Reconstructing Satellites in 3D from Amateur Telescope Images
This work addresses space situational awareness by providing a cost-effective method for monitoring satellites from Earth, though it appears incremental as it builds on existing computational imaging and 3D reconstruction techniques.
The paper tackles the problem of reconstructing 3D satellite models from ground-based telescope images, which is challenging due to atmospheric turbulence and limited viewpoints, and achieves robust reconstructions of low-Earth orbit satellites, outperforming state-of-the-art NeRF-based approaches in metrics like SSIM and Chamfer Distance.
Monitoring space objects is crucial for space situational awareness, yet reconstructing 3D satellite models from ground-based telescope images is 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. Project page: https://ai4scientificimaging.org/ReconstructingSatellites