k-D tree
by Guy Shechter
06 Mar 2004
(Updated 14 Jun 2004)
Perform closest point search or range query using a k-D tree implementation.
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| File Information |
| Description |
This distribution contains the KDTREE, KDTREEIDX, and KDRANGEQUERY functions.
KDTREE Find closest points using a k-D tree.
CP = KDTREE( REFERENCE, MODEL ) finds the closest points in
REFERENCE for each point in MODEL. The search is performed in an efficient manner by building a k-D tree from the datapoints in REFERENCE, and querying the tree for each datapoint in MODEL.
PTS = KDRANGEQUERY( ROOT, QUERYPT, DISTLIM ) finds all the points stored in the k-D tree ROOT that are within DISTLIM units from the QUERYPT. Proximity is quantified using a D-dimensional Euclidean (2-norm) distance.
Included are the source code files in kdtree/src, as well as precompiled mex files for the following systems:
- Mac OS X (Matlab 6.5)
- Win2K (MATLAB 6.1)
- Redhat Linux (MATLAB 6.5) |
| Acknowledgements |
This submission has inspired the following:
Iterative Closest Point Method, C++, Kdtree implementation in matlab , kd-tree for matlab
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| MATLAB release |
MATLAB 6.5 (R13)
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| Comments and Ratings (41) |
| 27 Apr 2004 |
Carles Matabosch
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| 20 Jun 2004 |
Dimitris Nicoloutsopoulos
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| 27 Jul 2004 |
Brian Bonnlander
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| 26 Sep 2004 |
Chris Kelly
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Steven Knudsen
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ambrose thompson
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martin richards
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| 30 Dec 2004 |
Ray Hu
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| 16 Mar 2005 |
A James
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João Ferreira
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wenchao hu
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Babak Taati
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Marzia De Lucia
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Samy Samy
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| 24 Nov 2006 |
Deepak Trivedi
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| 14 Mar 2007 |
Quan Ho
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| 14 Mar 2007 |
Quan Ho
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| 18 Dec 2007 |
ramanathan natarajan
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| 18 Jan 2008 |
Mert Sabuncu
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| 25 Mar 2008 |
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Norah .
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| 11 Sep 2008 |
Steven Boege
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| 16 Sep 2008 |
Andrea Tagliasacchi
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| 20 Mar 2009 |
Cengiz Oztireli
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| 16 Oct 2009 |
Alok
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| 12 Nov 2009 |
Peter Iles
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| 07 Jan 2010 |
Markus Leuthold
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| 24 Apr 2010 |
blue ice
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| 25 May 2010 |
Ram Lakshmi
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| 03 Jun 2010 |
blue ice
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| 12 Jun 2010 |
ghiayas
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| 05 Aug 2010 |
Sebastian
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| 25 Mar 2011 |
Duy Nguyen
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| 16 Aug 2011 |
S R
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| 20 Dec 2011 |
vineet
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| 29 Jan 2012 |
Tarek
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| Updates |
| 14 Jun 2004 |
The new features added June 2004:
+ Use N dimensional data points, where N>3.
+ Use KDTREEIND to return the index value to the closest point, instead of the closest point values.
+ KDRANGEQUERY will also return indices. |
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