3D Object Classification Using a Volumetric Deep Neural Network: An Efficient Octree Guided Auxiliary Learning Approach

3D Object Classification Using a Volumetric Deep Neural Network: An Efficient Octree Guided Auxiliary Learning Approach

We consider the recent challenges of 3D shape analysis based on a volumetric CNN that requires a huge computational power. This high-cost approach forces to reduce the volume resolutions when applying 3D CNN on volumetric data. In this context, we propose a multiorientation volumetric deep neural ne...

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Bibliographic Details
Main Authors: A. A. M. Muzahid, Wanggen Wan, Ferdous Sohel, Naimat Ullah Khan, Ofelia Delfina Cervantes Villagomez, Hidayat Ullah
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
3D shape analysis
object classification
convolutional neural network
DNNs
volumetric CNN
Online Access:https://ieeexplore.ieee.org/document/8964382/

Internet

https://ieeexplore.ieee.org/document/8964382/

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