Leveraging Vision Reconstruction Pipelines for Satellite Imagery

TL;DR

This paper investigates the applicability of state-of-the-art computer vision 3D reconstruction pipelines to satellite imagery, addressing adaptation challenges and demonstrating competitive speed and accuracy in this domain.

Contribution

It explores adapting vision-based structure from motion and multi-view stereo methods for satellite imagery reconstruction, highlighting their effectiveness.

Findings

Vision pipelines achieve competitive accuracy in satellite 3D reconstruction.

Speed of vision pipelines is comparable to specialized remote sensing methods.

Addressed key adaptation challenges for applying vision methods to satellite data.

Abstract

Reconstructing 3D geometry from satellite imagery is an important topic of research. However, disparities exist between how this 3D reconstruction problem is handled in the remote sensing context and how multi-view reconstruction pipelines have been developed in the computer vision community. In this paper, we explore whether state-of-the-art reconstruction pipelines from the vision community can be applied to the satellite imagery. Along the way, we address several challenges adapting vision-based structure from motion and multi-view stereo methods. We show that vision pipelines can offer competitive speed and accuracy in the satellite context.