Joint attitude estimation and 3D neural reconstruction of non-cooperative space objects
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
Obtaining a better knowledge of the current state and behavior of objects
orbiting Earth has proven to be essential for a range of applications such as
active debris removal, in-orbit maintenance, or anomaly detection. 3D models
represent a valuable source of information in the field of Space Situational
Awareness (SSA). In this work, we leveraged Neural Radiance Fields (NeRF) to
perform 3D reconstruction of non-cooperative space objects from simulated
images. This scenario is challenging for NeRF models due to unusual camera
characteristics and environmental conditions : mono-chromatic images, unknown
object orientation, limited viewing angles, absence of diffuse lighting etc. In
this work we focus primarly on the joint optimization of camera poses alongside
the NeRF. Our experimental results show that the most accurate 3D
reconstruction is achieved when training with successive images one-by-one. We
estimate camera poses by optimizing an uniform rotation and use regularization
to prevent successive poses from being too far apart.
