Farthest-Point Optimized Point Sets with Maximized Minimum Distance

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Date
2011
Authors
Schlömer, Thomas
Heck, Daniel
Deussen, Oliver
Journal Title
Journal ISSN
Volume Title
Publisher
ACM
Abstract
Efficient sampling often relies on irregular point sets that uniformly cover the sample space. We present a flexible and simple optimization strategy for such point sets. It is based on the idea of increasing the mutual distances by successively moving each point to the farthestpoint, i.e., the location that has the maximum distance from the rest of the point set. We present two iterative algorithms based on this strategy. The first is our main algorithm which distributes points in the plane. Our experimental results show that the resulting distributions have almost optimal blue noise properties and are highly suitable for image plane sampling. The second is a variant of the main algorithm that partitions any point set into equally sizedsubsets, each with large mutual distances; the resulting partitionings yield improved results in more general integration problems such as those occurring in physically based rendering
Description

        
@inproceedings{
10.1145:2018323.2018345
,
booktitle = {
Eurographics/ ACM SIGGRAPH Symposium on High Performance Graphics
},
editor = {
Carsten Dachsbacher and William Mark and Jacopo Pantaleoni
},
title = {{
Farthest-Point Optimized Point Sets with Maximized Minimum Distance
}},
author = {
Schlömer, Thomas
 and 
Heck, Daniel
 and 
Deussen, Oliver
},
year = {
2011
},
publisher = {
ACM
},
ISSN = {
2079-8687
},
ISBN = {
978-1-4503-0896-0
},
DOI = {
10.1145/2018323.2018345
}
}
Citation
URI
https://doi.org/10.1145/2018323.2018345
Collections
High-Performance Graphics 2011