التفاصيل البيبلوغرافية
| العنوان: |
3DSliceLeNet: Recognizing 3D Objects Using a Slice-Representation |
| المؤلفون: |
Francisco Gomez-Donoso, Felix Escalona, Sergio Orts-Escolano, Alberto Garcia-Garcia, Jose Garcia-Rodriguez, Miguel Cazorla |
| المصدر: |
IEEE Access, Vol 10, Pp 15378-15392 (2022) |
| بيانات النشر: |
IEEE, 2022. |
| سنة النشر: |
2022 |
| المجموعة: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: |
Deep learning, 3D object recognition, convolutional neural networks, Caffe, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: |
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 their high success rate. Although a number of approaches currently 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. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/9701312/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3148387 |
| URL الوصول: |
https://doaj.org/article/67f6038836ba414a9a35ba1271b03986 |
| رقم الانضمام: |
edsdoj.67f6038836ba414a9a35ba1271b03986 |
| قاعدة البيانات: |
Directory of Open Access Journals |
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