关键词: 模型配准, 主成分分析法, 迭代最近点算法

Abstract:  This paper proposes an improved ICP (iterative closest point) algorithm based on adaptive optimal thres-hold to align two shape models. These shape models can either be partial or complete. First, the models are pre-aligned using PCA (principal component analysis) roughly, then their size are adjusted using a three-dimensional scaling transformation. Second, the nearest neighbor search is carried out using KD-tree. This improves the speed of searching the corresponding points. Using ICP, the aligning error of the source and target model is calculated with respect to a set of threshold, then the threshold K_best corresponding to the minimum error is recorded. And then, K_best is taken as a new threshold to align the two models using ICP again. Finally, an algorithm for detecting coincidence degree      between 3D shapes is executed, thereafter, the source model is adjusted according to the resulting coincidence degree. The experimental results show that the proposed algorithm can align two models, especially for partial and complete models and clearly improves the accuracy of ICP algorithm.

Key words: model registration, principal component analysis, iterative closest point