Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99298
Title: An intrinsic algorithm for parallel Poisson disk sampling on arbitrary surfaces
Authors: Ying, Xiang
He, Ying
Xin, Shi-Qing
Sun, Qian
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2013
Source: Ying, X., Xin, S.-Q., Sun, Q., & He, Y. (2013). An Intrinsic Algorithm for Parallel Poisson Disk Sampling on Arbitrary Surfaces. IEEE Transactions on Visualization and Computer Graphics, 19(9), 1425-1437.
Series/Report no.: IEEE transactions on visualization and computer graphics
Abstract: Poisson disk sampling has excellent spatial and spectral properties, and plays an important role in a variety of visual computing. Although many promising algorithms have been proposed for multidimensional sampling in euclidean space, very few studies have been reported with regard to the problem of generating Poisson disks on surfaces due to the complicated nature of the surface. This paper presents an intrinsic algorithm for parallel Poisson disk sampling on arbitrary surfaces. In sharp contrast to the conventional parallel approaches, our method neither partitions the given surface into small patches nor uses any spatial data structure to maintain the voids in the sampling domain. Instead, our approach assigns each sample candidate a random and unique priority that is unbiased with regard to the distribution. Hence, multiple threads can process the candidates simultaneously and resolve conflicts by checking the given priority values. Our algorithm guarantees that the generated Poisson disks are uniformly and randomly distributed without bias. It is worth noting that our method is intrinsic and independent of the embedding space. This intrinsic feature allows us to generate Poisson disk patterns on arbitrary surfaces in IRn. To our knowledge, this is the first intrinsic, parallel, and accurate algorithm for surface Poisson disk sampling. Furthermore, by manipulating the spatially varying density function, we can obtain adaptive sampling easily.
URI: https://hdl.handle.net/10356/99298
http://hdl.handle.net/10220/17366
ISSN: 1077-2626
DOI: 10.1109/TVCG.2013.63
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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