Globally convergent range image registration by graph kernel algorithm
Globally convergent range image registration by graph kernel algorithm
复制标题
DOI：
10.1109/3dim.2005.51
复制
发表时间：
2005-06
期刊：
影响因子：
--
通讯作者：
R. Sára;I. Okatani;A. Sugimoto
中科院分区：
文献类型：
--
作者：
R. Sára;I. Okatani;A. Sugimoto
在不了解视点的情况下自动进行距离图像配准需要识别不同距离图像的公共区域，然后在这些区域中建立点对应关系。我们将其表述为基于图的优化问题。更具体地说，我们定义一个图，其中每个顶点表示两个点的假定匹配，每个边表示两个匹配之间的二元一致性决策，每个边方向表示从更差到更好的假定匹配的匹配质量。然后将图中定义的严格子内核最大化。最大严格子核算法使我们能够唯一确定点的最大一致匹配。为了评估单个匹配的质量，我们使用由点邻域中的所有表面法线生成的三重乘积的直方图。我们的实验结果表明了我们的方法对于粗略范围图像配准的有效性。
Automatic range image registration without any knowledge of the viewpoint requires identification of common regions across different range images and then establishing point correspondences in these regions. We formulate this as a graph-based optimization problem. More specifically, we define a graph in which each vertex represents a putative match of two points, each edge represents binary consistency decision between two matches, and each edge orientation represents match quality from worse to better putative match. Then strict sub-kernel defined in the graph is maximized. The maximum strict sub-kernel algorithm enables us to uniquely determine the largest consistent matching of points. To evaluate the quality of a single match, we employ the histogram of triple products that are generated by all surface normals in a point neighborhood. Our experimental results show the effectiveness of our method for rough range image registration.