Author:
Gao Xue-Yao,Yang Bo-Yu,Zhang Chun-Xiang
Abstract
<abstract>
<p>With the development of multimedia technology, the number of 3D models on the web or in databases is becoming increasingly larger and larger. It becomes more and more important to classify and retrieve 3D models. 3D model classification plays important roles in the mechanical design field, education field, medicine field and so on. Due to the 3D model's complexity and irregularity, it is difficult to classify 3D model correctly. Many methods of 3D model classification pay attention to local features from 2D views and neglect the 3D model's contour information, which cannot express it better. So, accuracy the of 3D model classification is poor. In order to improve the accuracy of 3D model classification, this paper proposes a method based on EfficientNet and Convolutional Neural Network (CNN) to classify 3D models, in which view feature and shape feature are used. The 3D model is projected into 2D views from different angles. EfficientNet is used to extract view feature from 2D views. Shape descriptors D1, D2, D3, Zernike moment and Fourier descriptors of 2D views are adopted to describe the 3D model and CNN is applied to extract shape feature. The view feature and shape feature are combined as discriminative features. Then, the softmax function is used to determine the 3D model's category. Experiments are conducted on ModelNet 10 dataset. Experimental results show that the proposed method achieves better than other methods.</p>
</abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
Reference32 articles.
1. J. W. Tangelder, R. C. Veltkamp, A survey of content based 3D shape retrieval methods, Multimedia Tools Appl., 39 (2008), 441–471. https://doi.org/10.1007/s11042-007-0181-0
2. H. Y. Zhou, A. A. Liu, W. Z. Nie, J. Nie, Multi-view saliency guided deep neural network for 3-D object retrieval and classification, IEEE Trans. Multimedia, 22 (2020), 1496–1506. https://doi.org/10.1109/TMM.2019.2943740
3. C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, L. Guibas, Volumetric and multi-view CNNs for object classification on 3D data, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), 5648–5656. https://doi.org/10.1109/CVPR.2016.609
4. X. A. Li, L. Y. Wang, J. Lu, Multiscale receptive fields graph attention network for point cloud classification, Complexity, 2021 (2021), 1076–2787. https://doi.org/10.1155/2021/8832081
5. Y. L. Zhang, J. T. Sun, M. K. Chen, Q. Wang, Y. Yuan, R. Ma, Multi-weather classification using evolutionary algorithm on EfficientNet, in 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, (2021), 546–551. https://doi.org/10.1109/PerComWorkshops51409.2021.9430939