NEURAL RADIANCE FIELDS IN SPACE APPLICATIONS: A COMPREHENSIVE REVIEW

Neural Radiance Fields (NeRF) have emerged as a powerful deep learning technique, revolutionizing the representationandrendering of 3Dscenes. Althoughoriginally developed for computer vision and graphics applications, the potential of NeRF is increasingly being recognized in space- related fields. This paper provides a comprehensive review of the applications, advancements and challenges associated with the use of NeRF in space exploration, satellite imaging and remote sensing. We begin by introducing the foundational concepts of NeRF, including its architecture, underlying principles and computational requirements. We then explore how NeRF has been adapted and applied to space-specific challenges such as high-resolution 3D reconstruction of planetary surfaces, the visualization of satellite data and the enhancement of space mission planning. Furthermore, we discuss the integration of NeRF with other cutting-edge technologies like machine learning, autonomous systems and real-time rendering, highlighting the potential for future breakthroughs in space missions. Finally, we outline thecurrent limitations and open research questions, offering insights into the future directions of NeRF in space applications. This review aims to serve as a valuable resource for researchers and practitioners exploring the intersection of machine learning, computer graphics and space science.