FuseNet: a multi-modal feature fusion network for 3D shape classification
Author:
Funder
National Natural Science Foundation of China
China Postdoctoral Science Foundation
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-024-03581-2.pdf
Reference59 articles.
1. Bai, S., Bai, X., Zhou, Z., Zhang, Z., Jan Latecki, L.: GIFT: a real-time and scalable 3d shape search engine. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5023–5032 (2016)
2. Bai, S., Bai, X., Zhou, Z., Zhang, Z., Tian, Q., Latecki, L.J.: GIFT: towards scalable 3d shape retrieval. IEEE Trans. Multimed. 19(6), 1257–1271 (2017)
3. Cai, Z., Fan, Q., Feris, R.S., Vasconcelos, N.: A unified multi-scale deep convolutional neural network for fast object detection. In: Proceedings of the 14th European Conference on Computer Vision—ECCV 2016, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, pp. 354–370. Springer (2016)
4. Chen, L., Zhang, Q.: DDGCN: graph convolution network based on direction and distance for point cloud learning. Vis. Comput. 39(3), 863–873 (2023)
5. Chen, X., Chen, Y., Gupta, K., Zhou, J., Najjaran, H.: SliceNet: a proficient model for real-time 3d shape-based recognition. Neurocomputing 316, 144–155 (2018)
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