I received a PhD in Computer Science and Engineering from the University of Bologna in 2002 and currently I'm associate professor, with national scientific habilitation 09/H1 for the role of full professor, at the Department of Computer Science and Engineering, School of Engineering and Architecture, University of Bologna. My research interests include: computer vision, deep-learning, depth and scene flow estimation from monocular and stereo images, domain adaptation, and embedded computer vision [CV]


Short bio

Stefano Mattoccia

Associate Professor, PhD

Department of Computer Science and Engineering (DISI)
School of Engineering and Architecture
University of Bologna

Address: Viale del Risorgimento, 2  40136 Bologna, Italy

Phone:   +39 051 2093860  - Fax +29 051 2093869 

Email:     stefano.mattoccia#AT#unibo.it


Keywords: computer vision, machine-learning, 3D vision, depth and scene flow estimation from monocular and stereo images, domain adaptation, embedded computer vision

My research activity is concerned with computer vision, machine learning applied to computer vision problems and embedded vision systems. These slides (2021) outline some of these research topics. For a more detailed overview take a look at the publication page or Google Scholar. My Orcid ID is: https://orcid.org/0000-0002-3681-7704

My team: Matteo Poggi (Assistant professor), Fabio Tosi (Postdoc researcher), Filippo Aleotti (PhD student), Youmin Zhang (PhD student), Fan Rizhao (PhD student), Huan Li (PhD student 2021), Andrea Conti (research fellow/PhD student 2021), Alessio Mingozzi (research fellow). Visiting PhD students: Xin Qiao (Xi'an Jiaotong University), Chaoqiang Zhao (East China University of Science and Technology, Shanghai)


  1. -Sono disponbili tesi di laurea (magistrale e triennale) e tirocini nell’ambito di tematiche inerenti la computer vision con particolare riferimento alla generazione e alla elaborazione di dati 3D mediante machine learning.
    Le tematiche di ricerca vertono su: deep-learning/machine learning, computer vision e 3D vision, sistemi di computer vision embedded, sistemi assistivi per ipovedenti e non vedenti, robotica, sistemi di guida autonoma.

  2. -Sono disponibili tesi e tirocini da svolgere all’estero e/o in azienda

  3. -Per informazioni o per fissare un appuntamento: stefano.mattoccia__AT__unibo.it

  4. -Una lista di tesi e tirocini svolti e in corso è disponibile a questo link

Tesi di laurea e tirocini/Theses and internships

  1. -72943 Sistemi Digitali M, Ing. Informatica (Magistrale) 2019 -> 2021

  2. -28012 Calcolatori Elettronici T, Ing. Informatica  2009 -> 2021

  3. -88146 Fondamenti di Informatica P2, Ing. Meccatronica  2019 -> 2021

  4. -78101 Sistemi Embedded riconfigurabili M, Ing. Informatica (Magistrale) 2009 -> 2019

  5. -28011 Reti Logiche T, Ing. Informatica 2016-2018, Ing. Automazione 2006-2008, Ing. Elettronica 2003 -> 2005


Service and professional activities

  1. -Best paper award at 14th IEEE Embedded Vision Workshop (CVPR 2018)

  2. -Best demo paper award at 4th IEEE Mobile Vision Workshop (CVPR2014)

  3. -Outstanding reviewer, International Conference on Computer Vision and Pattern Recognition (CVPR 2013)

  4. -Best paper award at 7th IEEE Embedded Vision Workshop (CVPR 2011)


Recent talks, tutorials, courses, seminars and demos

Informativa sulla privacy disponibile a questo link

Workshop: Traditional Computer Vision in the Age of Deep Learning (TradiCV), ICCV 2021
Special issue:
Traditional Computer Vision in the Age of Deep Learning on Int. Journal of Computer Vision


  1. -1 paper accepted at ICCV 2021

  2. -1 paper accepted on IEEE Internet of Things Journal (2021)

  3. -3 papers accepted on IEEE Trans. on Pattern Analysis and Machine Intelligence (2021)

  4. -1 paper accepted on IEEE Trans. on Circuits and Systems for Video Technology (2021)

  5. -1 paper accepted at CVPR 2021

  6. -2 papers accepted at ECCV 2020

  7. -2 papers accepted at CVPR 2020

  8. -1 paper accepted at ICRA 2020

Recent selected papers:
- Learning optical flow from still images, CVPR 2021

- Self-adapting confidence estimation for stereo, ECCV 2020

- Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation, ECCV 2020

- On the uncertainty of self-supervised monocular depth estimation, CVPR 2020

  1. -Distilled semantics for comprehensive scene understanding from videos, CVPR 2020

  2. -Real-Time Semantic Stereo Matching, ICRA 2020

- Learning end-to-end scene flow by distilling single tasks knowledge, AAAI 2020

  1. -Unsupervised diomain adaptation for depth prediction from mages, TPAMI

  2. -Real-time self-adaptive deep stereo, CVPR 2019

  3. -Guided stereo matching, CVPR 2019

- Learning monocular depth estimation infusing traditional stereo knowledge, CVPR 2019

- Beyond local reasoning for stereo confidence estimation with deep learning, ECCV 2018
- Towards real-time unsupervised monocular depth estimation on CPU, IROS 2018

Updates and recent results

  1. -Member of the advisory board of the PhD programme in Engineering and Information Technology for Structural and Environmental Monitoring and Risk Management - EIT4SEMM

  2. -General co-Chair 12th IEEE Embedded Vision Workshop (EVW2016), CVPR 2016 workshop

  3. -Program Chair 11th IEEE Embedded Vision Workshop (EVW2015), CVPR 2015 workshop

  4. -Program co-Chair 10th IEEE Embedded Vision Workshop (EVW2014), CVPR 2014 workshop

  5. -Area Chair IEEE International Conference on Multimedia & Expo ICME 2014

  6. -Area Chair IEEE International Conference on Multimedia & Expo ICME 2013

  7. -Guest Editor IEEE Journal of Selected Topics in Signal Processing, Special Issue on Emerging Techniques in 3D 2013

  8. -Guest Editor Springer’s Journal of Signal Processing Systems 2016

  9. -Reviewer for major international conferences and the following journals:

    IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, IEEE Transactions on Medical Images, IEEE Transactions on Circuits and Systems I, IEEE Transactions on Circuits and Systems - II, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on VLSI Systems, IEEE  Transactions on Neural Systems and Rehabilitation Engineering, IEEE Transactions on Emerging Topics in Computing, IEEE Signal Processing Letter, Computer Vision and Image Understanding, Image and Vision Computing, Machine Vision and Applications, Pattern Recognition Letters, Elsevier Journal of Visual Communication and Image Representation, Journal of Signal Processing Systems, Journal of Microprocessors and Microsystems, Digital Signal Processing, IET Computer Vision, IET Intelligent Transport Systems, VLSI Design, EURASIP Journal on Image and Video Processing, ISRN Machine Vision, SPIE Journal of Electronic Imaging, SPIE Optical Engineering, Optics Express, International Journal of Computational Vision and Robotics, Artificial Intelligence in Medicine, International Journal of Artificial Intelligence and Soft Computing, Artificial Intelligence Review, The Knowledge Engineering Review, Journal of Network and Computer Applications

  10. -Key member (2010-2014) of the Interest Group on 3D Rendering, Processing and Communications of  IEEE Multimedia Communication TC

  11. -Member of IEEE and CVPL (IAPR) 

  12. -Member of the following PhD dissertation committees:

        TU Graz (2018), University of Bologna (2018), University of Torino (2018), University of Padova (2017),
        University of Parma (2015)

  Youmin Zhang, University of Bologna/China Scholarship Council (2020-present)
      Depth sensing

  Fan Rizhao, University of Bologna /China Scholarship Council (2020-present)
      Depth sensing

  Filippo Aleotti, University of Bologna (2018-present)
      Unsupervised monocular depth estimation

  Fabio Tosi, University of Bologna (2017-present)
      Deep-learning for 3D reconstruction

      Matteo Poggi, University of Bologna (2014-2017)
      Machine learning for stereo vision

  Leonardo De-Maeztu Reinares, Public University of Navarre, Pamplona, Spain (2009-2012)
      Towards accurate and real-time local stereo correspondence algorithms: computational efficiency and massively
      parallel architectures
, Co-supervised with Arantxa Villanueva (Public University of Navarre)

  Federico Tombari, University of Bologna (2006-2009)
      Methodologies for visual correspondence, Co-supervised with Prof. Luigi Di Stefano (University of Bologna)

PhD students

Real-time single image depth perception in the wild with handheld devices: source code (iOS,Android) and web demo
  1. -Tutorial: Facing depth estimation in the wild with deep networks, M. Poggi, F. Tosi, F. Aleotti, K. Batsos, P. Mordohai,  S. Mattoccia, ECCV 2020, August 28, 2020

  2. - Tutorial: , Learning and understanding single image depth estimation in the wild, M. Poggi, F. Tosi, F. Aleotti, S. Mattoccia, C. Godard, J. Watson, M. Firman, G .J. Brostow, CVPR 2020, June 15, 2020

  3. -Course: Depth sensing technologies for autonomous vehicles, S. Mattoccia, Poggi, F. Tosi, F. Aleotti, Master in Sustainable and Integrated Mobility in Urban Regions, University of Bologna, Imola (Bologna), Oct/Nov 2019

  4. -Tutorial: Learning-based depth estimation from stereo and monocular images: successes, limitations and future challenges, M. Poggi, F. Tosi, K. Batsos, P. Mordohai, S. Mattoccia, CVPR 2019, Long Beach, USA, June 17, 2019

  5. -Demo: Real-time monocular depth estimation without GPU, M. Poggi, F. Tosi, F. Aleotti, S. Mattoccia, CVPR 2019, Long Beach, USA, June 18-20, 2019

  6. -Demo: Real-time self-adaptive deep stereo, A. Tonioni, F. Tosi, M. Poggi, S. Mattoccia. L. Di Stefano, CVPR 2019, Long Beach, USA, June 18-20, 2019

  7. -Course: Computer vision and machine learning,  EMBA | Bologna Business School, Bologna, May 17 2019

  8. -Demo: Energy-Efficient Monocular Depth Estimation on ARM-based Embedded Platforms, V. Peluso, A. Cipolletta, A. Calimera,  M. Poggi, F. Tosi, S. Mattoccia, U-booth at DATE 2019, March, 2019, Florence, Italy

  9. -Talk: Real-time depth from image with deep-learning, EON Experience Fest 2019, March 28, 2019, Bologna [PDF]

  10. -Course for PhD students: Learning-based dense depth estimation from stereo and monocular images, Matteo Poggi and Stefano Mattoccia, January/February 2019 [Abstract] [Website

  11. -Talk: Learning-based methods for depth from images and confidence estimation, Naver Labs Europe, Grenoble (France), December 14, 2018 [PDF]

  12. -Tutorial: Learning-based depth estimation from stereo and monocular images: successes, limitations and future challenges, M. Poggi, F. Tosi, K. Batsos, P. Mordohai, S. Mattoccia 3DV 2018, Verona, Italy, Sept. 8, 2018 - Slides available here.

  13. -Demo:Towards Real-time Learning of Monocular Depth Estimation Enabling Multiple View Synthesis on CPU, M. Poggi, F. Tosi, S. Mattoccia European Conference on Computer Vision (ECCV2018), Sept. 10, 2018 Munich, Germany

  14. -Talk: 3D Sensing from Images, with Matteo Poggi and Fabio Tosi, European Machine Vision Forum 2018, Bologna, Italy, September 6, 2018 [Abstract] [PDF]

  15. -Demo: Towards real-time monocular and unsupervised depth estimation on CPU, wM. Poggi, F. Aleotti and F. Tosi, International Conference on 3D Vision (3DV2018), Sept. 5-7, 2018, Verona, Italy

  16. -Talk: Deep-learning for depth estimation, TU Graz, Graz University of Technology - Institute of Computer Graphics & Vision, April 17, 2018

  17. -Talks: Deep-learning for low level vision problems and Mapping of computer vision algorithms on FPGAs with High Level Synthesis tools, Summer School on Deep Learning on Chip (Macloc2017) September 20-22, 2017 – Politecnico di Torino, Torino (Italy) [PDF]