Advances in machine learning have increased interest in solving visual computing problems using a class of coordinate-based neural networks that parameterize physical properties of scenes or objects across space and time. These methods, which we call neural fields, have seen widespread success in applications such as 3D shape and image synthesis, animation of human bodies, 3D reconstruction, and pose estimation. Rapid progress has led to numerous papers, but a formulation and review of the problems in visual computing has not yet emerged. We provide context, mathematical grounding, and a review of 251 papers in the literature on neural fields. In Part I, we focus on neural fields techniques by identifying common components of neural field methods, including different generalization, representation, forward map, architecture, and manipulation methods. In Part II, we focus on applications of neural fields to different problems in visual computing, and beyond (e.g., robotics, audio). Our review shows the breadth of topics already covered in visual computing, both historically and in current incarnations, and highlights the improved quality, flexibility, and capability brought by neural field methods. Finally, we present a companion website that contributes a living database that can be continually updated by the community.
CCS Concepts