论文标题
对齐部分重叠点集：内部近似算法
Aligning Partially Overlapping Point Sets: an Inner Approximation Algorithm
论文作者
论文摘要
在没有关于转换价值的事先信息的部分重叠点集是计算机视觉中的一个挑战性问题，部分重叠点集。为了实现这一目标，我们首先将匹配算法的鲁棒点匹配算法的目标降低到低维变量的函数。但是，所得功能仅在包括可行区域的有限区域上凹入。为了解决此问题，我们采用了仅在目标函数凹入的区域内运行的内部近似优化算法。我们的算法不需要在转换上进行正规化，因此可以处理没有先前有关转换值的信息的情况。我们的方法也是全球最佳的$ε-$，因此可以保证是可靠的。此外，其计算最昂贵的子例程是一个线性分配问题，可以有效地解决。实验结果表明，与最先进的算法相比，所提出的方法的鲁棒性更好。当转换参数的数量很少时，我们的方法也很有效。
Aligning partially overlapping point sets where there is no prior information about the value of the transformation is a challenging problem in computer vision. To achieve this goal, we first reduce the objective of the robust point matching algorithm to a function of a low dimensional variable. The resulting function, however, is only concave over a finite region including the feasible region. To cope with this issue, we employ the inner approximation optimization algorithm which only operates within the region where the objective function is concave. Our algorithm does not need regularization on transformation, and thus can handle the situation where there is no prior information about the values of the transformations. Our method is also $ε-$globally optimal and thus is guaranteed to be robust. Moreover, its most computationally expensive subroutine is a linear assignment problem which can be efficiently solved. Experimental results demonstrate the better robustness of the proposed method over state-of-the-art algorithms. Our method is also efficient when the number of transformation parameters is small.
