World's Best Scientists 2026 revealed!
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Computer Science
Germany
2026

D-Index & Metrics

Computer Science

D-Index
114
Citations
71610
World Ranking
190
National Ranking
10

Research.com Recognitions

  • 2026 - Research.com Computer Science in Germany Leader Award
  • 2025 - Research.com Computer Science in Germany Leader Award
  • 2023 - Research.com Computer Science in Germany Leader Award
  • 2022 - Research.com Computer Science in Germany Leader Award

Overview

Nassir Navab is affiliated with the Technical University of Munich in Germany. Their research spans multiple interdisciplinary fields including Computer Science, Medicine, and Engineering.

The scientist has contributed extensively to subfields such as Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Biomedical Engineering, and Surgery.

Navab's work covers a variety of main topics, including:

  • Radiomics and Machine Learning in Medical Imaging
  • Surgical Simulation and Training
  • Augmented Reality Applications
  • Anatomy and Medical Technology
  • Medical Image Segmentation Techniques
  • Robotics and Sensor-Based Localization
  • Advanced Neural Network Applications

Recent publications by Navab include:

  • Applicability of augmented reality in orthopedic surgery - A systematic review (2020), published in BMC Musculoskeletal Disorders
  • ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation (2022), presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation (2021), presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments (2020), published in IEEE Robotics and Automation Letters
  • GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting (2022), presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Frequent co-authors working with Navab include:

  • Federico Tombari
  • Benjamin Busam
  • Thomas Wendler
  • Shadi Albarqouni
  • Ulrich Eck

Navab's research output is widely published across various venues, predominantly in:

  • arXiv (Cornell University)
  • IEEE Robotics and Automation Letters
  • International Journal of Computer Assisted Radiology and Surgery
  • Lecture Notes in Computer Science
  • Medical Image Analysis

The scientist has authored contributions to book publications as well, including a title released by Springer Science+Business Media:

  • Information Processing in Medical Imaging (2023)

Best Publications

  • V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

    Fausto Milletari;Nassir Navab;Seyed-Ahmad Ahmadi

  • Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

    Babak Ehteshami Bejnordi;Mitko Veta;Paul Johannes van Diest;Bram van Ginneken

  • Deeper Depth Prediction with Fully Convolutional Residual Networks

    Iro Laina;Christian Rupprecht;Vasileios Belagiannis;Federico Tombari

  • Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015

    Nassir Navab;Joachim Hornegger;William M. Wells;Alejandro F. Frangi

  • Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes

    Stefan Hinterstoisser;Vincent Lepetit;Slobodan Ilic;Stefan Holzer

  • Model globally, match locally: Efficient and robust 3D object recognition

    Bertram Drost;Markus Ulrich;Nassir Navab;Slobodan Ilic

  • SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again

    Wadim Kehl;Fabian Manhardt;Federico Tombari;Slobodan Ilic

  • Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks

    Abhijit Guha Roy;Nassir Navab;Christian Wachinger

  • Medical Image Computing and Computer-Assisted Intervention -- Miccai 2010

    Tianzi Jiang;Nassir Navab;Josien P. W. Pluim;Max A. Viergever

  • Tissue Classification as a Potential Approach for Attenuation Correction in Whole-Body PET/MRI: Evaluation with PET/CT Data

    Axel Martinez-Möller;Michael Souvatzoglou;Gaspar Delso;Ralph A. Bundschuh

  • CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction

    Keisuke Tateno;Federico Tombari;Iro Laina;Nassir Navab

  • Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images

    Abhishek Vahadane;Tingying Peng;Amit Sethi;Shadi Albarqouni

  • Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes

    Stefan Hinterstoisser;Stefan Holzer;Cedric Cagniart;Slobodan Ilic

  • Gradient Response Maps for Real-Time Detection of Textureless Objects

    S. Hinterstoisser;C. Cagniart;S. Ilic;P. Sturm

  • AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images

    Shadi Albarqouni;Christoph Baur;Felix Achilles;Vasileios Belagiannis

  • ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks.

    Abhijit Guha Roy;Sailesh Conjeti;Sri Phani Krishna Karri;Debdoot Sheet

  • Dense image registration through MRFs and efficient linear programming.

    Ben Glocker;Nikos Komodakis;Nikos Komodakis;Georgios Tziritas;Nassir Navab

  • Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge

    K. Murphy;B. van Ginneken;J. M. Reinhardt;S. Kabus

  • Surgical data science for next-generation interventions.

    Lena Maier-Hein;Swaroop S. Vedula;Stefanie Speidel;Nassir Navab;Nassir Navab

  • Recalibrating Fully Convolutional Networks With Spatial and Channel “Squeeze and Excitation” Blocks

    Abhijit Guha Roy;Nassir Navab;Christian Wachinger

  • Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention

    Wolfgang Wein;Shelby Brunke;Ali Khamene;Matthew R. Callstrom

  • Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound

    Fausto Milletari;Seyed-Ahmad Ahmadi;Christine Kroll;Annika Plate

Frequent Co-Authors

Federico Tombari
Federico Tombari Technical University of Munich
Christian Wachinger
Christian Wachinger Technical University of Munich
Slobodan Ilic
Slobodan Ilic Technical University of Munich
Ben Glocker
Ben Glocker Imperial College London
Mehran Armand
Mehran Armand Johns Hopkins University
Russell H. Taylor
Russell H. Taylor Johns Hopkins University
Nikos Paragios
Nikos Paragios CentraleSupélec
Dorin Comaniciu
Dorin Comaniciu Siemens (United States)
Andreas Maier
Andreas Maier University of Erlangen-Nuremberg
Nikos Komodakis
Nikos Komodakis University of Crete

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