针对目前基于飞行时间(TOF)原理的3维相机实现物体完整表面的3维点云重建过程中,多视角散乱点云配准精度低的问题,提出一种优化配准方法。该方法通过构建一个目标功能函数,并结合相邻点云的转换矩阵对该目标函数进行最小化求解,直接获取任意位置的点云到基准点云所处坐标系的绝对转换矩阵,避免了对连续点云之间的配准而引起误差的累加。对不同的物体进行实验,实验结果表明,该方法在保证点云配准速度的同时,提高了多视角点云配准的精度,物体点云模型重建效果较好,有利于实现后期3维曲面网格的重建。

To solve the problem of low accuracy of multi-view scattered point cloud registration in objects surface 3D point cloud reconstruction process using TOF (time-of-flight) cameras, this paper presents an optimization registration method. The method obtains the absolute transformation matrix of any point-cloud to the reference coordinate system by building a target functions and combining adjacent point-cloud transformation matrix to minimize the objective function, avoid the accumulation of errors caused by registration of continuous point clouds. We use different objects for our experiments, and the results show that the method ensures the speed of point cloud registration. At the same time, it improves the accuracy of the multi-view point cloud registration, as well as enhances the effect of the objects point cloud model reconstruction. Furthemore, it is conducive to realize 3D surface mesh reconstruction.