei Guo; Dong-Ming Yan; Hui Huang; Oliver Deussen

Low-Discrepancy Blue Noise Sampling

Abdalla Ahmed ¹ Hélène Perrier ^{2, 3} David Coeurjolly ² Victor Ostromoukhov ³ Jianwei Guo ⁴ Dong-Ming Yan ⁴ Hui Huang ^{5, 6} Oliver Deussen ^{1, 6}

1 Department of Computer and Information Science, University of Konstanz

2 M2DisCo - Geometry Processing and Constrained Optimization

LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

3 R3AM - Rendu Réaliste pour la Réalité Augmentée Mobile

LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

4 NLPR - National Laboratory of Pattern Recognition [Beijing]

5 Shenzhen Univerisity [Shenzhen]

6 SIAT - Shenzhen Institute of Advanced Technology [Shenzhen]

Abstract : We present a novel technique that produces two-dimensional low- discrepancy (LD) blue noise point sets for sampling. Using one- dimensional binary van der Corput sequences, we construct two- dimensional LD point sets, and rearrange them to match a target spectral profile while preserving their low discrepancy. We store the rearrangement information in a compact lookup table that can be used to produce arbitrarily large point sets. We evaluate our tech- nique and compare it to the state-of-the-art sampling approaches.

Keywords : Monte Carlo Sampling Low Discrepancy Quasi-Monte Carlo Methods QMC Blue Noise

Document type :

Journal articles

Domain :

Computer Science [cs] / Image Processing [eess.IV]

Computer Science [cs] / Computational Geometry [cs.CG]

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Submitted on : Tuesday, September 27, 2016 - 4:59:12 PM
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