The proliferation of non-cooperative resident space objects (RSOs) in orbit has spurred the demand for active space debris removal, on-orbit servicing (OOS), classification, and functionality identification of these RSOs. Recent advances in computer vision have enabled high-definition 3D modeling of objects based on a set of 2D images captured from different viewing angles. This work adapts Instant NeRF and D-NeRF, variations of the neural radiance field (NeRF) algorithm to the problem of mapping RSOs in orbit for the purposes of functionality identification and assisting with OOS. The algorithms are evaluated for 3D reconstruction quality and hardware requirements using datasets of images of a spacecraft mock-up taken under two different lighting and motion conditions at the Orbital Robotic Interaction, On-Orbit Servicing and Navigation (ORION) Laboratory at Florida Institute of Technology. Instant NeRF is shown to learn high-fidelity 3D models with a computational cost that could feasibly be trained on on-board computers.

翻译：在轨非合作空间目标（RSO）数量的激增，催生了对主动空间碎片清除、在轨服务（OOS）、分类及功能辨识的需求。计算机视觉领域的最新进展，使得利用不同视角拍摄的二维图像实现高清晰度三维建模成为可能。本研究将神经辐射场（NeRF）算法的两种变体——Instant NeRF与D-NeRF——应用于在轨空间目标映射问题，旨在支持功能辨识及辅助在轨服务。通过使用佛罗里达理工学院轨道机器人交互、在轨服务与导航（ORION）实验室在两种不同光照与运动条件下拍摄的航天器模型图像数据集，评估了这些算法的三维重建质量及硬件需求。结果表明，Instant NeRF能够以可部署于星载计算机的计算成本，学习获得高保真度的三维模型。