3DSliceLeNet: Recognizing 3D Objects using a Slice-Representation
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http://hdl.handle.net/10045/121559
Títol: | 3DSliceLeNet: Recognizing 3D Objects using a Slice-Representation |
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Autors: | Gomez-Donoso, Francisco | Escalona, Félix | Orts-Escolano, Sergio | Garcia-Garcia, Alberto | Garcia-Rodriguez, Jose | Cazorla, Miguel |
Grups d'investigació o GITE: | Robótica y Visión Tridimensional (RoViT) | Arquitecturas Inteligentes Aplicadas (AIA) |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Paraules clau: | Deep Learning | 3D Object Recognition | Convolutional Neural Networks | Caffe |
Àrees de coneixement: | Ciencia de la Computación e Inteligencia Artificial | Arquitectura y Tecnología de Computadores |
Data de publicació: | 1-de febrer-2022 |
Editor: | IEEE |
Citació bibliogràfica: | IEEE Access. 2022, 10: 15378-15392. https://doi.org/10.1109/ACCESS.2022.3148387 |
Resum: | Convolutional Neural Networks (CNNs) have become the default paradigm for addressing classification problems, especially, but not only, in image recognition. This is mainly due to the high success rate they provide. Although there are currently approaches that apply deep learning to the 3D shape recognition problem, they are either too slow for online use or too error-prone. To fill this gap, we propose 3DSliceLeNet, a deep learning architecture for point cloud classification. Our proposal converts the input point clouds into a two-dimensional representation by performing a slicing process and projecting the points to the principal planes, thus generating images that are used by the convolutional architecture. 3DSliceLeNet successfully achieves both high accuracy and low computational cost. A dense set of experiments has been conducted to validate our system under the ModelNet challenge, a large-scale 3D Computer Aided Design (CAD) model dataset. Our proposal achieves a success rate of 94.37% and an Area Under Curve (AUC) of 0.978 on the ModelNet-10 classification task. |
Patrocinadors: | Grant PID2019-104818RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. |
URI: | http://hdl.handle.net/10045/121559 |
ISSN: | 2169-3536 |
DOI: | 10.1109/ACCESS.2022.3148387 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/article |
Drets: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
Revisió científica: | si |
Versió de l'editor: | https://doi.org/10.1109/ACCESS.2022.3148387 |
Apareix a la col·lecció: | INV - AIA - Artículos de Revistas INV - RoViT - Artículos de Revistas |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
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Gomez-Donoso_etal_2022_IEEEAccess.pdf | 2,87 MB | Adobe PDF | Obrir Vista prèvia | |
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