V$$^2$$MLP: an accurate and simple multi-view MLP network for fine-grained 3D shape recognition
Author:
Funder
National Natural Science Foundation of China
Natural Science Foundation of Ningxia Province
Publisher
Springer Science and Business Media LLC
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s00371-023-03191-4.pdf
Reference41 articles.
1. Xiong, S., Tziafas, G., Kasaei, H.: Enhancing fine-grained 3D object recognition using hybrid multi-modal vision transformer-CNN models. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023) (2023)
2. Wu, R., Bai, J., Li, W., Jiang, J.: DCNet: exploring fine-grained vision classification for 3D point clouds. Vis. Comput. (2023). https://doi.org/10.1007/s00371-023-02816-y
3. Shao, H., Bai, J., Wu, R., Jiang, J., Liang, H.: FGPNet: a weakly supervised fine-grained 3D point clouds classification network. Pattern Recogn. 139, 109509 (2023). https://doi.org/10.1016/j.patcog.2023.109509
4. Liu, X., Han, Z., Liu, Y.-S., Zwicker, M.: Fine-grained 3D shape classification with hierarchical part-view attention. IEEE Trans. Image Process. 30, 1744–1758 (2021)
5. Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 945–953 (2015)
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