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.

通过利用小型业余望远镜捕获的视频数据，该研究提出了一个用于低地球轨道卫星的三维重建框架。该框架包括深度学习的图像恢复、特征点提取和相机姿态初始化的预处理流程，以及改进的三维高斯光滑算法应用于重构三维模型，通过支持训练和姿态估计产生精细的三维点云。通过合成数据集和中国空间站的实际观测验证，该方法在从地面观测中重建三维空间目标方面具有显著优势。