World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
4655
World Ranking
13130
National Ranking
5274

Sean Ryan Fanello 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 Sean Ryan Fanello 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: 95 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

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

Sean Ryan Fanello 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 Sean Ryan Fanello 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: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Overview

Sean Ryan Fanello is affiliated with Google in the United States and has contributed extensively to the fields of computer science and engineering. Their research primarily focuses on computer vision and pattern recognition, with additional work in computer graphics and computer-aided design, computational mechanics, control and systems engineering, and media technology.

The scientist's main topics of study include advanced vision and imaging, computer graphics and visualization techniques, 3D shape modeling and analysis, image enhancement techniques, generative adversarial networks and image synthesis, human pose and action recognition, and face recognition and analysis.

Sean Ryan Fanello has published in a variety of venues, with frequent contributions to arXiv (Cornell University), ACM Transactions on Graphics, the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Computer Graphics Forum, and GetMobile Mobile Computing and Communications. These venues reflect the scientist's focus on graphics, vision, and computational methods.

Recent papers authored or co-authored by Sean Ryan Fanello include the following:

  • Total relighting, 2021, ACM Transactions on Graphics
  • Light stage super-resolution, 2020, ACM Transactions on Graphics
  • Multiresolution Deep Implicit Functions for 3D Shape Representation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • State of the Art on Neural Rendering, 2020, Computer Graphics Forum
  • Neural rendering in a room, 2022, ACM Transactions on Graphics

The scientist frequently collaborates with peers in related research, including Yinda Zhang, Feitong Tan, Rohit Pandey, Sofien Bouaziz, and Qiangeng Xu.

Best Publications

  • Holoportation: Virtual 3D Teleportation in Real-time

    Sergio Orts-Escolano;Christoph Rhemann;Sean Fanello;Wayne Chang

  • Fusion4D: real-time performance capture of challenging scenes

    Mingsong Dou;Sameh Khamis;Yury Degtyarev;Philip Davidson

  • StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction

    Sameh Khamis;Sean Ryan Fanello;Christoph Rhemann;Adarsh Kowdle

  • State of the Art on Neural Rendering

    Ayush Tewari;Ohad Fried;Justus Thies;Vincent Sitzmann

  • The relightables: volumetric performance capture of humans with realistic relighting

    Kaiwen Guo;Peter Lincoln;Philip Davidson;Jay Busch

  • In-air gestures around unmodified mobile devices

    Jie Song;Gábor Sörös;Fabrizio Pece;Sean Ryan Fanello

  • HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching

    Vladimir Tankovich;Christian Hane;Yinda Zhang;Adarsh Kowdle

  • Motion2fusion: real-time volumetric performance capture

    Mingsong Dou;Philip Davidson;Sean Ryan Fanello;Sameh Khamis

  • HyperDepth: Learning Depth from Structured Light without Matching

    Sean Ryan Fanello;Christoph Rhemann;Vladimir Tankovich;Adarsh Kowdle

  • LookinGood: enhancing performance capture with real-time neural re-rendering

    Ricardo Martin-Brualla;Rohit Pandey;Shuoran Yang;Pavel Pidlypenskyi

  • Keep it simple and sparse: real-time action recognition

    Sean Ryan Fanello;Ilaria Gori;Giorgio Metta;Francesca Odone

  • Total relighting: learning to relight portraits for background replacement

    Rohit Pandey;Sergio Orts Escolano;Chloe Legendre;Christian Häne

  • ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems

    Yinda Zhang;Yinda Zhang;Sameh Khamis;Christoph Rhemann;Julien P. C. Valentin

  • Depth from motion for smartphone AR

    Julien Valentin;Adarsh Kowdle;Jonathan T. Barron;Neal Wadhwa

  • Learning to be a depth camera for close-range human capture and interaction

    Sean Ryan Fanello;Cem Keskin;Shahram Izadi;Pushmeet Kohli

  • FlexSense: a transparent self-sensing deformable surface

    Christian Rendl;David Kim;Sean Fanello;Patrick Parzer

  • Deep reflectance fields: high-quality facial reflectance field inference from color gradient illumination

    Abhimitra Meka;Christian Häne;Rohit Pandey;Michael Zollhöfer

  • UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems

    Sean Ryan Fanello;Julien Valentin;Christoph Rhemann;Adarsh Kowdle

  • Filter Forests for Learning Data-Dependent Convolutional Kernels

    Sean Ryan Fanello;Cem Keskin;Pushmeet Kohli;Shahram Izadi

  • Total relighting

    Unknown

  • Advances in neural rendering

    A. Tewari;O. Fried;J. Thies;V. Sitzmann

  • Deep relightable textures: volumetric performance capture with neural rendering

    Abhimitra Meka;Rohit Pandey;Christian Häne;Sergio Orts-Escolano

  • HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching

    Vladimir Tankovich;Christian Häne;Sean Fanello;Yinda Zhang

  • ActiveStereoNet: Unsupervised End-to-End Learning for Active Stereo Systems

    Yinda Zhang;Sameh Khamis;Christoph Rhemann;Julien Valentin

Frequent Co-Authors

Shahram Izadi
Shahram Izadi Google (United States)
Christoph Rhemann
Christoph Rhemann Google (United States)
Yinda Zhang
Yinda Zhang Google (United States)
Giorgio Metta
Giorgio Metta Italian Institute of Technology
Paul Debevec
Paul Debevec Google (United States)
David Kim
David Kim Microsoft (United States)
Andrea Tagliasacchi
Andrea Tagliasacchi Simon Fraser University
Thomas Funkhouser
Thomas Funkhouser Google (United States)
Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
Christian Theobalt
Christian Theobalt Max Planck Institute for Informatics

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