# Dynamic Scene 3D Reconstruction of an Uncooperative Resident Space Object

arXiv: https://arxiv.org/abs/2509.07932 · HTML: https://mlsanity.com/paper/2509.07932/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020)
- [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [HexPlane: A Fast Representation for Dynamic Scenes](https://mlsanity.com/paper/2301.09632/) (2023)
- [Neuralangelo: High-Fidelity Neural Surface Reconstruction](https://mlsanity.com/paper/2306.03092/) (2023)
- [Fast Dynamic Radiance Fields with Time-Aware Neural Voxels](https://mlsanity.com/paper/2205.15285/) (2022)
- [Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images](https://mlsanity.com/paper/2008.07760/) (2020)
- [FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction](https://mlsanity.com/paper/2304.01480/) (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)

## Built on by (most cited first, 1 of 1)

- [From Images2Mesh: A 3D Surface Reconstruction Pipeline for Non-Cooperative Space Objects](https://mlsanity.com/paper/2605.00147/) (2026)

## Abstract (arXiv)

Characterization of uncooperative Resident Space Objects (RSO) play a crucial role in On-Orbit Servicing (OOS) and Active Debris Removal (ADR) missions to assess the geometry and motion properties. To address the challenges of reconstructing tumbling uncooperative targets, this study evaluates the performance of existing state-of-the-art 3D reconstruction algorithms for dynamic scenes, focusing on their ability to generate geometrically accurate models with high-fidelity. To support our evaluation, we developed a simulation environment using Isaac Sim to generate physics-accurate 2D image sequences of tumbling satellite under realistic orbital lighting conditions. Our preliminary results on static scenes using Neuralangelo demonstrate promising reconstruction quality. The generated 3D meshes closely match the original CAD models with minimal errors and artifacts when compared using Cloud Compare (CC). The reconstructed models were able to capture critical fine details for mission planning. This provides a baseline for our ongoing evaluation of dynamic scene reconstruction.