World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
70
Citations
36163
World Ranking
1824
National Ranking
929

Noah Snavely 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 Noah Snavely 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: 173 publications — 36th percentile

36% of scientists in this discipline score the same or lower.

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

Noah Snavely 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 Noah Snavely 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: 70 D-Index — 87th percentile

87% 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

  • 2012 - Fellow of Alfred P. Sloan Foundation

Overview

Noah Snavely is a researcher affiliated with Cornell University in the United States. Their work primarily focuses on computer science, with a specialization in computer vision and pattern recognition. Their research spans multiple subfields including computer graphics and computer-aided design, computational mechanics, aerospace engineering, and plant science.

The scientist's main topics of work include advanced vision and imaging, computer graphics and visualization techniques, generative adversarial networks and image synthesis, advanced image and video retrieval techniques, 3D shape modeling and analysis, image enhancement techniques, and robotics and sensor-based localization.

Frequent coauthors of Noah Snavely include Zhengqi Li, Hadar Averbuch-Elor, Bharath Hariharan, Angjoo Kanazawa, and Ruojin Cai.

They have published extensively, with a concentrated presence in venues such as:

  • arXiv (Cornell University)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Applications in Plant Sciences
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Some recent papers by Noah Snavely include:

  • NeRF++: Analyzing and Improving Neural Radiance Fields (2020), published in arXiv (Cornell University)
  • Unsupervised Semantic Segmentation by Distilling Feature Correspondences (2022), published in arXiv (Cornell University)
  • Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image (2021), presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • MetaSDF: Meta-learning Signed Distance Functions (2020), released via arXiv (Cornell University)
  • IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images (2022), presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Noah Snavely was awarded a fellowship by the Alfred P. Sloan Foundation in 2012.

Best Publications

  • Photo tourism: exploring photo collections in 3D

    Noah Snavely;Steven M. Seitz;Richard Szeliski

  • Modeling the World from Internet Photo Collections

    Noah Snavely;Steven M. Seitz;Richard Szeliski

  • Unsupervised Learning of Depth and Ego-Motion from Video

    Tinghui Zhou;Matthew Brown;Noah Snavely;David G. Lowe

  • Building Rome in a day

    Sameer Agarwal;Yasutaka Furukawa;Noah Snavely;Ian Simon

  • Building Rome in a day

    Sameer Agarwal;Noah Snavely;Ian Simon;Steven M. Seitz

  • MegaDepth: Learning Single-View Depth Prediction from Internet Photos

    Zhengqi Li;Noah Snavely

  • Multi-View Stereo for Community Photo Collections

    M. Goesele;N. Snavely;B. Curless;H. Hoppe

  • Stereo magnification: learning view synthesis using multiplane images

    Tinghui Zhou;Richard Tucker;John Flynn;Graham Fyffe

  • Spacetime faces: high resolution capture for modeling and animation

    Li Zhang;Noah Snavely;Brian Curless;Steven M. Seitz

  • Deep Stereo: Learning to Predict New Views from the World's Imagery

    John Flynn;Ivan Neulander;James Philbin;Noah Snavely

  • IBRNet: Learning Multi-View Image-Based Rendering

    Qianqian Wang;Zhicheng Wang;Kyle Genova;Pratul Srinivasan

  • Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes

    Zhengqi Li;Simon Niklaus;Noah Snavely;Oliver Wang

  • Material recognition in the wild with the Materials in Context Database

    Sean Bell;Paul Upchurch;Noah Snavely;Kavita Bala

  • Bundle adjustment in the large

    Sameer Agarwal;Noah Snavely;Steven M. Seitz;Richard Szeliski

  • Location recognition using prioritized feature matching

    Yunpeng Li;Noah Snavely;Daniel P. Huttenlocher

  • NeRF++: Analyzing and Improving Neural Radiance Fields.

    Kai Zhang;Gernot Riegler;Noah Snavely;Vladlen Koltun

  • Worldwide pose estimation using 3d point clouds

    Yunpeng Li;Noah Snavely;Dan Huttenlocher;Pascal Fua

  • Scene Summarization for Online Image Collections

    I. Simon;N. Snavely;S.M. Seitz

  • DeepView: View Synthesis With Learned Gradient Descent

    John Flynn;Michael Broxton;Paul Debevec;Matthew DuVall

  • Intrinsic images in the wild

    Sean Bell;Kavita Bala;Noah Snavely

  • Discrete-continuous optimization for large-scale structure from motion

    David Crandall;Andrew Owens;Noah Snavely;Dan Huttenlocher

  • Robust Global Translations with 1DSfM

    Kyle Wilson;Noah Snavely

Frequent Co-Authors

Steven M. Seitz
Steven M. Seitz University of Washington
Kavita Bala
Kavita Bala Cornell University
Richard Szeliski
Richard Szeliski University of Washington
Sameer Agarwal
Sameer Agarwal Google (United States)
Brian Curless
Brian Curless University of Washington
Jonathan T. Barron
Jonathan T. Barron Google (United States)
David J. Crandall
David J. Crandall Indiana University
Serge Belongie
Serge Belongie University of Copenhagen

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