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Davide Boscaini

Researcher
  • Phone: +39 0461 314525
  • FBK Povo
Short bio

Davide Boscaini was born in Verona (Italy) in 1988. He received a B.S. degree in Applied Mathematics in 2010 and a M.S. degree in Mathematics in 2012, both from the University of Verona (Italy). His master thesis on "Spectral Methods for Shape Analysis" was developed during an intership at the Vision, Image, Processing and Sound Lab on topics related with the analysis of 3D deformable objects. The desire to deepen his knowledge in this topic led Davide to start a Ph.D. at the University of Lugano (Switzerland) under the supervision of prof. Michael M. Bronstein. During the Ph.D. his research focussed on merging spectral approaches and deep learning techniques to develop novel algorithms for 3D Shape Analysis. Davide's efforts contributed to the birth of a new research field, called Geometric Deep Learning, aiming at extending deep learning techniques to geometric domains such as 3D shapes and graphs. He received his Ph.D. degree in Computer Science in 2017 with a dissertation on "Geometric Deep Learning for Shape Analysis". He is currently a researcher at the Technologies of Vision research unit of the Fondazione Bruno Kessler in Trento (Italy).

Research interests
3D shape analysis geometric deep learning
Related projects
Publications
  1. Boscaini, Davide; Poiesi, Fabio; Messelodi, Stefano; Younes, Ayman; Grande, Donato A.,
    Pattern Recognition. ICPR International Workshops and Challenges, Part IV,
    vol.12664,
    2021
    , pp. 692-
    704
    , (ICPR 2021: Pattern Recognition. ICPR International Workshops and Challenges,
    Virtual,
    January 10–15, 2021)
  2. Lakhal, Mohamed Ilyes; Boscaini, Davide; Poiesi, Fabio; Lanz, Oswald; Cavallaro, Andrea,
    Proceedings of the Asian Conference on Computer Vision,
    2020
    , (Asian Conference on Computer Vision (ACCV),
    Virtual Tokyo,
    Nov. 30 - Dec. 04)
  3. Svoboda, J.; Astolfi, P.; Boscaini, D.; Masci, J.; Bronstein, M.,
    Proceedings of 2020 IEEE International Joint Conference on Biometrics (IJCB),
    2020
    , pp. 1-
    9
    , (2020 IEEE International Joint Conference on Biometrics (IJCB),
    Houston, TX, USA,
    28 Sept.-1 Oct. 2020)
  4. Astolfi, P.; Verhagen, R.; Petit, L.; Olivetti, E.; Masci, J.; Boscaini, D.; Avesani, P.,
    Proceedings of Medical Image Computing and Computer Assisted Intervention – MICCAI 2020,
    vol.12267,
    2020
    , pp. 291-
    301
    , (International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020,
    Lima, Peru,
    4-8 October 2020)
  5. Poiesi, Fabio; Boscaini, Davide,
    Distinctive 3D local deep descriptors,
    IEEE International Conference on Pattern Recognition,
    2020
  6. Alliegro, Antonio; Boscaini, Davide; Tommasi, Tatiana,
    Proceedings of the European Conference on Computer Vision Workshops (ECCV 2020),
    vol.12535,
    2020
    , pp. 704-
    708
    , (ECCV 2020: Computer Vision – ECCV 2020 Workshops,
    Glasgow, United Kingdom,
    23-28 August 2020)
  7. Alliegro, Antonio; Boscaini, Davide; Tommasi, Tatiana,
    Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges,
    Proceedings of the 25th International Conference on Pattern Recognition,
    2020
  8. Osterno Vasconcelos, Levi; Mancini, Massimiliano; Boscaini, Davide; Rota Bulò, Samuel; Caputo, Barbara; Ricci, Elisa,
    Shape Consistent 2D Keypoint Estimation under Domain Shift,
    Proceedings of the 25th International Conference on Pattern Recognition,
    2020
  9. Gainza, P.; Sverrisson, F.; Monti, F.; Rodolà, E.; Boscaini, D.; Bronstein, M. M.; Correia, B. E.,
    in «NATURE METHODS»,
    2019
  10. Osterno Vasconcelos, Levi; Mancini, Massimiliano; Boscaini, Davide; Caputo, Barbara; Ricci, Elisa,
    Proceedings of 2019 International Conference on 3D Vision (3DV),
    2019
    , pp. 57-
    66
    , (2019 International Conference on 3D Vision (3DV),
    Québec City, QC, Canada,
    16-19 September 2019)

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