Cached k-d tree search for ICP algorithms

A. Nüchter, K. Lingemann, J. Hertzberg
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引用次数: 180

Abstract

The ICP (iterative closest point) algorithm is the de facto standard for geometric alignment of three-dimensional models when an initial relative pose estimate is available. The basis of ICP is the search for closest points. Since the development of ICP, k-d trees have been used to accelerate the search. This paper presents a novel search procedure, namely cached k-d trees, exploiting iterative behavior of the ICP algorithm. It results in a significant speedup of about 50% as we show in an evaluation using different data sets.
缓存的k-d树搜索ICP算法
当初始相对姿态估计可用时,ICP(迭代最近点)算法是三维模型几何对齐的事实上的标准。ICP的基础是寻找最近点。自ICP发展以来,k-d树已被用于加速搜索。本文提出了一种新的搜索过程,即缓存k-d树,利用ICP算法的迭代行为。正如我们在使用不同数据集的评估中所显示的那样,它导致了大约50%的显著加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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