从单目影像重建太空非合作目标三维表面，解决真实轨道图像中的光照与背景干扰问题。
From Images2Mesh: A 3D Surface Reconstruction Pipeline for Non-Cooperative Space Objects
- 基于分割去除背景，提升真实轨道图像中相机位姿估计成功率
- 引入逐帧曝光补偿，改善阴影区域重建效果
- 适用于真实空间任务影像，对光照变化敏感但有效
在轨检查影像对非合作空间物体的表征至关重要，可提供几何与结构信息，支撑主动清除空间碎片和在轨服务任务规划。然而，现有神经隐式表面重建方法多限于已知相机位姿和受控光照的合成或硬件在环数据。本文提出一种从单目检查影像重建非合作空间物体三维表面的流水线，应用于公开的国际空间站STS-119任务检查视频及H-IIA火箭上面级在轨影像。结果表明，基于分割的背景剔除对真实在轨影像中的相机位姿估计至关重要，因帧间背景变化导致直接处理完全失败；进一步引入逐帧曝光光度校正，并分析其在不同数据集上的表现，发现阴影区域的重建性能随输入影像光照特性而异。
原文摘要 · Abstract (English)
On-orbit inspection imagery is crucial as it enables characterization of non-cooperative resident space objects, providing the geometry and structural condition essential for active debris removal and on-orbit servicing mission planning. However, most existing neural implicit surface reconstruction methods have been confined to synthetic or hardware-in-the-loop data with known camera poses and controlled illumination. In this work, we present a pipeline for neural implicit surface reconstruction of non-cooperative space objects from monocular inspection imagery. We demonstrate it on publicly released ISS inspection footage from the STS-119 mission and publicly released on-orbit inspection footage of an H-IIA rocket upper stage. We find that segmentation-based background removal is essential for successful camera pose estimation from real on-orbit footage, where background variation between frames caused direct processing to fail entirely. We further incorporate photometric correction of per-frame exposure variations and analyze its behavior across datasets, finding that performance in shadowed regions varies with the illumination characteristics of the input footage.
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