World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
66
Citations
32324
World Ranking
2255
National Ranking
1126

Jonathan T. Barron publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Jonathan T. Barron sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 121 publications — 15th percentile

15% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Jonathan T. Barron D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Jonathan T. Barron sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 66 D-Index — 84th percentile

84% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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

  • Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields

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

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

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

  • DreamFusion: Text-to-3D using 2D Diffusion

    Unknown

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

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

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

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

  • Block-NeRF: Scalable Large Scene Neural View Synthesis

    Unknown

  • Shape, Illumination, and Reflectance from Shading

    Jonathan T. Barron;Jitendra Malik

  • Deep bilateral learning for real-time image enhancement

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

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

    Unknown

  • HyperNeRF

    Unknown

  • Zero-Shot Text-Guided Object Generation with Dream Fields

    Unknown

  • IBRNet: Learning Multi-View Image-Based Rendering

    Qianqian Wang;Zhicheng Wang;Kyle Genova;Pratul Srinivasan

  • Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation

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

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

    Unknown

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

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

  • A General and Adaptive Robust Loss Function

    Jonathan T. Barron

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

    Unknown

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

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

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

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

  • Burst Denoising with Kernel Prediction Networks

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

  • 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

  • Unprocessing Images for Learned Raw Denoising

    Tim Brooks;Ben Mildenhall;Tianfan Xue;Jiawen Chen

  • NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis

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

  • The Fast Bilateral Solver

    Jonathan T. Barron;Ben Poole

  • iNeRF: Inverting Neural Radiance Fields for Pose Estimation

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

  • Pushing the Boundaries of View Extrapolation With Multiplane Images

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

  • Deformable Neural Radiance Fields.

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

Frequent Co-Authors

Ren Ng
Ren Ng University of California, Berkeley
Ravi Ramamoorthi
Ravi Ramamoorthi University of California, San Diego
Jitendra Malik
Jitendra Malik University of California, Berkeley
Paul Debevec
Paul Debevec Google (United States)
Christoph Rhemann
Christoph Rhemann Google (United States)
Sam T. Roweis
Sam T. Roweis New York University
Noah Snavely
Noah Snavely Cornell University
Steven M. Seitz
Steven M. Seitz University of Washington

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

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

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.

Best Scientists Citing Jonathan T. Barron

Trending Scientists

Recently Published Articles