From Images2Mesh: A 3D Surface Reconstruction Pipeline for Non-Cooperative Space Objects
From Images2Mesh: A 3D Surface Reconstruction Pipeline for Non-Cooperative Space Objects
From Images2Mesh：一种针对非合作空间目标的 3D 表面重建流程
Abstract: 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.
摘要： 在轨巡检影像至关重要，因为它能够实现对非合作在轨空间目标的表征，为主动碎片清除和在轨服务任务规划提供必要的几何结构和状态信息。
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.
然而，目前大多数现有的神经隐式表面重建方法仅局限于已知相机位姿和受控光照条件下的合成数据或硬件在环数据。
In this work, we present a pipeline for neural implicit surface reconstruction of non-cooperative space objects from monocular inspection imagery.
在这项工作中，我们提出了一种从单目巡检影像中对非合作空间目标进行神经隐式表面重建的流程。
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.
我们利用 STS-119 任务中公开的国际空间站（ISS）巡检影像以及 H-IIA 火箭上面级公开的在轨巡检影像对该流程进行了验证。
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.
我们发现，基于分割的背景去除对于从真实的在轨影像中成功估计相机位姿至关重要，因为在这些影像中，帧间的背景变化会导致直接处理完全失败。
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.
我们进一步结合了针对单帧曝光变化的测光校正，并分析了其在不同数据集上的表现，发现阴影区域的重建性能会随着输入影像的光照特性而变化。
Paper Details:
- arXiv: 2605.00147 [cs.CV]
- Authors: Bala Prenith Reddy Gopu, Patrick Quinn, George M. Nehma, Madhur Tiwari, Matt Ueckermann, David Hinckley, Christopher McKenna
- Submitted: 30 Apr 2026
论文详情：
- arXiv: 2605.00147 [cs.CV]
- 作者： Bala Prenith Reddy Gopu, Patrick Quinn, George M. Nehma, Madhur Tiwari, Matt Ueckermann, David Hinckley, Christopher McKenna
- 提交日期： 2026年4月30日