Reconstructing Satellites in 3D from Amateur Telescope Images

• URL: http://arxiv.org/abs/2404.18394v2
• Date: Mon, 25 Nov 2024 08:47:46 GMT
• Title: Reconstructing Satellites in 3D from Amateur Telescope Images
• Authors: Zhiming Chang, Boyang Liu, Yifei Xia, Weimin Bai, Youming Guo, Boxin Shi, He Sun,
• Abstract summary: 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. We apply a customized Structure from Motion (SfM) approach, followed by an improved 3D Gaussian splatting algorithm, to achieve high-fidelity 3D model reconstruction.
• Score: 42.850623200702394
• License:
• 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 apply a customized Structure from Motion (SfM) approach, followed by an improved 3D Gaussian splatting algorithm, to achieve high-fidelity 3D model reconstruction. 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 on synthetic datasets and actual observations of China's Space Station and International Space Station, showcasing its significant advantages over existing methods in reconstructing 3D space objects from ground-based observations.

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