  This paper proposes a framework for the 3D reconstruction of satellites in
low-Earth orbit, utilizing videos captured by small amateur telescopes. The
video data obtained from these telescopes differ significantly from data for
standard 3D reconstruction tasks, characterized by intense motion blur,
atmospheric turbulence, pervasive background light pollution, extended focal
length and constrained observational perspectives. To address these challenges,
our approach begins with a comprehensive pre-processing workflow that
encompasses deep learning-based image restoration, feature point extraction and
camera pose initialization. We proceed with the application of an improved 3D
Gaussian splatting algorithm for reconstructing the 3D model. Our technique
supports simultaneous 3D Gaussian training and pose estimation, enabling the
robust generation of intricate 3D point clouds from sparse, noisy data. The
procedure is further bolstered by a post-editing phase designed to eliminate
noise points inconsistent with our prior knowledge of a satellite's geometric
constraints. We validate our approach using both synthetic datasets and actual
observations of China's Space Station, showcasing its significant advantages
over existing methods in reconstructing 3D space objects from ground-based
observations.
