Large-scale DSM registration via motion averaging

TL;DR

This paper introduces a novel, efficient motion averaging approach for large-scale DSM registration that improves both computational speed and accuracy, especially suitable for high-resolution satellite data.

Contribution

The paper presents a new motion averaging framework for DSM registration that handles large datasets efficiently and overcomes limitations of sequential methods.

Findings

Significant reduction in computation time.

Improved registration accuracy.

Effective handling of large, high-resolution DSM datasets.

Abstract

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.