Effective Nearest Neighbor Search for Aligning and Merging Range Images

This paper describes 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, in this paper, 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 re-creating the kd 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.