Neural Radiance Fields for the Real World: A Survey
It provides a comprehensive review for researchers in computer vision and robotics, but is incremental as it synthesizes existing work rather than presenting new findings.
This survey paper compiles recent innovations, applications, and challenges in Neural Radiance Fields (NeRFs), which reconstruct 3D scenes from 2D images, and identifies gaps in the literature to offer future research directions.
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.