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A comment on Using k-d trees to efficiently calculate nearest neighbors in 3D vector space.

Re:Using k-d trees to efficiently calculate nearest neighbor... by georgecmu on Saturday February 10, 2018 @02:19PM (#16348985)
Extract from Andrew Moore's PhD Thesis: Ecient Memory-based Learning for Robot Control PhD. Thesis; Technical Report No. 209, Computer Laboratory, University of Cambridge. 1991.

[1991] https://www.ri.cmu.edu/pub_files/pub1/moore_andrew_1991_1/mo...

Table 6.4 describes my actual implementation of the nearest neighbour algorithm. It is called with four parameters: the kd-tree, the target domain vector, a representation of a hyperrectangle in Domain, and a value indicating the maximum distance from the target which is worth searching.

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Re:Using k-d trees to efficiently calculate nearest neighbor... by amerine on Saturday February 10, 2018 @10:01PM (#16350949)
It’s been a really long time since I’ve read that paper. Thanks for the link and the call-out out 6.4. That “target distance worth searching” seems interesting when thinking about color-replacement.
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