World's Best Scientists 2026 revealed!
Leonidas J. Guibas

Leonidas J. Guibas

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Computer Science
USA
2026

D-Index & Metrics

Computer Science

D-Index
148
Citations
119991
World Ranking
37
National Ranking
21

Leonidas J. Guibas 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 Leonidas J. Guibas 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 646 publications — 97th percentile

97% of scientists in this discipline score the same or lower.

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

Leonidas J. Guibas 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 Leonidas J. Guibas sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 148 D-Index — 100th percentile

100% 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

  • 2026 - Research.com Computer Science in United States Leader Award
  • 2025 - Research.com Computer Science in United States Leader Award
  • 2023 - Research.com Computer Science in United States Leader Award
  • 2022 - Research.com Computer Science in United States Leader Award
  • 2018 - Fellow of the American Academy of Arts and Sciences
  • 2017 - Member of the National Academy of Engineering For contributions to data structures, algorithm analysis, and computational geometry.
  • 2012 - IEEE Fellow For contributions to algorithms for computational geometry
  • 2007 - ACM AAAI Allen Newell Award For pioneering work in computational geometry, with profound applications across an astonishingly broad range of Computer Science disciplines.
  • 1999 - ACM Fellow For his work on geometric data structures, arrangements of surfaces and their applications, geometric algorithms in computer graphics, and algorithmic issues in computer vision.

Overview

Leonidas J. Guibas is affiliated with Stanford University in the United States. Their research primarily spans the fields of Computer Science and Engineering, with a notable focus on Computer Vision and Pattern Recognition, Computational Mechanics, Computer Graphics and Computer-Aided Design, Control and Systems Engineering, and Artificial Intelligence.

The main topics covered in their work include 3D Shape Modeling and Analysis, Computer Graphics and Visualization Techniques, Advanced Vision and Imaging, Human Pose and Action Recognition, 3D Surveying and Cultural Heritage, Robotics and Sensor-Based Localization, and Image Processing and 3D Reconstruction.

Among their recent publications are the following papers:

  • ShapeNet: An Information-Rich 3D Model Repository, 2023, published in Zenodo (CERN European Organization for Nuclear Research)
  • Efficient Geometry-aware 3D Generative Adversarial Networks, 2022, published in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation, 2022, published in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Vector Neurons: A General Framework for SO(3)-Equivariant Networks, 2021, published in 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Next-generation deep learning based on simulators and synthetic data, 2021, published in Trends in Cognitive Sciences

Frequent collaborators in their research include Kaichun Mo, Tolga Birdal, Gordon Wetzstein, Congyue Deng, and Yanchao Yang.

Their work has been published predominantly in venues such as:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • ACM Transactions on Graphics
  • Computer Graphics Forum

Leonidas J. Guibas has received several professional honors, including:

  • Fellow of the American Academy of Arts and Sciences, 2018
  • Member of the National Academy of Engineering, 2017, for contributions to data structures, algorithm analysis, and computational geometry
  • IEEE Fellow, 2012, for contributions to algorithms for computational geometry
  • ACM AAAI Allen Newell Award, 2007, for pioneering work in computational geometry with applications across various Computer Science disciplines
  • ACM Fellow, 1999, for work on geometric data structures, arrangements of surfaces and applications, geometric algorithms in computer graphics, and algorithmic issues in computer vision

Best Publications

  • PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

    R. Qi Charles;Hao Su;Mo Kaichun;Leonidas J. Guibas

  • PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

    Charles Ruizhongtai Qi;Li Yi;Hao Su;Leonidas J. Guibas

  • The Earth Mover's Distance as a Metric for Image Retrieval

    Yossi Rubner;Carlo Tomasi;Leonidas J. Guibas

  • PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

    Charles R. Qi;Hao Su;Kaichun Mo;Leonidas J. Guibas

  • KPConv: Flexible and Deformable Convolution for Point Clouds

    Hugues Thomas;Charles R. Qi;Jean-Emmanuel Deschaud;Beatriz Marcotegui

  • Frustum PointNets for 3D Object Detection from RGB-D Data

    Charles R. Qi;Wei Liu;Chenxia Wu;Hao Su

  • A Point Set Generation Network for 3D Object Reconstruction from a Single Image

    Haoqiang Fan;Hao Su;Leonidas Guibas

  • A metric for distributions with applications to image databases

    Y. Rubner;C. Tomasi;L.J. Guibas

  • ShapeNet: An Information-Rich 3D Model Repository

    Angel X. Chang;Thomas A. Funkhouser;Leonidas J. Guibas;Pat Hanrahan

  • Primitives for the manipulation of general subdivisions and the computation of Voronoi

    Leonidas Guibas;Jorge Stolfi

  • Wireless Sensor Networks: An Information Processing Approach

    Feng Zhao;Leonidas Guibas

  • A concise and provably informative multi-scale signature based on heat diffusion

    Jian Sun;Maks Ovsjanikov;Leonidas Guibas

  • Volumetric and Multi-view CNNs for Object Classification on 3D Data

    Charles R. Qi;Hao Su;Matthias NieBner;Angela Dai

  • A scalable active framework for region annotation in 3D shape collections

    Li Yi;Vladimir G. Kim;Duygu Ceylan;I-Chao Shen

  • Deep Hough Voting for 3D Object Detection in Point Clouds

    Charles R. Qi;Or Litany;Kaiming He;Leonidas Guibas

  • A dichromatic framework for balanced trees

    Leo J. Guibas;Robert Sedgewick

  • Locating and bypassing holes in sensor networks

    Qing Fang;Jie Gao;Leonidas J. Guibas

  • Robust Monte Carlo methods for light transport simulation

    Leonidas J. Guibas;Eric Veach

  • Learning Representations and Generative Models for 3D Point Clouds.

    Panos Achlioptas;Olga Diamanti;Ioannis Mitliagkas;Leonidas J. Guibas

  • Taskonomy: Disentangling Task Transfer Learning

    Amir R. Zamir;Alexander Sax;William Shen;Leonidas Guibas

  • Volumetric and Multi-View CNNs for Object Classification on 3D Data

    Charles R. Qi;Hao Su;Matthias Niessner;Angela Dai

  • Deep Knowledge Tracing

    Chris Piech;Jonathan Spencer;Jonathan Huang;Surya Ganguli

  • Taskonomy: Disentangling Task Transfer Learning.

    Amir Roshan Zamir;Amir Roshan Zamir;Alexander Sax;William B. Shen;Leonidas J. Guibas

  • Primitives for the manipulation of general subdivisions and the computation of Voronoi diagrams

    Leo J. Guibas;Jorge Stolfi

Frequent Co-Authors

Micha Sharir
Micha Sharir Tel Aviv University
Hao Su
Hao Su University of California, San Diego
John Hershberger
John Hershberger Mentor Graphics
Qixing Huang
Qixing Huang The University of Texas at Austin
Herbert Edelsbrunner
Herbert Edelsbrunner Institute of Science and Technology Austria
Maks Ovsjanikov
Maks Ovsjanikov École Polytechnique
Jie Gao
Jie Gao Rutgers, The State University of New Jersey
Li Zhang
Li Zhang Google (United States)
Bernard Chazelle
Bernard Chazelle Princeton University
Niloy J. Mitra
Niloy J. Mitra University College London

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