3D ResNets for 3D Object Classification

  • Anastasia Ioannidou
  • Elisavet ChatzilariEmail author
  • Spiros Nikolopoulos
  • Ioannis Kompatsiaris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)


During the last few years, deeper and deeper networks have been constantly proposed for addressing computer vision tasks. Residual Networks (ResNets) are the latest advancement in the field of deep learning that led to remarkable results in several image recognition and detection tasks. In this work, we modify two variants of the original ResNets, i.e. Wide Residual Networks (WRNs) and Residual of Residual Networks (RoRs), to work on 3D data and investigate for the first time, to our knowledge, their performance in the task of 3D object classification. We use a dataset containing volumetric representations of 3D models so as to fully exploit the underlying 3D information and present evidence that ‘3D ResNets’ constitute a valuable tool for classifying objects on 3D data as well.


3D object classification 3D object recognition Deep learning Residual networks 



The research leading to these results has received funding from the European Union H2020 Horizon Programme (2014–2020) under grant agreement 665066, project DigiArt (The Internet Of Historical Things And Building New 3D Cultural Worlds).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anastasia Ioannidou
    • 1
  • Elisavet Chatzilari
    • 1
    Email author
  • Spiros Nikolopoulos
    • 1
  • Ioannis Kompatsiaris
    • 1
  1. 1.Information Technologies Institute, Centre for Research and Technology HellasThermiGreece

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