Neural Fields in Visual Computing and Beyond

Recent advances in machine learning have led to increased interest solving visual computing problems using methods that employ coordinate-based neural networks. These methods, which we call fields, parameterize physical properties of scenes or objects across space and time. They seen widespread success such as 3D shape image synthesis, animation human bodies, reconstruction, pose estimation. Rapid progress has numerous papers, but a consolidation the discovered knowledge not yet emerged. We provide context, mathematical grounding, review over 250 papers literature on fields. In Part I, focus field techniques by identifying common components including different conditioning, representation, forward map, architecture, manipulation methods. II, applications fields computing, beyond (e.g., robotics, audio). Our shows breadth topics already covered both historically current incarnations, highlights improved quality, flexibility, capability brought Finally, present companion website acts living database can be continually updated community.