World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
18965
World Ranking
2570
National Ranking
347

Yulan Guo 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 Yulan Guo 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: 180 publications — 38th percentile

38% of scientists in this discipline score the same or lower.

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

Yulan Guo 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 Yulan Guo 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: 64 D-Index — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Overview

Yulan Guo is affiliated with the National University of Defense Technology in China. Their research primarily spans the fields of computer science and engineering, with concentrated efforts in computer vision and pattern recognition, computational mechanics, aerospace engineering, media technology, and geology.

The scientist's work engages with several main topics within these disciplines, including:

  • Advanced Vision and Imaging
  • 3D Shape Modeling and Analysis
  • Advanced Image Processing Techniques
  • 3D Surveying and Cultural Heritage
  • Image Processing Techniques and Applications
  • Remote Sensing and LiDAR Applications
  • Advanced Neural Network Applications

Yulan Guo has contributed to numerous publications, frequently publishing in venues such as arXiv (Cornell University), IEEE Transactions on Pattern Analysis and Machine Intelligence, the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Journal of Image and Graphics, and IEEE Signal Processing Letters.

Recent published papers include:

  • Deep Learning for 3D Point Clouds: A Survey, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Deep Video Super-Resolution Using HR Optical Flow Estimation, 2020, IEEE Transactions on Image Processing
  • Deformable 3D Convolution for Video Super-Resolution, 2020, IEEE Signal Processing Letters

Throughout their career, Yulan Guo has collaborated frequently with several coauthors, including Longguang Wang, Yingqian Wang, Qingyong Hu, Wei An, and Jungang Yang. These collaborations have contributed to advancing research in 3D data processing, image enhancement, and related computer vision tasks.

Best Publications

  • Deep Learning for 3D Point Clouds: A Survey

    Yulan Guo;Hanyun Wang;Qingyong Hu;Hao Liu

  • RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

    Qingyong Hu;Bo Yang;Linhai Xie;Stefano Rosa

  • Rotational Projection Statistics for 3D Local Surface Description and Object Recognition

    Yulan Guo;Yulan Guo;Ferdous Ahmed Sohel;Mohammed Bennamoun;Min Lu

  • Dense Nested Attention Network for Infrared Small Target Detection.

    Boyang Li;Chao Xiao;Longguang Wang;Yingqian Wang

  • 3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey

    Yulan Guo;Mohammed Bennamoun;Ferdous Ahmed Sohel;Min Lu

  • A Comprehensive Performance Evaluation of 3D Local Feature Descriptors

    Yulan Guo;Mohammed Bennamoun;Ferdous Sohel;Min Lu

  • Geometric Transformer for Fast and Robust Point Cloud Registration

    Unknown

  • Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds

    Unknown

  • Median Robust Extended Local Binary Pattern for Texture Classification

    Li Liu;Songyang Lao;Paul W. Fieguth;Yulan Guo

  • Local binary features for texture classification

    Li Liu;Paul Fieguth;Yulan Guo;Xiaogang Wang

  • Unsupervised Degradation Representation Learning for Blind Super-Resolution

    Longguang Wang;Yingqian Wang;Xiaoyu Dong;Qingyu Xu

  • Learning for Disparity Estimation Through Feature Constancy

    Zhengfa Liang;Yiliu Feng;Yulan Guo;Hengzhu Liu

  • SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration

    Sheng Ao;Qingyong Hu;Bo Yang;Andrew Markham

  • Exploring Sparsity in Image Super-Resolution for Efficient Inference

    Longguang Wang;Xiaoyu Dong;Yingqian Wang;Xinyi Ying

  • Learning Parallax Attention for Stereo Image Super-Resolution

    Longguang Wang;Yingqian Wang;Zhengfa Liang;Zaiping Lin

  • Light Field Image Super-Resolution Using Deformable Convolution

    Yingqian Wang;Jungang Yang;Longguang Wang;Xinyi Ying

  • Axiom−based Grad−CAM: Towards Accurate Visualization and Explanation of CNNs

    Ruigang Fu;Qingyong Hu;Xiaohu Dong;Yulan Guo

  • Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling.

    Qingyong Hu;Bo Yang;Linhai Xie;Stefano Rosa

  • MTU-Net: Multilevel TransUNet for Space-Based Infrared Tiny Ship Detection

    Unknown

  • Semantic-Aware Domain Generalized Segmentation

    Unknown

  • Hyperspectral Image Restoration Using Low-Rank Tensor Recovery

    Haiyan Fan;Yunjin Chen;Yulan Guo;Hongyan Zhang

  • Spatial–Spectral Total Variation Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising

    Haiyan Fan;Chang Li;Yulan Guo;Gangyao Kuang

  • An Accurate and Robust Range Image Registration Algorithm for 3D Object Modeling

    Yulan Guo;Ferdous Ahmed Sohel;Mohammed Bennamoun;Jianwei Wan

  • Learning Multi-View Representation With LSTM for 3-D Shape Recognition and Retrieval

    Chao Ma;Yulan Guo;Jungang Yang;Wei An

  • An efficient 3D face recognition approach using local geometrical signatures

    Yinjie Lei;Mohammed Bennamoun;Munawar Hayat;Yulan Guo

Frequent Co-Authors

Wei An
Wei An National University of Defense Technology
Mohammed Bennamoun
Mohammed Bennamoun University of Western Australia
Ferdous Sohel
Ferdous Sohel Murdoch University
Jonathan Li
Jonathan Li University of Waterloo
Cheng Wang
Cheng Wang Xiamen University
Andrew Markham
Andrew Markham University of Oxford
Deke Guo
Deke Guo Sun Yat-sen University
Niki Trigoni
Niki Trigoni University of Oxford
Yao Zhao
Yao Zhao Beijing Jiaotong University
Wei Chen
Wei Chen National University of Singapore

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