World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
57
Citations
12088
World Ranking
3867
National Ranking
235

Andrew Markham 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 Andrew Markham 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: 192 publications — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Andrew Markham 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 Andrew Markham 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: 57 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

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

Overview

Andrew Markham is affiliated with the University of Oxford in the United Kingdom. Their research spans fields primarily within Computer Science and Engineering, focusing on subfields such as Computer Vision and Pattern Recognition, Aerospace Engineering, Signal Processing, Electrical and Electronic Engineering, and Computational Mechanics.

Their body of work encompasses numerous topics, including Robotics and Sensor-Based Localization, Advanced Vision and Imaging, Indoor and Outdoor Localization Technologies, 3D Shape Modeling and Analysis, 3D Surveying and Cultural Heritage, Advanced Image and Video Retrieval Techniques, and Speech and Audio Processing.

Some of Markham's recent publications include the following papers:

  • "Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations," 2023, published in Advances in Computational Mathematics
  • "Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling," 2021, published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "AtLoc: Attention Guided Camera Localization," 2020, published in Proceedings of the AAAI Conference on Artificial Intelligence
  • "Deep-Learning-Based Pedestrian Inertial Navigation: Methods, Data Set, and On-Device Inference," 2020, published in IEEE Internet of Things Journal
  • "Milli-RIO: Ego-Motion Estimation With Low-Cost Millimetre-Wave Radar," 2020, published in IEEE Sensors Journal

Markham frequently collaborates with other researchers. Frequent co-authors include:

  • Niki Trigoni
  • Chris Xiaoxuan Lu
  • Changhao Chen
  • Peijun Zhao
  • Qingyong Hu

Their publications are often found in key academic venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Neural Networks and Learning Systems
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Sensors Journal
  • SSRN Electronic Journal

Best Publications

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

    Qingyong Hu;Bo Yang;Linhai Xie;Stefano Rosa

  • Visual SLAM and Structure from Motion in Dynamic Environments: A Survey

    Muhamad Risqi U. Saputra;Andrew Markham;Niki Trigoni

  • VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem

    Ronald Clark;Sen Wang;Hongkai Wen;Andrew Markham

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

    Sheng Ao;Qingyong Hu;Bo Yang;Andrew Markham

  • IONet: Learning to Cure the Curse of Drift in Inertial Odometry

    Changhao Chen;Xiaoxuan Lu;Andrew Markham;Niki Trigoni

  • VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization

    Ronald Clark;Sen Wang;Andrew Markham;Niki Trigoni

  • Non-Line-of-Sight Identification and Mitigation Using Received Signal Strength

    Zhuoling Xiao;Hongkai Wen;Andrew Markham;Niki Trigoni

  • Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

    Bo Yang;Jianan Wang;Ronald Clark;Qingyong Hu

  • Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations.

    Ben Moseley;Andrew Markham;Tarje Nissen-Meyer

  • mID: Tracking and Identifying People with Millimeter Wave Radar

    Peijun Zhao;Chris Xiaoxuan Lu;Jianan Wang;Changhao Chen

  • Evolution and sustainability of a wildlife monitoring sensor network

    Vladimir Dyo;Stephen A. Ellwood;David W. Macdonald;Andrew Markham

  • GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks

    Yasin Almalioglu;Muhamad Risqi U. Saputra;Pedro P. B. de Gusmao;Andrew Markham

  • Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

    Qingyong Hu;Bo Yang;Sheikh Khalid;Wen Xiao

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

    Qingyong Hu;Bo Yang;Linhai Xie;Stefano Rosa

  • 3D Object Reconstruction from a Single Depth View with Adversarial Learning

    Bo Yang;Hongkai Wen;Sen Wang;Ronald Clark

  • Does BTLE measure up against WiFi? A comparison of indoor location performance

    Xiaojie Zhao;Zhuoling Xiao;Andrew Markham;Niki Trigoni

  • SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000x Fewer Labels

    Unknown

  • Lightweight map matching for indoor localisation using conditional random fields

    Zhuoling Xiao;Hongkai Wen;Andrew Markham;Niki Trigoni

  • Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning

    Linhai Xie;Sen Wang;Andrew Markham;Niki Trigoni

  • AtLoc: Attention Guided Camera Localization

    Bing Wang;Changhao Chen;Chris Xiaoxuan Lu;Peijun Zhao

  • See through smoke: robust indoor mapping with low-cost mmWave radar

    Chris Xiaoxuan Lu;Stefano Rosa;Peijun Zhao;Bing Wang

  • A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence

    Changhao Chen;Bing Wang;Chris Xiaoxuan Lu;Niki Trigoni

Frequent Co-Authors

Niki Trigoni
Niki Trigoni University of Oxford
David W. Macdonald
David W. Macdonald University of Oxford
Yulan Guo
Yulan Guo Sun Yat-sen University
Phil Blunsom
Phil Blunsom University of Oxford
Andrew J. Loveridge
Andrew J. Loveridge University of Oxford
Stephen J. Roberts
Stephen J. Roberts University of Oxford
Cecilia Mascolo
Cecilia Mascolo University of Cambridge
John A. Stankovic
John A. Stankovic University of Virginia
Simon Chamaillé-Jammes
Simon Chamaillé-Jammes Centre national de la recherche scientifique, CNRS
Christina D. Buesching
Christina D. Buesching University of Oxford

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