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.
@article{arxiv.2605.00147,
title = {From Images2Mesh: A 3D Surface Reconstruction Pipeline for Non-Cooperative Space Objects},
author = {Bala Prenith Reddy Gopu and Patrick Quinn and George M. Nehma and Madhur Tiwari and Matt Ueckermann and David Hinckley and Christopher McKenna},
journal= {arXiv preprint arXiv:2605.00147},
year = {2026}
}