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http://hdl.handle.net/10397/87779
Title: | VoxRec : hybrid convolutional neural network for active 3D object recognition | Authors: | Karambakhsh, A Sheng, B Li, P Yang, P Jung, Y Feng, DD |
Issue Date: | 2020 | Source: | IEEE access, 2020, v. 8, p. 70969-70980 | 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. | Keywords: | Three-dimensional displays Solid modeling Convolutional neural networks Object recognition Feature extraction Shape Object recognition Recurrent neural networks Multi-layer neural network Octrees |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE access | EISSN: | 2169-3536 | DOI: | 10.1109/ACCESS.2020.2987177 | Rights: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ The following publication A. Karambakhsh, B. Sheng, P. Li, P. Yang, Y. Jung and D. D. Feng, "VoxRec: Hybrid Convolutional Neural Network for Active 3D Object Recognition," in IEEE Access, vol. 8, pp. 70969-70980, 2020 is available at https://dx.doi.org/10.1109/ACCESS.2020.2987177 |
Appears in Collections: | Journal/Magazine Article |
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Karambakhsh_Voxrec_Hybrid_Convolutional.pdf | 2.62 MB | Adobe PDF | View/Open |
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