从图像到网格：一种用于非合作空间物体的3D表面重建流水线
From Images2Mesh: A 3D Surface Reconstruction Pipeline for Non-Cooperative Space Objects
AI总结：
本文提出了一种基于单目图像的神经隐式表面重建流水线，用于非合作空间物体的在轨检查影像处理，解决了真实场景中背景分割和光照校正的问题。
摘要：
在轨检查影像对于表征非合作在轨空间物体至关重要，因为它提供了用于主动碎片清除和在轨维护任务规划的几何和结构信息。然而，大多数现有神经隐式表面重建方法局限于合成或硬件在环数据，这些数据具有已知的相机姿态和受控照明。在本文中，我们提出了一种用于从单目检查影像重建非合作空间物体神经隐式表面的流水线。我们在公开发布的STS-119任务ISS检查视频和公开发布的H-IIA运载火箭上面级在轨检查视频上进行了演示。我们发现基于分割的背景去除对于从真实在轨影像中成功估计相机姿态至关重要，其中帧间背景变化导致直接处理完全失败。我们进一步结合了每帧曝光变化的光度校正，并分析其在不同数据集上的行为，发现阴影区域的性能会随着输入影像的照明特性而变化。
英文摘要：
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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