Joint attitude estimation and 3D neural reconstruction of non-cooperative space objects

TL;DR

This paper presents a method using Neural Radiance Fields to reconstruct 3D models of non-cooperative space objects from challenging simulated images, jointly estimating camera poses and object structure.

Contribution

It introduces a joint optimization approach for camera pose estimation and 3D reconstruction of space objects using NeRF under difficult environmental conditions.

Findings

Successive image training yields the most accurate reconstructions.

Joint pose and structure optimization improves model accuracy.

Regularization prevents large pose jumps during training.

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.