# Leveraging Vision Reconstruction Pipelines for Satellite Imagery

arXiv: https://arxiv.org/abs/1910.02989 · HTML: https://mlsanity.com/paper/1910.02989/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2019.

## Built on by (most cited first, 23 of 23)

- [Sat-NeRF: Learning Multi-View Satellite Photogrammetry With Transient Objects and Shadow Modeling Using RPC Cameras](https://mlsanity.com/paper/2203.08896/) (2022)
- [Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery](https://mlsanity.com/paper/2510.15869/) (2025)
- [Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo Matching](https://mlsanity.com/paper/2109.11121/) (2021)
- [SkySplat: Generalizable 3D Gaussian Splatting from Multi-Temporal Sparse Satellite Images](https://mlsanity.com/paper/2508.09479/) (2025)
- [Single View Geocentric Pose in the Wild](https://mlsanity.com/paper/2105.08229/) (2021)
- [Sat-DN: Implicit Surface Reconstruction from Multi-View Satellite Images with Depth and Normal Supervision](https://mlsanity.com/paper/2502.08352/) (2025)
- [From Orbit to Ground: Generative City Photogrammetry from Extreme Off-Nadir Satellite Images](https://mlsanity.com/paper/2512.07527/) (2025)
- [3D Surface Reconstruction From Multi-Date Satellite Images](https://mlsanity.com/paper/2102.02502/) (2021)
- [Cumulative Assessment for Urban 3D Modeling](https://mlsanity.com/paper/2107.04622/) (2021)
- [Pursuing 3D Scene Structures with Optical Satellite Images from Affine Reconstruction to Euclidean Reconstruction](https://mlsanity.com/paper/2201.06037/) (2022)
- [ShadowGS: Shadow-Aware 3D Gaussian Splatting for Satellite Imagery](https://mlsanity.com/paper/2601.00939/) (2026)
- [Fast Satellite Tensorial Radiance Field for Multi-date Satellite Imagery of Large Size](https://mlsanity.com/paper/2309.11767/) (2023)
- [rpcPRF: Generalizable MPI Neural Radiance Field for Satellite Camera](https://mlsanity.com/paper/2310.07179/) (2023)
- [Learning autonomous driving from aerial imagery](https://mlsanity.com/paper/2410.14177/) (2024)
- [Semantic Neural Radiance Fields for Multi-Date Satellite Data](https://mlsanity.com/paper/2502.16992/) (2025)
- [RPC-GS: Gaussian Splatting with native RPC Rendering for Satellite Imagery](https://mlsanity.com/paper/2606.06690/) (2026)
- [EO-VGGT: Orbital Ray-Conditioned 3D Foundation Models for Satellite Multi-View Reconstruction](https://mlsanity.com/paper/2607.00417/) (2026)
- [Refined Equivalent Pinhole Model for Large-scale 3D Reconstruction from Spaceborne CCD Imagery](https://mlsanity.com/paper/2310.20117/) (2023)
- [Success Probability in Multi-View Imaging](https://mlsanity.com/paper/2407.21027/) (2024)
- [Methods for evaluating the resolution of 3D data derived from satellite images](https://mlsanity.com/paper/2506.11876/) (2025)
- [SatGeo-NeRF: Geometrically Regularized NeRF for Satellite Imagery](https://mlsanity.com/paper/2603.21931/) (2026)
- [Robust RPC Bundle Adjustment for Multi-Date Satellite Imagery with Season-Invariant Correspondences](https://mlsanity.com/paper/2607.26973/) (2026)
- [GeoRay: Gauge-Aware Feed-Forward Satellite 3D Reconstruction in the Geodetic Frame](https://mlsanity.com/paper/2608.29680/) (2026)

## Abstract (arXiv)

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.