World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
11267
World Ranking
4825
National Ranking
289

Yu-Kun Lai 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 Yu-Kun Lai 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: 276 publications — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Yu-Kun Lai 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 Yu-Kun Lai 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: 53 D-Index — 67th percentile

67% of scientists in this discipline score the same or lower.

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

Overview

Yu-Kun Lai is affiliated with Cardiff University in the United Kingdom. Their research spans the fields of Computer Science and Engineering, with a focus on several subfields including Computer Vision and Pattern Recognition, Computational Mechanics, Computer Graphics and Computer-Aided Design, Control and Systems Engineering, and Artificial Intelligence.

The scientist's work is documented in numerous papers published across a variety of venues. Prominent publication venues include:

  • arXiv (Cornell University)
  • IEEE Transactions on Visualization and Computer Graphics
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • ACM Transactions on Graphics
  • Computational Visual Media

The frequent co-authors collaborating with Yu-Kun Lai are:

  • Lin Gao
  • Paul L. Rosin
  • Kun Li
  • Ze Ji
  • Jing Wu

Yu-Kun Lai has contributed to various research topics, including:

  • 3D Shape Modeling and Analysis
  • Advanced Vision and Imaging
  • Computer Graphics and Visualization Techniques
  • Generative Adversarial Networks and Image Synthesis
  • Human Pose and Action Recognition
  • Face Recognition and Analysis
  • Advanced Image and Video Retrieval Techniques

Selected recent papers authored or co-authored by Yu-Kun Lai include:

  • "An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals," 2020, IEEE Transactions on Affective Computing
  • "NeRF-Editing: Geometry Editing of Neural Radiance Fields," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "A survey on deep geometry learning: From a representation perspective," 2020, Computational Visual Media
  • "Hierarchical Reinforcement Learning With Universal Policies for Multistep Robotic Manipulation," 2021, IEEE Transactions on Neural Networks and Learning Systems

Best Publications

  • Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid

    G. K. L. Tam;Zhi-Quan Cheng;Yu-Kun Lai;F. C. Langbein

  • IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition

    Xiaoping Wu;Chi Zhan;Yu-Kun Lai;Ming-Ming Cheng

  • CartoonGAN: Generative Adversarial Networks for Photo Cartoonization

    Yang Chen;Yu-Kun Lai;Yong-Jin Liu

  • NeRF-Editing: Geometry Editing of Neural Radiance Fields

    Unknown

  • VV-Net: Voxel VAE Net With Group Convolutions for Point Cloud Segmentation

    Hsien-Yu Meng;Lin Gao;Yu-Kun Lai;Dinesh Manocha

  • An Efficient LSTM Network for Emotion Recognition from Multichannel EEG Signals

    Xiaobing Du;Cuixia Ma;Guanhua Zhang;Jinyao Li

  • SDM-NET: deep generative network for structured deformable mesh

    Lin Gao;Jie Yang;Tong Wu;Yu-Jie Yuan

  • MLCVNet: Multi-Level Context VoteNet for 3D Object Detection

    Qian Xie;Yu-Kun Lai;Jing Wu;Zhoutao Wang

  • StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning

    Unknown

  • Variational Autoencoders for Deforming 3D Mesh Models

    Qingyang Tan;Lin Gao;Yu-Kun Lai;Shihong Xia

  • Fast mesh segmentation using random walks

    Yu-Kun Lai;Shi-Min Hu;Ralph R. Martin;Paul L. Rosin

  • APDrawingGAN: Generating Artistic Portrait Drawings From Face Photos With Hierarchical GANs

    Ran Yi;Yong-Jin Liu;Yu-Kun Lai;Paul L. Rosin

  • Weakly Supervised Coupled Networks for Visual Sentiment Analysis

    Jufeng Yang;Dongyu She;Yu-Kun Lai;Paul L. Rosin

  • Robust Feature Classification and Editing

    Yu-Kun Lai;Qian-Yi Zhou;Shi-Min Hu;J. Wallner

  • PISE: Person Image Synthesis and Editing with Decoupled GAN

    Jinsong Zhang;Kun Li;Yu-Kun Lai;Jingyu Yang

  • Automatic and topology-preserving gradient mesh generation for image vectorization

    Yu-Kun Lai;Shi-Min Hu;Ralph R. Martin

  • Robust principal curvatures on multiple scales

    Yong-Liang Yang;Yu-Kun Lai;Shi-Min Hu;Helmut Pottmann

  • Rapid and effective segmentation of 3D models using random walks

    Yu-Kun Lai;Shi-Min Hu;Ralph R. Martin;Paul L. Rosin

  • Automatic semantic modeling of indoor scenes from low-quality RGB-D data using contextual information

    Kang Chen;Yu-Kun Lai;Yu-Xin Wu;Ralph Martin

  • Principal curvatures from the integral invariant viewpoint

    Helmut Pottmann;Johannes Wallner;Yong-Liang Yang;Yu-Kun Lai

  • Automatic unpaired shape deformation transfer

    Lin Gao;Jie Yang;Yi-Ling Qiao;Yu-Kun Lai

  • 3D indoor scene modeling from RGB-D data: a survey

    Kang Chen;Yu-Kun Lai;Shi-Min Hu

Frequent Co-Authors

Paul L. Rosin
Paul L. Rosin Cardiff University
Lin Gao
Lin Gao Xidian University
Shi-Min Hu
Shi-Min Hu Tsinghua University
Ralph R. Martin
Ralph R. Martin Cardiff University
Yong-Jin Liu
Yong-Jin Liu Tsinghua University
Jingyu Yang
Jingyu Yang Tianjin University
Hongbo Fu
Hongbo Fu City University of Hong Kong
Leif Kobbelt
Leif Kobbelt RWTH Aachen University
Yebin Liu
Yebin Liu Tsinghua University
Ming-Ming Cheng
Ming-Ming Cheng Nankai University

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