非刚性点集配准的关键是找到点集之间的正确对应关系。传统点集配准方法通常将特征点的全局空间距离作为判别准则，而未考虑点集的邻域结构信息，容易产生误匹配；为此，提出了一种基于邻域结构和驱动力相结合的非刚性点集配准算法。在一致性点漂移（CPD）算法的基础上，提出了一种局部距离计算方法，将其与空间距离相结合，有助于提高匹配精度；此外，对传统形状上下文方法进行了改进，构建了一种新的驱动力准则，以在初始配准过程中提高搜索速度，在后期减小配准误差；最后，采用期望最大化（EM）算法迭代求解各点对的对应关系。在常用国际点集数据集上的仿真实验结果表明，在非刚性变形、噪声、离群值和遮挡等情况下，本文方法比经典方法具有更高的鲁棒性，匹配准确率更高，对真实图像也可以获得比较理想的配准效果。

Finding the correct correspondence is the key point to non-rigid point sets registration. Traditional point set registration methods usually produce mismatches due to only selecting the global spatial distance of features as the criterion but ignoring the neighborhood structure information. To solve this problem, a non-rigid point sets registration algorithm is proposed using neighborhood structure and driving force criterion. A method on the basis of the consensus point drift (CPD) algorithm is proposed to calculate the local mixing distance. Combining it with the original space distance is helpful to improve the matching precision. Besides, a new driving force criterion is constructed based on the improved shape context, which is helpful to improve the searching speed in the original matching process and decrease the matching error in the later process. Finally, we solve the correspondence of each point using the Expectation Maximization (EM) algorithm. Experimental results on commonly-used international point set datasets demonstrate that our method surpass the state-of-the-art ones in terms of robustness and accuracy when deformations, noises, outliers or occlusions exist. Moreover, the proposed algorithm can also achieve ideal registration results on the real images.