针对三维重建中的点云配准问题，提出一种基于点云特征的自动配准算法。利用微软Kinect传感器采集物

体的多视角深度图像，提取目标区域并转化为三维点云。对点云进行滤波并估计快速点特征直方图特征，结合双向

快速近似最近邻搜索算法得到初始对应点集，并使用随机采样一致性算法确定最终对应点集。根据奇异值分解法

求出点云的变换矩阵初始值，在初始配准的基础上运用迭代最近点算法做精细配准。实验结果表明，该配准方法既

保证了三维点云的配准质量，又降低了计算复杂度，具有较高的可操作性和鲁棒性。

-Aiming at the problem of point cloud registration in 3D reconstruction, this paper presents an automatic regis-

tration method based on the feature of point cloud. Firstly, it utilizes Microsoft Kinect sensor to capture depth images in

several different views and the interest regions are extracted and converted to 3D point cloud. Secondly, point clouds are

filtered and the fast point feature histograms are estimated, then the bidirectional fast approximate nearest neighbor algorithm

and random sample consensus are employed to search the final corresponding points. Finally, after computing the initial

transformation matric applying singular value decomposition, the iterative closest point algorithm is used to get refined

result on the base of initial registration. Experiments show that this registration method can not only ensure the quality of

point cloud registration, but reduce the computation complexity, and achieve higher maneuverability and better robustness.