点云配准是光学三维(3D)轮廓测量术的关键技术之一。无标志点的点云配准大多由迭代最近点(ICP)算法实现。为提高ICP 算法的性能，提出了一种基于点云单应性的迭代最近点配准算法。描述了该算法中单应性点对的建立方法，并推导了点云之间的坐标变换。用一种手持式三维轮廓扫描仪对一个同时具备高频轮廓和低频轮廓的石膏像进行扫描，共得到92帧点云。利用改进ICP算法，82帧点云被成功配准。同时也利用三种具有代表性的ICP算法对这92 帧点云进行配准实验以作比较。实验表明，该算法具有稳健性强、收敛速度快、收敛精度高的优点，有助于三维模型的快速重建。

Registration of point clouds is one of the key technology of optical three-dimensional (3D) profilometry. Registrations without markers are always realized by using iterative closest point (ICP) algorithm. To improve the performance of ICP algorithm, an improved ICP algorithm based on the biunique correspondence of point clouds is proposed. The establishment of biunique point pairs is introduced, and the transformation of coordinates between point clouds are derived. By using a handheld 3D scanner to scan a statue consisting of high-frequency and low-frequency profiles, then 92 frames of point clouds are obtained. Using the proposed improved ICP algorithm, 82 frames of point clouds are successfully registered. Three representative variants of ICP are applied to register these 92 frames for comparison. Experimental results demonstrate that the proposed algorithm has advantages of strong robustness, high convergent speed and high convergent accuracy, which is useful for fast reconstruction of 3D models.