World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
20446
World Ranking
11862
National Ranking
585

Björn Ommer 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 Björn Ommer 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: 172 publications — 35th percentile

35% of scientists in this discipline score the same or lower.

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

Björn Ommer 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 Björn Ommer 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: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Overview

Björn Ommer is affiliated with Ludwig-Maximilians-Universität München in Germany. Their primary field of study is Computer Science, with a focus on several subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Graphics and Computer-Aided Design, Radiology, Nuclear Medicine and Imaging, and Computational Mechanics.

The main research topics covered by Ommer span Generative Adversarial Networks and Image Synthesis, Computer Graphics and Visualization Techniques, Advanced Vision and Imaging, Domain Adaptation and Few-Shot Learning, Advanced Image Processing Techniques, Multimodal Machine Learning Applications, and Medical Imaging Techniques and Applications.

Their recent published papers include:

  • High-Resolution Image Synthesis with Latent Diffusion Models, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • High-Resolution Image Synthesis with Latent Diffusion Models, 2021, arXiv (Cornell University)
  • Neuropathic pain caused by miswiring and abnormal end organ targeting, 2022, Nature
  • SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • State of the Art on Diffusion Models for Visual Computing, 2024, Computer Graphics Forum

The scientist has frequently published in the following venues:

  • arXiv (Cornell University)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • PLoS ONE
  • Medical Physics

Ommer has collaborated regularly with several co-authors, including:

  • Vincent Tao Hu
  • Robin Rombach
  • Patrick Esser
  • Pingchuan Ma
  • Stefan Andreas Baumann

Best Publications

  • High-Resolution Image Synthesis with Latent Diffusion Models

    Unknown

  • Taming Transformers for High-Resolution Image Synthesis

    Patrick Esser;Robin Rombach;Bjorn Ommer

  • High-Resolution Image Synthesis with Latent Diffusion Models

    Unknown

  • A Style-Aware Content Loss for Real-Time HD Style Transfer

    Artsiom Sanakoyeu;Dmytro Kotovenko;Sabine Lang;Björn Ommer

  • A Variational U-Net for Conditional Appearance and Shape Generation

    Patrick Esser;Ekaterina Sutter;Björn Ommer

  • Video parsing for abnormality detection

    Borislav Antic;Bjorn Ommer

  • Content and Style Disentanglement for Artistic Style Transfer

    Dmytro Kotovenko;Artsiom Sanakoyeu;Sabine Lang;Bjorn Ommer

  • Unsupervised Part-Based Disentangling of Object Shape and Appearance

    Dominik Lorenz;Leonard Bereska;Timo Milbich;Bjorn Ommer

  • Learning Multi-Scale Photo Exposure Correction

    Mahmoud Afifi;Konstantinos G. Derpanis;Bjorn Ommer;Michael S. Brown

  • Improving Spatiotemporal Self-supervision by Deep Reinforcement Learning

    Uta Büchler;Biagio Brattoli;Björn Ommer

  • Divide and Conquer the Embedding Space for Metric Learning

    Artsiom Sanakoyeu;Vadim Tschernezki;Uta Buchler;Bjorn Ommer

  • Taming Transformers for High-Resolution Image Synthesis

    Patrick Esser;Robin Rombach;Björn Ommer

  • Revisiting Training Strategies and Generalization Performance in Deep Metric Learning

    Karsten Roth;Timo Milbich;Samrath Sinha;Prateek Gupta

  • Multi-scale object detection by clustering lines

    Bjorn Ommer;Jitendra Malik

  • Learning the Compositional Nature of Visual Object Categories for Recognition

    B. Ommer;J.M. Buhmann

  • Deep Semantic Feature Matching

    Nikolai Ufer;Bjorn Ommer

  • MIC: Mining Interclass Characteristics for Improved Metric Learning

    Biagio Brattoli;Karsten Roth;Bjorn Ommer

  • CliqueCNN: Deep Unsupervised Exemplar Learning

    Miguel Ángel Bautista;Artsiom Sanakoyeu;Ekaterina Tikhoncheva;Björn Ommer

  • A Content Transformation Block for Image Style Transfer

    Dmytro Kotovenko;Artsiom Sanakoyeu;Pingchuan Ma;Sabine Lang

  • Cross and Learn: Cross-Modal Self-supervision

    Nawid Sayed;Biagio Brattoli;Björn Ommer

  • Learning the Compositional Nature of Visual Objects

    B. Ommer;J.M. Buhmann

  • State of the Art on Diffusion Models for Visual Computing

    Unknown

  • Retrieval-Augmented Diffusion Models

    Unknown

  • SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation

    Stefan Andreas Baur;David Josef Emmerichs;Frank Moosmann;Peter Pinggera

  • A Disentangling Invertible Interpretation Network for Explaining Latent Representations

    Patrick Esser;Robin Rombach;Bjorn Ommer

  • Voting by grouping dependent parts

    Pradeep Yarlagadda;Antonio Monroy;Björn Ommer

  • MIC: Mining Interclass Characteristics for Improved Metric Learning

    Karsten Roth;Biagio Brattoli;Björn Ommer

Frequent Co-Authors

Martin E. Schwab
Martin E. Schwab University of Zurich
Fritjof Helmchen
Fritjof Helmchen University of Zurich
Yoshua Bengio
Yoshua Bengio University of Montreal
Michael S. Brown
Michael S. Brown York University
Volker Roth
Volker Roth University of Basel
Majid Mirmehdi
Majid Mirmehdi University of Bristol
Fred A. Hamprecht
Fred A. Hamprecht Heidelberg University
Jitendra Malik
Jitendra Malik University of California, Berkeley

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