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DDGCN: graph convolution network based on direction and distance for point cloud learning

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Abstract

Point cloud is usually used to construct the surface shape of three-dimensional geometric objects. Due to the disorder and irregularity of the point cloud, it is still a challenge to fully acquire the semantic features of the point cloud. With the development of graph neural network and graph convolution neural network, researchers are integrating point cloud and graph structure to better represent the semantic features of the point cloud. In this paper, we propose a novel graph convolutional neural network that integrates distance and direction (DDGCN), which constructs a dynamic neighborhood graph by obtaining the similarity matrix of the point cloud, and then uses several multi-layer perceptrons to obtain the local features of the point cloud. For the sake of making the intra-classes in the point cloud data more compact and the spacing between classes larger than the intra-class spacing, we propose a new loss function combined with center loss. The proposed DDGCN has been tested on ModelNet40 dataset, ShapeNet Part dataset and S3DIS dataset, and has achieved state-of-the-art performance in both classification and segmentation tasks.

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References

  1. Armeni, I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., Savarese, S.: 3d semantic parsing of large-scale indoor spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1534–1543 (2016)

  2. Chen, C., Fragonara, L.Z., Tsourdos, A.: Gapnet: graph attention based point neural network for exploiting local feature of point cloud. arXiv preprint arXiv:1905.08705 (2019)

  3. Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., Markham, A.: Randla-net: efficient semantic segmentation of large-scale point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11108–11117 (2020)

  4. Jiang, M., Wu, Y., Zhao, T., Zhao, Z., Lu, C.: Pointsift: a sift-like network module for 3d point cloud semantic segmentation. arXiv preprint arXiv:1807.00652 (2018)

  5. Kingma, D.P., Adam, J.B.: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  6. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  7. Landrieu, L., Simonovsky, M.: Large-scale point cloud semantic segmentation with superpoint graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4558–4567 (2018)

  8. Li, G., Muller, M., Thabet, A., Ghanem, B.: Deepgcns: Can gcns go as deep as cnns? In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9267–9276 (2019)

  9. Li, H., Sun, Z.: A structural-constraint 3d point clouds segmentation adversarial method. Vis. Comput. 37(2), 325–340 (2021)

    Article  Google Scholar 

  10. Lin, Z.H., Huang, S.Y., Wang, Y.C.F.: Convolution in the cloud: learning deformable kernels in 3d graph convolution networks for point cloud analysis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1800–1809 (2020)

  11. Lu, Y., Zhou, K., Wu, X., Gong, P.: A novel multi-graph framework for salient object detection. Vis. Comput. 35(11), 1683–1699 (2019)

    Article  Google Scholar 

  12. 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)

  13. Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. arXiv preprint arXiv:1706.02413 (2017)

  14. Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2008)

    Article  Google Scholar 

  15. Sun, Y., Miao, Y., Chen, J., Pajarola, R.: Pgcnet: patch graph convolutional network for point cloud segmentation of indoor scenes. Vis. Comput. 36(10), 2407–2418 (2020)

    Article  Google Scholar 

  16. Te, G., Hu, W., Zheng, A., Guo, Z.: Rgcnn: regularized graph cnn for point cloud segmentation. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 746–754 (2018)

  17. Wang, C., Samari, B., Siddiqi, K.: Local spectral graph convolution for point set feature learning. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 52–66 (2018)

  18. Wang, L., Huang, Y., Hou, Y., Zhang, S., Shan, J.: Graph attention convolution for point cloud semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10296–10305 (2019)

  19. Wang, X., He, J., Ma, L.: Exploiting local and global structure for point cloud semantic segmentation with contextual point representations. arXiv preprint arXiv:1911.05277 (2019)

  20. Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph cnn for learning on point clouds. Acm Trans. Graph. (tog) 38(5), 1–12 (2019)

    Article  Google Scholar 

  21. Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: European Conference on Computer Vision, pp. 499–515. Springer (2016)

  22. Wu, W., Qi, Z., Fuxin, L.: Pointconv: Deep convolutional networks on 3d point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9621–9630 (2019)

  23. Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912–1920 (2015)

  24. Xu, M., Ding, R., Zhao, H., Qi, X.: Paconv: position adaptive convolution with dynamic kernel assembling on point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3173–3182 (2021)

  25. Xu, Q., Sun, X., Wu, C.Y., Wang, P., Neumann, U.: Grid-gcn for fast and scalable point cloud learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5661–5670 (2020)

  26. Yi, L., Kim, V.G., Ceylan, D., Shen, I.C., Yan, M., Su, H., Lu, C., Huang, Q., Sheffer, A., Guibas, L.: A scalable active framework for region annotation in 3d shape collections. ACM Trans. Graph. (ToG) 35(6), 1–12 (2016)

  27. Zhang, Y., Rabbat, M.: A graph-cnn for 3d point cloud classification. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6279–6283. IEEE (2018)

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  1. School of Artificial Intelligence and Computer science, Jiangnan University, Wuxi, 214122, China

    Lifang Chen & Qian Zhang

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  1. Lifang Chen
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  2. Qian Zhang
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Correspondence to Lifang Chen.

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Chen, L., Zhang, Q. DDGCN: graph convolution network based on direction and distance for point cloud learning. Vis Comput 39, 863–873 (2023). https://doi.org/10.1007/s00371-021-02351-8

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