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The 3D reconstruction of uncooperative space targets plays many important roles in space operations. Based on the problems of strong interference and large scale, we proposed a sparse 3D reconstruction method for multi-view 2D images. We built a minimum SFM (Structure-from-Motion) system as a comparison benchmark. First, feature extraction and matching are performed on all images, and sequential images are obtained based on consensus features. Then incremental sparse reconstruction is performed, and finally point cloud post-processing filtering is performed. In order to improve the density and accuracy of reconstruction points, LightGlue neural network is used for 2D feature matching. It has been verified that compared with the existing brute force matching, this method is more suitable for uncooperative space targets, and has obvious advantages in reconstruction accuracy and density.

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Sparse Reconstruction of Uncooperative Space Targets Based on LightGlue

  • 会议论文

  • 发表日期：

    2025年

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  • Yibin Zhao,
  • Jianjun Yi,
  • Yihan Pan,
  • Liwei Chen

摘要

The 3D reconstruction of uncooperative space targets plays many important roles in space operations. Based on the problems of strong interference and large scale, we proposed a sparse 3D reconstruction method for multi-view 2D images. We built a minimum SFM (Structure-from-Motion) system as a comparison benchmark. First, feature extraction and matching are performed on all images, and sequential images are obtained based on consensus features. Then incremental sparse reconstruction is performed, and finally point cloud post-processing filtering is performed. In order to improve the density and accuracy of reconstruction points, LightGlue neural network is used for 2D feature matching. It has been verified that compared with the existing brute force matching, this method is more suitable for uncooperative space targets, and has obvious advantages in reconstruction accuracy and density.