  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.
