Neural Fields in Visual Computing and Beyond

Eurographics / CGF State-of-the-Art Report

1Brown University 2Unity 3University of Toronto 4NVIDIA 5Meta Reality Labs
6Google 7Technical University of Munich 8Massachusetts Institute of Technology *Equal advising


Recent advances in machine learning have created increasing interest in solving visual computing problems using a class of coordinate-based neural networks that parametrize physical properties of scenes or objects across space and time. These methods, which we call neural fields, have seen successful application in the synthesis of 3D shapes and image, animation of human bodies, 3D reconstruction, and pose estimation. However, due to rapid progress in a short time, many papers exist but a comprehensive review and formulation of the problem has not yet emerged. In this report, we address this limitation by providing context, mathematical grounding, and an extensive review of literature on neural fields. This report covers research along two dimensions. In Part I, we focus on techniques in neural fields by identifying common components of neural field methods, including different representations, architectures, forward mapping, and generalization 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, demonstrating the improved quality, flexibility, and capability brought by neural fields methods. Finally, we present a companion website that contributes a living version of this review that can be continually updated by the community.


This work was supported by NSF CNS-2038897 and the Google Research Scholar Program. We thank Sunny Li for their help in designing the website, Jayden Yi for a conceptual readthrough, and Alexander Rush and Hendrik Strobelt for the Mini-Conf project.


    journal = {Computer Graphics Forum},
    title = {Neural Fields in Visual Computing and Beyond},
    author = {Xie, Yiheng and Takikawa, Towaki and Saito, Shunsuke and Litany, Or and Yan, Shiqin and Khan, Numair and Tombari, Federico and Tompkin, James and Sitzmann, Vincent and Sridhar, Srinath},
    year = {2022},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.14505}