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D-Index & Metrics

Computer Science

D-Index
65
Citations
20266
World Ranking
2428
National Ranking
103

Overview

Federico Tombari is affiliated with the Technical University of Munich in Germany. Their research primarily spans the fields of Computer Science and Engineering, with a significant focus on Computer Vision and Pattern Recognition, Artificial Intelligence, and Computational Mechanics.

The scientist's work covers diverse topics including 3D Shape Modeling and Analysis, Advanced Vision and Imaging, Robotics and Sensor-Based Localization, Multimodal Machine Learning Applications, Advanced Neural Network Applications, Computer Graphics and Visualization Techniques, and Human Pose and Action Recognition.

Frequent publication venues for their work include:

  • arXiv (Cornell University)
  • IEEE Robotics and Automation Letters
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • International Journal of Computer Vision
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Selected recent papers authored or co-authored by Federico Tombari include:

  • "Neural Fields in Visual Computing and Beyond", 2022, Computer Graphics Forum
  • "Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language", 2022, arXiv (Cornell University)
  • "SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation", 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation", 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation", 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

The scientist has collaborated frequently with other researchers, including Nassir Navab, Fabian Manhardt, Luc Van Gool, Benjamin Busam, and Xiangyang Ji.

Federico Tombari has contributed scholarly books published by Springer Science+Business Media, including the title "Image Analysis and Processing - ICIAP 2022".

Best Publications

  • Deeper Depth Prediction with Fully Convolutional Residual Networks

    Iro Laina;Christian Rupprecht;Vasileios Belagiannis;Federico Tombari

  • Unique signatures of histograms for local surface description

    Federico Tombari;Samuele Salti;Luigi Di Stefano

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

    Wadim Kehl;Fabian Manhardt;Federico Tombari;Slobodan Ilic

  • SHOT: Unique signatures of histograms for surface and texture description

    Samuele Salti;Federico Tombari;Luigi Di Stefano

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

    Keisuke Tateno;Federico Tombari;Iro Laina;Nassir Navab

  • Neural Fields in Visual Computing and Beyond

    Unknown

  • Tutorial: Point Cloud Library: Three-Dimensional Object Recognition and 6 DOF Pose Estimation

    A. Aldoma;Z. Marton;F. Tombari;W. Wohlkinger

  • BOP: Benchmark for 6D Object Pose Estimation

    Tomas Hodan;Frank Michel;Eric Brachmann;Wadim Kehl

  • Performance Evaluation of 3D Keypoint Detectors

    Federico Tombari;Samuele Salti;Luigi Di Stefano

  • GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation

    Gu Wang;Fabian Manhardt;Federico Tombari;Xiangyang Ji

  • A combined texture-shape descriptor for enhanced 3D feature matching

    Federico Tombari;Samuele Salti;Luigi Di Stefano

  • Unique shape context for 3d data description

    Federico Tombari;Samuele Salti;Luigi Di Stefano

  • 3D Point Capsule Networks

    Yongheng Zhao;Tolga Birdal;Haowen Deng;Federico Tombari

  • Registration with the Point Cloud Library: A Modular Framework for Aligning in 3-D

    Dirk Holz;Alexandru E. Ichim;Federico Tombari;Radu B. Rusu

  • Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation

    Wadim Kehl;Fausto Milletari;Federico Tombari;Federico Tombari;Slobodan Ilic;Slobodan Ilic

  • Segmentation-based adaptive support for accurate stereo correspondence

    Federico Tombari;Stefano Mattoccia;Luigi Di Stefano

  • ZNCC-based template matching using bounded partial correlation

    Luigi Di Stefano;Stefano Mattoccia;Federico Tombari

  • Classification and evaluation of cost aggregation methods for stereo correspondence

    F. Tombari;S. Mattoccia;L. Di Stefano;E. Addimanda

  • Object Recognition in 3D Scenes with Occlusions and Clutter by Hough Voting

    Federico Tombari;Luigi Di Stefano

  • A global hypotheses verification method for 3d object recognition

    Aitor Aldoma;Federico Tombari;Luigi Di Stefano;Markus Vincze

  • OUR-CVFH – Oriented, Unique and Repeatable Clustered Viewpoint Feature Histogram for Object Recognition and 6DOF Pose Estimation

    Aitor Aldoma;Federico Tombari;Radu Bogdan Rusu;Markus Vincze

Frequent Co-Authors

Nassir Navab
Nassir Navab Technical University of Munich
Luigi Di Stefano
Luigi Di Stefano University of Bologna
Stefano Mattoccia
Stefano Mattoccia University of Bologna
Slobodan Ilic
Slobodan Ilic Technical University of Munich
Gregory D. Hager
Gregory D. Hager Johns Hopkins University
Radu Bogdan Rusu
Radu Bogdan Rusu Fyusion, Inc
Soheyl Noachtar
Soheyl Noachtar Ludwig-Maximilians-Universität München
Luca Benini
Luca Benini ETH Zurich
Michele Magno
Michele Magno ETH Zurich
Davide Brunelli
Davide Brunelli University of Trento

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