Robust 3D Shape Classification via Non-Local Graph Attention Network

Shengwei Qin, Zhong Li, Ligang Liu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 5374-5383

Abstract


We introduce a non-local graph attention network (NLGAT), which generates a novel global descriptor through two sub-networks for robust 3D shape classification. In the first sub-network, we capture the global relationships between points (i.e., point-point features) by designing a global relationship network (GRN). In the second sub-network, we enhance the local features with a geometric shape attention map obtained from a global structure network (GSN). To keep rotation invariant and extract more information from sparse point clouds, all sub-networks use the Gram matrices with different dimensions as input for working with robust classification. Additionally, GRN effectively preserves the low-frequency features and improves the classification results. Experimental results on various datasets exhibit that the classification effect of the NLGAT model is better than other state-of-the-art models. Especially, in the case of sparse point clouds (64 points) with noise under arbitrary SO(3) rotation, the classification result (85.4%) of NLGAT is improved by 39.4% compared with the best development of other methods.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Qin_2023_CVPR, author = {Qin, Shengwei and Li, Zhong and Liu, Ligang}, title = {Robust 3D Shape Classification via Non-Local Graph Attention Network}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {5374-5383} }