DualMLP: a two-stream fusion model for 3D point cloud classification

Author:

Paul SnehaORCID,Patterson Zachary,Bouguila Nizar

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Reference41 articles.

1. Paul, S., Patterson, Z., Bouguila, N.: Improved training for 3d point cloud classification. In: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), pp. 253–263 (2022). Springer

2. Paul, S., Patterson, Z., Bouguila, N.: Crossmoco: multi-modal momentum contrastive learning for point cloud. In: 2023 20th Conference on Robots and Vision (CRV), pp. 273–280 (2023). IEEE

3. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017)

4. Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inf. Process. Syst. 30 (2017)

5. Ma, X., Qin, C., You, H., Ran, H., Fu, Y.: Rethinking network design and local geometry in point cloud: A simple residual MLP framework. arXiv preprint arXiv:2202.07123 (2022)

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