Neural Radiance Fields (NeRFs) have remodeled 3D scene representation since release. NeRFs can effectively reconstruct complex 3D scenes from 2D images, advancing different fields and applications such as scene understanding, 3D content generation, and robotics. Despite significant research progress, a thorough review of recent innovations, applications, and challenges is lacking. This survey compiles key theoretical advancements and alternative representations and investigates emerging challenges. It further explores applications on reconstruction, highlights NeRFs' impact on computer vision and robotics, and reviews essential datasets and toolkits. By identifying gaps in the literature, this survey discusses open challenges and offers directions for future research.
@article{arxiv.2501.13104,
title = {Neural Radiance Fields for the Real World: A Survey},
author = {Wenhui Xiao and Remi Chierchia and Rodrigo Santa Cruz and Xuesong Li and David Ahmedt-Aristizabal and Olivier Salvado and Clinton Fookes and Leo Lebrat},
journal= {arXiv preprint arXiv:2501.13104},
year = {2025}
}