NurbsNet: A Nurbs approach for 3d object recognition

Felix Escalona, Diego Viejo, Robert B. Fisher, Miguel Cazorla

Research output: Chapter in Book/Report/Conference proceedingConference contribution


With the emergence of low cost 3D sensors, the focus is moving towards the recognition and scene understanding of tridimensional data. This kind of representation is really challenging in terms of computation, and it needs the development of new strategies and algorithms to be handled and interpreted.

In this work, we propose NurbsNet, a novel approach for 3D object classification based on local similarities with free form surfaces modeled as Nurbs.

The proposal has been tested in ModelNet10 and ModelNet40 with results that are promising with less training iterations than state-of-the-art methods and very low memory consumption.
Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks (IJCNN)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Electronic)978-1-7281-6926-2
ISBN (Print)978-1-7281-6927-9
Publication statusPublished - 28 Sep 2020
EventThe International Joint Conference on Neural Networks 2020 - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

ISSN (Print)2161-4393
ISSN (Electronic)2161-4407


ConferenceThe International Joint Conference on Neural Networks 2020
Abbreviated titleIJCNN 2020
CountryUnited Kingdom
Internet address


  • 3d object recognition
  • neural networks
  • Nurbs

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