Generating wide-area digital surface models (DSMs) requires registering a
large number of individual, and partially overlapped DSMs. This presents a
challenging problem for a typical registration algorithm, since when a large
number of observations from these multiple DSMs are considered, it may easily
cause memory overflow. Sequential registration algorithms, although can
significantly reduce the computation, are especially vulnerable for small
overlapped pairs, leading to a large error accumulation. In this work, we
propose a novel solution that builds the DSM registration task as a motion
averaging problem: pair-wise DSMs are registered to build a scene graph, with
edges representing relative poses between DSMs. Specifically, based on the grid
structure of the large DSM, the pair-wise registration is performed using a
novel nearest neighbor search method. We show that the scene graph can be
optimized via an extremely fast motion average algorithm with O(N) complexity
(N refers to the number of images). Evaluation of high-resolution
satellite-derived DSM demonstrates significant improvement in computation and
accuracy.

通过将数字表面模型注册任务建立为一个运动平均问题，我们提出了一种新的解决方案：成对的数字表面模型通过建立场景图来注册，其中边表示数字表面模型之间的相对姿势。根据大型数字表面模型的网格结构，采用一种新的最近邻搜索方法进行成对的注册。通过极快的运动平均算法对场景图进行优化，实验证明在计算和准确性方面有显著的改进。