2026 Jonathan T. Barron: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	66	2255	2188	1125	1084	121	32324

Overview

Jonathan T. Barron is affiliated with Google in the United States. Their research primarily spans computer science and engineering with a focus on subfields such as computer vision and pattern recognition, computer graphics and computer-aided design, computational mechanics, aerospace engineering, and media technology.

Their work covers several main topics including advanced vision and imaging, computer graphics and visualization techniques, 3D shape modeling and analysis, image enhancement techniques, advanced image processing techniques, generative adversarial networks and image synthesis, and advanced neural network applications.

Jonathan T. Barron has contributed to numerous publications, notably in venues such as arXiv (Cornell University), the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), ACM Transactions on Graphics, and Computer Graphics Forum.

Among recent papers authored or coauthored by Barron are:

"Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields" (2022) published in the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
"NeRF" (2021) published in Communications of the ACM
"Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains" (2020) published in arXiv (Cornell University)
"Block-NeRF: Scalable Large Scene Neural View Synthesis" (2022) published in the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
"NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis" (2020) published in arXiv (Cornell University)

Barron frequently collaborates with other researchers. Prominent coauthors include:

Ben Mildenhall
Pratul P. Srinivasan
Peter Hedman
Dor Verbin
Matthew Tancik

Their publication record shows consistent contributions to the fields of computer vision and graphics, with a notable emphasis on neural radiance fields and related imaging techniques. This research has been disseminated across a variety of top-tier conferences and journals.

Best Publications

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

Ben Mildenhall;Pratul P. Srinivasan;Matthew Tancik;Jonathan T. Barron

4849
Citations
Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields

Jonathan T. Barron;Ben Mildenhall;Dor Verbin;Pratul P. Srinivasan

1424
Citations
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

Matthew Tancik;Pratul P. Srinivasan;Ben Mildenhall;Sara Fridovich-Keil

1352
Citations
DreamFusion: Text-to-3D using 2D Diffusion

Unknown

1224
Citations
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields

Jonathan T. Barron;Ben Mildenhall;Matthew Tancik;Peter Hedman

1136
Citations
NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections

Ricardo Martin-Brualla;Noha Radwan;Mehdi S. M. Sajjadi;Jonathan T. Barron

1073
Citations
Block-NeRF: Scalable Large Scene Neural View Synthesis

Unknown

1018
Citations
Shape, Illumination, and Reflectance from Shading

Jonathan T. Barron;Jitendra Malik

806
Citations
Deep bilateral learning for real-time image enhancement

Michaël Gharbi;Jiawen Chen;Jonathan T. Barron;Samuel W. Hasinoff

785
Citations
Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields

Unknown

740
Citations
HyperNeRF

Unknown

699
Citations
Zero-Shot Text-Guided Object Generation with Dream Fields

Unknown

666
Citations
IBRNet: Learning Multi-View Image-Based Rendering

Qianqian Wang;Zhicheng Wang;Kyle Genova;Pratul Srinivasan

658
Citations
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation

Jordi Pont-Tuset;Pablo Arbelaez;Jonathan T.Barron;Ferran Marques

657
Citations
HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video

Unknown

612
Citations
A Category-Level 3D Object Dataset: Putting the Kinect to Work.

Allison Janoch;Sergey Karayev;Yangqing Jia;Jonathan T. Barron

533
Citations
A General and Adaptive Robust Loss Function

Jonathan T. Barron

520
Citations
NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images

Unknown

488
Citations
Burst photography for high dynamic range and low-light imaging on mobile cameras

Samuel W. Hasinoff;Dillon Sharlet;Ryan Geiss;Andrew Adams

479
Citations
RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs

Michael Niemeyer;Jonathan T. Barron;Ben Mildenhall;Mehdi S. M. Sajjadi

474
Citations
Burst Denoising with Kernel Prediction Networks

Ben Mildenhall;Jonathan T. Barron;Jiawen Chen;Dillon Sharlet

423
Citations
Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform

Liang-Chieh Chen;Jonathan T. Barron;George Papandreou;Kevin Murphy

407
Citations
Unprocessing Images for Learned Raw Denoising

Tim Brooks;Ben Mildenhall;Tianfan Xue;Jiawen Chen

386
Citations
NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis

Pratul P. Srinivasan;Boyang Deng;Xiuming Zhang;Matthew Tancik

384
Citations
The Fast Bilateral Solver

Jonathan T. Barron;Ben Poole

374
Citations
iNeRF: Inverting Neural Radiance Fields for Pose Estimation

Lin Yen-Chen;Pete Florence;Jonathan T. Barron;Alberto Rodriguez

365
Citations
Pushing the Boundaries of View Extrapolation With Multiplane Images

Pratul P. Srinivasan;Richard Tucker;Jonathan T. Barron;Ravi Ramamoorthi

343
Citations
Deformable Neural Radiance Fields.

Keunhong Park;Utkarsh Sinha;Jonathan T. Barron;Sofien Bouaziz

120
Citations

Frequent Co-Authors

Pratul P. Srinivasan Google

Ben Mildenhall Google

Ravi Ramamoorthi University of California, San Diego

Jitendra Malik University of California, Berkeley

Paul Debevec Google (United States)

Christoph Rhemann Google (United States)

Steven M. Seitz University of Washington

Sam T. Roweis New York University

Noah Snavely Cornell University

Sean Ryan Fanello Google (United States)

External Links

Personal website of Jonathan T. Barron Google Scholar page

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

Exploring online degree options can help you launch your Computer Science career more flexibly and affordably. If you're looking for the fastest degree to get online, there are accelerated computer science and IT programs that can be completed in less time, allowing you to enter the workforce sooner.

With the rapid growth of artificial intelligence, pursuing ai degrees online is an increasingly popular choice. These programs cover essential topics like machine learning and data analytics, preparing you for in-demand tech roles.

When deciding which educational path to follow, it's helpful to compare college programs and majors based on factors such as career prospects, potential earnings, and personal interests. Computer Science consistently ranks among the top choices for students looking to maximize their future opportunities.

For those already holding a bachelor's degree, an easy masters degree in fields related to tech or business can add further value without an overwhelming time investment. This flexibility makes it easier than ever to specialize and advance your career entirely online.