Pursuing 3D Scene Structures with Optical Satellite Images from Affine Reconstruction to Euclidean Reconstruction

TL;DR

This paper introduces a hierarchical framework for 3D scene reconstruction from optical satellite images that requires only 4 GCPs, combining affine dense reconstruction with an affine-to-Euclidean upgrade, outperforming existing methods.

Contribution

It presents a novel hierarchical approach that reduces GCP requirements from 39 to 4, enabling more practical 3D reconstruction from satellite images.

Findings

Outperforms three state-of-the-art methods on public datasets

Requires only 4 GCPs for Euclidean reconstruction

Effective affine dense reconstruction without GCPs

Abstract

How to use multiple optical satellite images to recover the 3D scene structure is a challenging and important problem in the remote sensing field. Most existing methods in literature have been explored based on the classical RPC (rational polynomial camera) model which requires at least 39 GCPs (ground control points), however, it is not trivial to obtain such a large number of GCPs in many real scenes. Addressing this problem, we propose a hierarchical reconstruction framework based on multiple optical satellite images, which needs only 4 GCPs. The proposed framework is composed of an affine dense reconstruction stage and a followed affine-to-Euclidean upgrading stage: At the affine dense reconstruction stage, an affine dense reconstruction approach is explored for pursuing the 3D affine scene structure without any GCP from input satellite images. Then at the affine-to-Euclidean upgrading stage, the obtained 3D affine structure is upgraded to a Euclidean one with 4 GCPs. Experimental results on two public datasets demonstrate that the proposed method significantly outperforms three state-of-the-art methods in most cases.