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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| Vydáno v: | IEEE access Ročník 8; s. 1 |
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01.01.2020
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Feng, David Dagan Karambakhsh, Ahmad Sheng, Bin Li, Ping Jung, Younhyun Yang, Po |
| Author_xml | – sequence: 1 givenname: Ahmad surname: Karambakhsh fullname: Karambakhsh, Ahmad organization: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China – sequence: 2 givenname: Bin surname: Sheng fullname: Sheng, Bin organization: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. (e-mail: shengbin@sjtu.edu.cn) – sequence: 3 givenname: Ping surname: Li fullname: Li, Ping organization: Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong – sequence: 4 givenname: Po surname: Yang fullname: Yang, Po organization: Department of Computer Science, University of Sheffield, Sheffield S1 4DP, U.K – sequence: 5 givenname: Younhyun surname: Jung fullname: Jung, Younhyun organization: Department of Software, Gachon University, Seongnam, Republic of Korea, and The University of Sydney, Sydney NSW 2006, Australia – sequence: 6 givenname: David Dagan surname: Feng fullname: Feng, David Dagan organization: Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, The University of Sydney, Sydney NSW 2006, Australia |
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| SubjectTerms | 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 |
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| Title | VoxRec: Hybrid Convolutional Neural Network for Active 3D Object Recognition |
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