By Topic

Effective nearest neighbor search for aligning and merging range images

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
R. Sagawa ; Inst. of Sci. & Ind. Res., Osaka Univ., Japan ; T. Masuda ; K. Ikeuchi

We describe a novel method, which extends the search algorithm of a k-d tree for aligning and merging range images. If the nearest neighbor point is far from a query, many of the leaf nodes must be examined during the search, which actually will not finish in logarithmic time. However, such a distant point is not as important as the nearest neighbor in many applications, such as aligning and merging range images; the reason for this is either because it is not consequently used or because its weight becomes very small. Thus, we propose a new algorithm that does not search strictly by pruning branches if the nearest neighbor point lies beyond a certain threshold. We call the technique the bounds-overlap-threshold (BOT) test. The BOT test can be applied without recreating the k-d tree if the threshold value changes. Then, we describe how we applied our new method to three applications in order to analyze its performance. Finally, we discuss the method's effectiveness.

Published in:

3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings. Fourth International Conference on

Date of Conference:

6-10 Oct. 2003