VoxRec: Hybrid Convolutional Neural Network for Active 3D Object Recognition

Deep Neural Network methods have been used to a variety of challenges in automatic 3D recognition. Although discovered techniques provide many advantages in comparison with conventional methods, they still suffer from different drawbacks, e.g., a large number of pre-processing stages and time-consum...

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Bibliographic Details
Published in:IEEE access Vol. 8; p. 1
Main Authors: Karambakhsh, Ahmad, Sheng, Bin, Li, Ping, Yang, Po, Jung, Younhyun, Feng, David Dagan
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Convolutional neural networks
Feature extraction
multi-layer neural network
Neural networks
Object recognition
Octree coding
Octrees
recurrent neural networks
Segmentation
Shape
Solid modeling
Three dimensional models
Three-dimensional displays
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Deep Neural Network methods have been used to a variety of challenges in automatic 3D recognition. Although discovered techniques provide many advantages in comparison with conventional methods, they still suffer from different drawbacks, e.g., a large number of pre-processing stages and time-consuming training. In this paper, an innovative approach has been suggested for recognizing 3D models. It contains encoding 3D point clouds, surface normal, and surface curvature, merge them to provide more effective input data, and train it via a deep convolutional neural network on Shapenetcore dataset. We also proposed a similar method for 3D segmentation using Octree coding method. Finally, comparing the accuracy with some of the state-of-the-art demonstrates the effectiveness of our proposed method.
Bibliography:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2987177