提出了一种具有自终止特性的对未知物体模型自动3维测量的视点规划方法。首先在寻找最优视点阶段，利用群矢量链方法获得视觉传感器在下一个视点的观测方向，而视觉传感器在空间的确切位置参数是通过计算矢量场内的边界积分获得，其中获得最大边界积分的空间位置被定为下一个最优视点。同时为了保证对未知物体模型自动3维测量过程具有自终止特性，根据高斯定理，提出由测量所得的点云直接计算出物体体积的方法，并将连续两个视点下物体体积的变化量作为终止测量的依据来判断规划过程是否需要终止或继续。实验结果表明，该具有自终止特性的视点规划方法是可行而且有效的。

In this paper we present a method of view planning for automatically constructing the model of an unknown object from range images.The method computes the next best view in two steps.First,the exploration direction for the next view is determined via a Mass Vector Chain(MVC) based scheme.Then the accurate position of the next view is obtained by computing the boundary integral of the vectors fields.The position with the maximum integral value is selected as Next Best View(NBV).We also present a selftermination criterion for judging the completion condition in the measurement and reconstruction process.The termination condition is derived based on changes in the volume computed from two successive viewpoints.The experimental results show that the method is effective in practical implementation.