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| Author(s) |
Schloemer, T., Heck, D., Deussen, O. |
| Title |
Farthest-Point Optimized Point Sets with Maximized Minimum Distance |
| 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 "farthest
point," 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 sized
subsets, each with large mutual distances; the resulting partitionings
yield improved results in more general integration problems
such as those occurring in physically based rendering. |
| Download |
StHdDo11.pdf |
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