World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
81
Citations
30748
World Ranking
1004
National Ranking
536

Ravi Ramamoorthi 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 Ravi Ramamoorthi 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: 248 publications — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Ravi Ramamoorthi 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 Ravi Ramamoorthi 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: 81 D-Index — 93rd percentile

93% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2017 - ACM Fellow For contributions to computer graphics rendering and physics-based computer vision
  • 2017 - IEEE Fellow For contributions to foundations of computer graphics and computer vision
  • 2015 - ACM Distinguished Member
  • 2005 - Fellow of Alfred P. Sloan Foundation

Overview

Ravi Ramamoorthi is affiliated with the University of California, San Diego in the United States. Their research primarily spans computer science and engineering, with substantial contributions focused on computer vision and computer graphics.

Their main subfields of study include:

  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Computational Mechanics
  • Atomic and Molecular Physics, and Optics
  • Instrumentation

Ramamoorthi's research covers various topics, prominently featuring computer graphics and visualization techniques, advanced vision and imaging, and 3D shape modeling and analysis. Other significant topics in their work include image enhancement and advanced image processing techniques, generative adversarial networks and image synthesis, and face recognition and analysis.

The scientist has published extensively, with frequent contributions to leading venues such as:

  • arXiv (Cornell University)
  • ACM Transactions on Graphics
  • Computer Graphics Forum
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Some recent papers authored or coauthored by Ramamoorthi include:

  • NeRF, 2021, Communications of the ACM
  • Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains, 2020, arXiv (Cornell University)
  • NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, 2020, arXiv (Cornell University)
  • Neural Reflectance Fields for Appearance Acquisition, 2020, arXiv (Cornell University)
  • Modulated Periodic Activations for Generalizable Local Functional Representations, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Frequent coauthors collaborating with Ramamoorthi include:

  • Zexiang Xu
  • Manmohan Chandraker
  • Sai Bi
  • Tiancheng Sun
  • Kai-En Lin

Recognized in their field, Ramamoorthi has received several awards including ACM Fellow and IEEE Fellow in 2017 for contributions to computer graphics and computer vision foundations. They were named an ACM Distinguished Member in 2015 and a Fellow of the Alfred P. Sloan Foundation in 2005.

Best Publications

  • NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

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

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

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

  • Local light field fusion: practical view synthesis with prescriptive sampling guidelines

    Ben Mildenhall;Pratul P. Srinivasan;Rodrigo Ortiz-Cayon;Nima Khademi Kalantari

  • An efficient representation for irradiance environment maps

    Ravi Ramamoorthi;Pat Hanrahan

  • A signal-processing framework for inverse rendering

    Ravi Ramamoorthi;Pat Hanrahan

  • Learning-based view synthesis for light field cameras

    Nima Khademi Kalantari;Ting-Chun Wang;Ravi Ramamoorthi

  • Depth from Combining Defocus and Correspondence Using Light-Field Cameras

    Michael W. Tao;Sunil Hadap;Jitendra Malik;Ravi Ramamoorthi

  • Image to Image Translation for Domain Adaptation

    Zak Murez;Soheil Kolouri;David Kriegman;Ravi Ramamoorthi

  • Spacetime stereo: a unifying framework for depth from triangulation

    J. Davis;R. Ramamoorthi;S. Rusinkiewicz

  • Spacetime stereo: a unifying framework for depth from triangulation

    J. Davis;D. Nehab;R. Ramamoorthi;S. Rusinkiewicz

  • Efficiently combining positions and normals for precise 3D geometry

    Diego Nehab;Szymon Rusinkiewicz;James Davis;Ravi Ramamoorthi

  • Deep high dynamic range imaging of dynamic scenes

    Nima Khademi Kalantari;Ravi Ramamoorthi

  • On the relationship between radiance and irradiance: determining the illumination from images of a convex Lambertian object

    Ravi Ramamoorthi;Pat Hanrahan

  • All-frequency shadows using non-linear wavelet lighting approximation

    Ren Ng;Ravi Ramamoorthi;Pat Hanrahan

  • Occlusion-Aware Depth Estimation Using Light-Field Cameras

    Ting-Chun Wang;Alexei A. Efros;Ravi Ramamoorthi

  • Deep Stereo Using Adaptive Thin Volume Representation With Uncertainty Awareness

    Shuo Cheng;Zexiang Xu;Shilin Zhu;Zhuwen Li

  • Pushing the Boundaries of View Extrapolation With Multiplane Images

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

  • Analytic PCA construction for theoretical analysis of lighting variability in images of a Lambertian object

    R. Ramamoorthi

  • Triple product wavelet integrals for all-frequency relighting

    Ren Ng;Ravi Ramamoorthi;Pat Hanrahan

  • Learning to reconstruct shape and spatially-varying reflectance from a single image

    Zhengqin Li;Zexiang Xu;Ravi Ramamoorthi;Kalyan Sunkavalli

  • Frequency space environment map rendering

    Ravi Ramamoorthi;Pat Hanrahan

Frequent Co-Authors

Peter N. Belhumeur
Peter N. Belhumeur Columbia University
Shree K. Nayar
Shree K. Nayar Columbia University
Manmohan Chandraker
Manmohan Chandraker University of California, San Diego
Kalyan Sunkavalli
Kalyan Sunkavalli Adobe Systems (United States)
Ren Ng
Ren Ng University of California, Berkeley
Henrik Wann Jensen
Henrik Wann Jensen University of California, San Diego
Szymon Rusinkiewicz
Szymon Rusinkiewicz Princeton University
Maneesh Agrawala
Maneesh Agrawala Stanford University

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