Abstract
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.
Abstract (translated)
本文提出了一种利用小业余望远镜捕获的视频对低地球轨道卫星进行三维重建的框架。这些望远镜获得的视频数据与标准的三维重建任务的数据显示有很大的差异，其特点为强烈的运动模糊、大气扰动、广泛的背景光污染和延长焦距，并且存在约束的观测视角。为了应对这些挑战，我们的方法从全面预处理工作开始，包括基于深度学习的图像修复、特征点提取和相机姿态初始化。我们接下来应用改进的3D高斯展平算法来重建3D模型。我们的技术支持同时进行3D高斯训练和姿态估计，从而能够从稀疏、噪声数据中生成精致的3D点云。这一过程进一步得到了一个后编辑阶段的支持，该阶段旨在消除与先前知识不符的噪声点。我们通过使用中国空间站的真实观测数据来验证我们的方法，展示了其从地面观测数据中重构3D空间物体的重要优势。
URL
https://arxiv.org/abs/2404.18394
