  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.
