World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
70
Citations
91395
World Ranking
1814
National Ranking
919

Hao Su 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 Hao Su 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: 205 publications — 48th percentile

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

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

Hao Su 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 Hao Su 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.

Overview

Hao Su is a researcher affiliated with the University of California, San Diego in the United States. Their work spans multiple areas within computer science and engineering, focusing mainly on computer vision, computer graphics, and related computational techniques.

Their research contributions include topics such as:

  • Advanced Vision and Imaging
  • Computer Graphics and Visualization Techniques
  • 3D Shape Modeling and Analysis
  • Generative Adversarial Networks and Image Synthesis
  • Human Pose and Action Recognition
  • Multimodal Machine Learning Applications
  • Robot Manipulation and Learning

Main fields of study in Hao Su's research profile include:

  • Computer Science
  • Engineering

Within these broader domains, they have a specialized focus on subfields such as:

  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Computational Mechanics
  • Mechanical Engineering

Notable recent papers authored or co-authored by Hao Su include:

  • "MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo" (2021) presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction" (2022) presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Visibility-Aware Point-Based Multi-View Stereo Network" (2020) published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "ImageNet Large Scale Visual Recognition Challenge" (2024) posted on arXiv (Cornell University)
  • "OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding" (2023) posted on arXiv (Cornell University)

Hao Su has collaborated frequently with several co-authors, including:

  • Xiaoshuai Zhang
  • Zexiang Xu
  • Yuzhe Qin
  • Leonidas Guibas
  • Fanbo Xiang

The researcher has published numerous works in a variety of venues, with frequent appearances in:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Proceedings of the AAAI Conference on Artificial Intelligence

Hao Su is also an author of a forthcoming book titled Robotic Intelligent Assembly, to be published by Springer Nature in 2025.

Best Publications

  • ImageNet Large Scale Visual Recognition Challenge

    Olga Russakovsky;Jia Deng;Hao Su;Jonathan Krause

  • 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

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

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

  • 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

  • ShapeNet: An Information-Rich 3D Model Repository

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

  • 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

  • Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification

    Li-jia Li;Hao Su;Li Fei-fei;Eric P. Xing

  • Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views

    Hao Su;Charles R. Qi;Yangyan Li;Leonidas J. Guibas

  • MVSNeRF: Fast Generalizable Radiance Field Reconstruction From Multi-View Stereo

    Anpei Chen;Zexiang Xu;Fuqiang Zhao;Xiaoshuai Zhang

  • PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding

    Kaichun Mo;Shilin Zhu;Angel X. Chang;Li Yi

  • SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation

    Li Yi;Hao Su;Xingwen Guo;Leonidas Guibas

  • Leveraging elastic instabilities for amplified performance: Spine-inspired high-speed and high-force soft robots

    Yichao Tang;Yichao Tang;Yinding Chi;Jiefeng Sun;Tzu-Hao Huang

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

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

  • SAPIEN: A SimulAted Part-Based Interactive ENvironment

    Fanbo Xiang;Yuzhe Qin;Kaichun Mo;Yikuan Xia

  • Point-Based Multi-View Stereo Network

    Rui Chen;Songfang Han;Jing Xu;Hao Su

  • Learning Shape Abstractions by Assembling Volumetric Primitives

    Shubham Tulsiani;Hao Su;Leonidas J. Guibas;Alexei A. Efros

  • Deep Stereo Using Adaptive Thin Volume Representation With Uncertainty Awareness

    Shuo Cheng;Zexiang Xu;Shilin Zhu;Zhuwen Li

  • ObjectNet3D: A Large Scale Database for 3D Object Recognition

    Yu Xiang;Wonhui Kim;Wei Chen;Jingwei Ji

  • Synthesizing Training Images for Boosting Human 3D Pose Estimation

    Wenzheng Chen;Huan Wang;Yangyan Li;Hao Su

  • ImageNet Large Scale Visual Recognition Challenge

    Olga Russakovsky;Jia Deng;Hao Su;Jonathan Krause

Frequent Co-Authors

Leonidas J. Guibas
Leonidas J. Guibas Stanford University
Honggang Cui
Honggang Cui Johns Hopkins University
Wen-Bin Zhang
Wen-Bin Zhang Peking University
Stephen Z. D. Cheng
Stephen Z. D. Cheng University of Akron
Li Fei-Fei
Li Fei-Fei Stanford University
Yiwen Li
Yiwen Li Sichuan University
Angel X. Chang
Angel X. Chang Simon Fraser University
Qixing Huang
Qixing Huang The University of Texas at Austin
Dave J. Adams
Dave J. Adams University of Glasgow
Chrys Wesdemiotis
Chrys Wesdemiotis University of Akron

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