World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
14119
World Ranking
3418
National Ranking
205

Niki Trigoni 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 Niki Trigoni 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: 212 publications — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Niki Trigoni 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 Niki Trigoni 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: 59 D-Index — 77th percentile

77% of scientists in this discipline score the same or lower.

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

Overview

Niki Trigoni is affiliated with the University of Oxford in the United Kingdom. Their research focuses primarily on computer science and engineering, with a significant number of publications in these areas. The scientist has a substantial presence in several subfields, notably computer vision and pattern recognition, aerospace engineering, electrical and electronic engineering, signal processing, and computational mechanics.

Their work spans multiple specialized 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 human pose and action recognition.

Niki Trigoni has contributed to several research publications, with frequent authorship in venues such as arXiv (Cornell University), Zenodo (CERN European Organization for Nuclear Research), the Proceedings of the AAAI Conference on Artificial Intelligence, IEEE Transactions on Neural Networks and Learning Systems, and the IEEE Sensors Journal.

Some of the recent papers authored or coauthored by Niki Trigoni include:

  • Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • AtLoc: Attention Guided Camera Localization, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Deep-Learning-Based Pedestrian Inertial Navigation: Methods, Data Set, and On-Device Inference, 2020, IEEE Internet of Things Journal
  • Milli-RIO: Ego-Motion Estimation With Low-Cost Millimetre-Wave Radar, 2020, IEEE Sensors Journal
  • Deep learning-based robust positioning for all-weather autonomous driving, 2022, Nature Machine Intelligence

Collaboration is a notable aspect of Niki Trigoni's research activity, with frequent coauthors including Andrew Markham, Chris Xiaoxuan Lu, Changhao Chen, Peijun Zhao, and Zhuangzhuang Dai.

Best Publications

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

    Qingyong Hu;Bo Yang;Linhai Xie;Stefano Rosa

  • DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks

    Sen Wang;Ronald Clark;Hongkai Wen;Niki Trigoni

  • Supporting Search and Rescue Operations with UAVs

    Sonia Waharte;Niki Trigoni

  • 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

  • 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

  • Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model

    Shuyu Lin;Ronald Clark;Robert Birke;Sandro Schonborn

  • mID: Tracking and Identifying People with Millimeter Wave Radar

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

  • End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks:

    Sen Wang;Sen Wang;Ronald Clark;Hongkai Wen;Niki Trigoni

  • Delay-bounded routing in vehicular ad-hoc networks

    Antonios Skordylis;Niki Trigoni

  • 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

  • Multi-query optimization for sensor networks

    Niki Trigoni;Yong Yao;Alan Demers;Johannes Gehrke

  • 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

  • The Cougar Project: a work-in-progress report

    Alan Demers;Johannes Gehrke;Rajmohan Rajaraman;Niki Trigoni

  • 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

Frequent Co-Authors

Andrew Markham
Andrew Markham University of Oxford
David W. Macdonald
David W. Macdonald University of Oxford
Phil Blunsom
Phil Blunsom University of Oxford
Rajmohan Rajaraman
Rajmohan Rajaraman Northeastern University
Johannes Gehrke
Johannes Gehrke Microsoft (United States)
Alan Demers
Alan Demers Cornell University
Stephen J. Roberts
Stephen J. Roberts University of Oxford
Cecilia Mascolo
Cecilia Mascolo University of Cambridge
Mark Levene
Mark Levene Birkbeck, University of London
Bhaskar Krishnamachari
Bhaskar Krishnamachari University of Southern California

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