飞行时间三维相机的多视角散乱点云优化配准方法研究

针对目前基于飞行时间(TOF)原理的三维相机实现物体完整表面的三维点云重建过程中，多视角散乱点云配准精度低的问题，本文提出了一种多视角散乱点云优化配准方法。该方法通过构建一个目标功能函数，并结合相邻点云的变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坐标系的绝对变换矩阵，避免了对相邻点云的变换矩阵进行累积而引起误差的累加。实验结果表明，该方法提高了多视角点云配准的精度，同时增强了物体点云模型重建的效果，在三维曲面重建中具有较强的实用性。

Research on Multi-view scattered point cloud optimization registration using TOF camera

For the problem of low accuracy of multi-view scattered point cloud registration in objects surface 3D point cloud reconstruction process using TOF camera, this paper presents a 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 error caused by registration of continuous point cloud. we use different objects to do the experiment, and the results show that the method ensures the speed of point cloud registration, and at the same time improves the accuracy of the multi-view point cloud registration, as well as enhances the effect of the objects point cloud model reconstruction. Also, it is conducive to realize 3D surface mesh reconstruction.