# From Images2Mesh: A 3D Surface Reconstruction Pipeline for Non-Cooperative Space Objects

arXiv: https://arxiv.org/abs/2605.00147 · HTML: https://mlsanity.com/paper/2605.00147/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 11 of 11)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [Neuralangelo: High-Fidelity Neural Surface Reconstruction](https://mlsanity.com/paper/2306.03092/) (2023)
- [Vision-based Neural Scene Representations for Spacecraft](https://mlsanity.com/paper/2105.06405/) (2021)
- [3D Reconstruction of Non-cooperative Resident Space Objects using Instant NGP-accelerated NeRF and D-NeRF](https://mlsanity.com/paper/2301.09060/) (2023)
- [Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting](https://mlsanity.com/paper/2401.02588/) (2024)
- [SpaceYOLO: A Human-Inspired Model for Real-time, On-board Spacecraft Feature Detection](https://mlsanity.com/paper/2302.00824/) (2023)
- [Fast Learning of Non-Cooperative Spacecraft 3D Models through Primitive Initialization](https://mlsanity.com/paper/2507.19459/) (2025)
- [Improved 3D Gaussian Splatting of Unknown Spacecraft Structure Using Space Environment Illumination Knowledge](https://mlsanity.com/paper/2512.23998/) (2025)
- [Autonomous Satellite Detection and Tracking using Optical Flow](https://mlsanity.com/paper/2204.07025/) (2022)
- [Dynamic Scene 3D Reconstruction of an Uncooperative Resident Space Object](https://mlsanity.com/paper/2509.07932/) (2025)

## Abstract (arXiv)

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.