3DSliceLeNet: Recognizing 3D Objects using a Slice-Representation

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Title: 3DSliceLeNet: Recognizing 3D Objects using a Slice-Representation
Authors: Gomez-Donoso, Francisco | Escalona, Félix | Orts-Escolano, Sergio | Garcia-Garcia, Alberto | Garcia-Rodriguez, Jose | Cazorla, Miguel
Research Group/s: Robótica y Visión Tridimensional (RoViT) | Arquitecturas Inteligentes Aplicadas (AIA)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial | Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Deep Learning | 3D Object Recognition | Convolutional Neural Networks | Caffe
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial | Arquitectura y Tecnología de Computadores
Issue Date: 1-Feb-2022
Publisher: IEEE
Citation: IEEE Access. 2022, 10: 15378-15392. https://doi.org/10.1109/ACCESS.2022.3148387
Abstract: 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.
Sponsor: 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
Language: eng
Type: info:eu-repo/semantics/article
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Peer Review: si
Publisher version: https://doi.org/10.1109/ACCESS.2022.3148387
Appears in Collections:INV - AIA - Artículos de Revistas
INV - RoViT - Artículos de Revistas

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