From Orbit to Ground: Generative City Photogrammetry from Extreme Off-Nadir Satellite Images

TL;DR

This paper introduces a novel method for city-scale 3D reconstruction from satellite images, using a 2.5D height map and generative texture restoration to synthesize realistic ground views from extreme off-nadir satellite imagery.

Contribution

It proposes a specialized city geometry model and a texture enhancement pipeline tailored for satellite inputs, enabling robust, high-fidelity urban reconstructions from sparse, degraded images.

Findings

Reconstructed a 4 km² urban area from few satellite images.

Achieved state-of-the-art photorealistic ground view synthesis.

Produced watertight meshes with crisp roofs and extruded facades.

Abstract

City-scale 3D reconstruction from satellite imagery presents the challenge of extreme viewpoint extrapolation, where our goal is to synthesize ground-level novel views from sparse orbital images with minimal parallax. This requires inferring nearly $90^\circ$ viewpoint gaps from image sources with severely foreshortened facades and flawed textures, causing state-of-the-art reconstruction engines such as NeRF and 3DGS to fail. To address this problem, we propose two design choices tailored for city structures and satellite inputs. First, we model city geometry as a 2.5D height map, implemented as a Z-monotonic signed distance field (SDF) that matches urban building layouts from top-down viewpoints. This stabilizes geometry optimization under sparse, off-nadir satellite views and yields a watertight mesh with crisp roofs and clean, vertically extruded facades. Second, we paint the mesh appearance from satellite images via differentiable rendering techniques. While the satellite inputs may contain long-range, blurry captures, we further train a generative texture restoration network to enhance the appearance, recovering high-frequency, plausible texture details from degraded inputs. Our method's scalability and robustness are demonstrated through extensive experiments on large-scale urban reconstruction. For example, in our teaser figure, we reconstruct a $4\,\mathrm{km}^2$ real-world region from only a few satellite images, achieving state-of-the-art performance in synthesizing photorealistic ground views. The resulting models are not only visually compelling but also serve as high-fidelity, application-ready assets for downstream tasks like urban planning and simulation. Project page can be found at https://pku-vcl-geometry.github.io/Orbit2Ground/.