World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
18243
World Ranking
7368
National Ranking
291

Manolis Savva 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 Manolis Savva 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: 86 publications — 5th percentile

5% of scientists in this discipline score the same or lower.

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

Manolis Savva 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 Manolis Savva 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: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Overview

Manolis Savva is affiliated with Simon Fraser University in Canada and has made significant contributions in the fields of computer science and engineering. Their research primarily focuses on computer vision and pattern recognition, artificial intelligence, computational mechanics, geology, and control and systems engineering.

The scientist's work covers a range of topics including multimodal machine learning applications, human pose and action recognition, 3D shape modeling and analysis, 3D surveying and cultural heritage, reinforcement learning in robotics, advanced neural network applications, and image processing and 3D reconstruction.

Manolis Savva has a number of frequently cited recent publications, including:

  • ShapeNet: An Information-Rich 3D Model Repository, 2023, published in Zenodo (CERN European Organization for Nuclear Research)
  • ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects, 2020, arXiv (Cornell University)
  • Rearrangement: A Challenge for Embodied AI, 2020, arXiv (Cornell University)
  • MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation, 2020, arXiv (Cornell University)
  • Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI, 2021, arXiv (Cornell University)

Research collaborations are an important aspect of Savva's work. Frequent co-authors include Anne Lynn S. Chang, Dhruv Batra, Erik Wijmans, Hanxiao Jiang, and Ali Mahdavi-Amiri.

Publication venues where Manolis Savva has often published include:

  • arXiv (Cornell University)
  • Computer Graphics Forum
  • Zenodo (CERN European Organization for Nuclear Research)
  • AI Matters
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Best Publications

  • ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

    Angela Dai;Angel X. Chang;Manolis Savva;Maciej Halber

  • ShapeNet: An Information-Rich 3D Model Repository

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

  • Matterport3D: Learning from RGB-D Data in Indoor Environments

    Angel Chang;Angela Dai;Thomas Funkhouser;Maciej Halber

  • Semantic Scene Completion from a Single Depth Image

    Shuran Song;Fisher Yu;Andy Zeng;Angel X. Chang

  • Habitat: A Platform for Embodied AI Research

    Manolis Savva;Jitendra Malik;Devi Parikh;Dhruv Batra

  • ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes

    Angela Dai;Angel X. Chang;Manolis Savva;Maciej Halber

  • On Evaluation of Embodied Navigation Agents

    Peter Anderson;Angel X. Chang;Devendra Singh Chaplot;Alexey Dosovitskiy

  • Matterport3D: Learning from RGB-D Data in Indoor Environments

    Angel Chang;Angela Dai;Thomas Funkhouser;Maciej Halber

  • The Replica Dataset: A Digital Replica of Indoor Spaces.

    Julian Straub;Thomas Whelan;Lingni Ma;Yufan Chen

  • Example-based synthesis of 3D object arrangements

    Matthew Fisher;Daniel Ritchie;Manolis Savva;Thomas Funkhouser

  • Habitat: A Platform for Embodied AI Research

    Manolis Savva;Abhishek Kadian;Oleksandr Maksymets;Yili Zhao

  • ReVision: automated classification, analysis and redesign of chart images

    Manolis Savva;Nicholas Kong;Arti Chhajta;Li Fei-Fei

  • Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks

    Yinda Zhang;Shuran Song;Ersin Yumer;Manolis Savva

  • MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments

    Manolis Savva;Angel X. Chang;Alexey Dosovitskiy;Thomas A. Funkhouser

  • DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

    Erik Wijmans;Abhishek Kadian;Ari Morcos;Stefan Lee

  • Scan2CAD: Learning CAD Model Alignment in RGB-D Scans

    Armen Avetisyan;Manuel Dahnert;Angela Dai;Manolis Savva

  • Characterizing structural relationships in scenes using graph kernels

    Matthew Fisher;Manolis Savva;Pat Hanrahan

  • Deep convolutional priors for indoor scene synthesis

    Kai Wang;Manolis Savva;Angel X. Chang;Daniel Ritchie

  • PlanIT: planning and instantiating indoor scenes with relation graph and spatial prior networks

    Kai Wang;Yu-An Lin;Ben Weissmann;Manolis Savva

  • Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?

    Abhishek Kadian;Joanne Truong;Aaron Gokaslan;Alexander Clegg

  • ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects.

    Dhruv Batra;Aaron Gokaslan;Aniruddha Kembhavi;Oleksandr Maksymets

  • Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings.

    Kevin Chen;Christopher B. Choy;Manolis Savva;Angel X. Chang

Frequent Co-Authors

Angel X. Chang
Angel X. Chang Simon Fraser University
Thomas Funkhouser
Thomas Funkhouser Google (United States)
Dhruv Batra
Dhruv Batra Georgia Institute of Technology
Pat Hanrahan
Pat Hanrahan Stanford University
Shuran Song
Shuran Song Stanford University
Vladlen Koltun
Vladlen Koltun Apple (United States)
Christopher D. Manning
Christopher D. Manning Stanford University
Stefan Lee
Stefan Lee Oregon State University
Jitendra Malik
Jitendra Malik University of California, Berkeley
Matthew Fisher
Matthew Fisher Adobe Systems (United States)

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