Reconstructing Satellites in 3D from Amateur Telescope Images

Zhiming Chang1, Boyang Liu2, Yifei Xia1, Youming Guo3, Boxin Shi1and He Sun1†

1Peking University
2Earthrise Edu&Tech
3Institute of Optics and Electronics of the Chinese Academy of Sciences
†Corresponding author
Paper Code

Overview of the 3D satellite reconstruction method. (a) Image pre-processing: We suppress atmospheric turbulence and noise via classical signal processing and a Video Restoration Transformer (VRT). (b) Pose estimation & reconstruction: A modified SfM with orthographic projection initializes camera poses by sequentially registering adjacent frames, refined via branch-and-bound (BnB) search. We regulate Gaussian growth to capture low- to high-frequency details progressively. (c) Post-processing: KNN filtering removes noisy or isolated points. (d) Training schedule: The full optimization process is illustrated.

Abstract

Monitoring space objects is crucial for space situational awareness, yet reconstructing 3D satellite models from ground-based telescope images is super challenging due to atmospheric turbulence, long observation distances, limited viewpoints, and low signal-to-noise ratios. In this paper, we propose a novel computational imaging framework that overcomes these obstacles by integrating a hybrid image pre-processing pipeline with a joint pose estimation and 3D reconstruction module based on controlled Gaussian Splatting (GS) and Branch-and-Bound (BnB) search. We validate our approach on both synthetic satellite datasets and on-sky observations of China’s Tiangong Space Station and the International Space Station, achieving robust 3D reconstructions of low-Earth orbit satellites from ground-based data. Quantitative evaluations using SSIM, PSNR, LPIPS, and Chamfer Distance demonstrate that our method outperforms state-of-the-art NeRF-based approaches, and ablation studies confirm the critical role of each component. Our framework enables high-fidelity 3D satellite monitoring from Earth, offering a cost-effective alternative for space situational awareness.

Processed Data
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Reconstruction Results
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PointCloud Results
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BibTeX

@article{chang2024reconstructing,
  title={Reconstructing satellites in 3d from amateur telescope images},
  author={Chang, Zhiming and Liu, Boyang and Xia, Yifei and Bai, Weimin and Guo, Youming and Shi, Boxin and Sun, He},
  journal={arXiv preprint arXiv:2404.18394},
  year={2024}
}