Cycle-consistent Generative Adversarial Networks for Neural Style Transfer using data from Chang'E-4
This work addresses the problem of applying Generative Adversarial Networks to space science and planetary exploration data, specifically for the Iris Lunar Rover mission.
This paper introduces a framework for Neural Style Transfer using Cycle-consistency, applied to planetary data from the Chang'E-4 mission. The experiments are conducted in the context of the Iris Lunar Rover, a nano-rover planned for lunar deployment in 2021.
Generative Adversarial Networks (GANs) have had tremendous applications in Computer Vision. Yet, in the context of space science and planetary exploration the door is open for major advances. We introduce tools to handle planetary data from the mission Chang'E-4 and present a framework for Neural Style Transfer using Cycle-consistency from rendered images. The experiments are conducted in the context of the Iris Lunar Rover, a nano-rover that will be deployed in lunar terrain in 2021 as the flagship of Carnegie Mellon, being the first unmanned rover of America to be on the Moon.