Abstract
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.
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 language | English |
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Title of host publication | 2020 International Joint Conference on Neural Networks (IJCNN) |
Publisher | Institute of Electrical and Electronics Engineers |
Number of pages | 7 |
ISBN (Electronic) | 978-1-7281-6926-2 |
ISBN (Print) | 978-1-7281-6927-9 |
DOIs | |
Publication status | Published - 28 Sept 2020 |
Event | The International Joint Conference on Neural Networks 2020 - Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020 https://wcci2020.org/ |
Publication series
Name | |
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Publisher | IEEE |
ISSN (Print) | 2161-4393 |
ISSN (Electronic) | 2161-4407 |
Conference
Conference | The International Joint Conference on Neural Networks 2020 |
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Abbreviated title | IJCNN 2020 |
Country/Territory | United Kingdom |
City | Glasgow |
Period | 19/07/20 → 24/07/20 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- 3d object recognition
- neural networks
- Nurbs