Bilateral blue noise sampling

Abstract

Blue noise sampling is an important component in many graphics applications, but existing techniques consider mainly the spatial positions of samples, making them less effective when handling problems with non-spatial features. Examples include biological distribution in which plant spacing is influenced by non-positional factors such as tree type and size, photon mapping in which photon flux and direction are not a direct function of the attached surface, and point cloud sampling in which the underlying surface is unknown a priori. These scenarios can benefit from blue noise sample distributions, but cannot be adequately handled by prior art. Inspired by bilateral filtering, we propose a bilateral blue noise sampling strategy. Our key idea is a general formulation to modulate the traditional sample distance measures, which are determined by sample position in spatial domain, with a similarity measure that considers arbitrary per sample attributes. This modulation leads to the notion of <i>bilateral</i> blue noise whose properties are influenced by not only the uniformity of the sample positions but also the similarity of the sample attributes. We describe how to incorporate our modulation into various sample analysis and synthesis methods, and demonstrate applications in object distribution, photon density estimation, and point cloud sub-sampling.

DOI: 10.1145/2508363.2508375

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Cite this paper

@article{Chen2013BilateralBN, title={Bilateral blue noise sampling}, author={Jiating Chen and Xiaoyin Ge and Li-Yi Wei and Bin Wang and Yusu Wang and Huamin Wang and Yun Fei and Kang-Lai Qian and Jun-Hai Yong and Wenping Wang}, journal={ACM Trans. Graph.}, year={2013}, volume={32}, pages={216:1-216:11} }