Neural Radiance Fields: Past, Present, and Future
It provides a comprehensive overview for researchers and newcomers, categorizing NeRF-related work by datasets, objectives, applications, and evaluations, but is incremental as a survey rather than presenting new research.
This survey paper reviews the development and impact of Neural Radiance Fields (NeRFs), a method introduced by Mildenhall et al. that has led to over 1000 preprints and advanced applications in 3D modeling for computer graphics, robotics, and augmented/virtual reality.
The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 1000 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.