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FeatureB2SENet: point cloud classification of large scenes

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Abstract

With the continuous development of 3D data acquisition technology in recent years, it is more and more convenient to obtain the point cloud data of large scenes, which contains a variety of rich information. How to effectively and accurately classify and segment point cloud data of large scenes has become a research hot-spot in the field of computer vision. In this paper, we study the method based on clustering, make full use of the spatial location and context information, and propose a new network framework, FeatureB2SENet. In the 2D and 3D projection feature calculation, we generate a \(32\times 32\times 1\) feature image for each point and input it into the convolution neural network to process the feature image. Finally, a comprehensive verification analysis is carried out on GML\(\_\)A, GML\(\_\)B and Vaihingen data sets, which proves that the classification effect is better.

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References

  1. Haase, D., Amthor, M.: Rethinking depthwise separable convolutions: How intra-kernel correlations lead to improved mobilenets. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14588–14597 (2020)

  2. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

  3. Wang, L., Meng, W., Xi, R., Zhang, Y., Ma, C., Ling, L., Zhang, X.: 3d point cloud analysis and classification in large-scale scene based on deep learning. IEEE Access 7, 55649–55658 (2019)

    Article  Google Scholar 

  4. Rusu, R.B., Blodow, N, Beetz, M.: Fast point feature histograms (fpfh) for 3d registration. In: 2009 IEEE International Conference on Robotics and Automation, pp. 3212–3217 (2009)

  5. Salih, Y. Malik, A.S., Sidib, D., Simsim, M.T., Saad, N., Meriaudeau, F.: Compressed vfh descriptor for 3d object classification. In: 2014 3DTV-Conference: The True Vision–Capture, Transmission and Display of 3D Video (3DTV-CON), pp. 1–4 (2014)

  6. Jie, S., Zulong, L.: Feature selection and classification of airborne lidar data in urban areas using random forests. In: Geomatics and Information Science of Wuhan University vol. 39, pp. 1310–1313 (2014)

  7. Mallet, C., Bretar, F., Roux, M. Soergel, U., Heipke, C.: Relevance assessment of full-waveform lidar data for urban area classification. ISPRS J. Photogramm. Remote Sens., 66(6, Supplement):S71–S84, 2011. Advances in LIDAR Data Processing and Applications

  8. Weinmann, M., Jutzi, B., Hinz, S., Mallet, C.: Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers. ISPRS J. Photogramm. Remote Sens. 105, 286–304 (2015)

    Article  Google Scholar 

  9. Li, Y., Chen, D., Du, X., Xia, S., Wang, Y., Xu, S., Yang, Q.: Higher-order conditional random fields-based 3d semantic labeling of airborne laser-scanning point clouds. Remote Sens., 11(10) (2019)

  10. Zhang, Z., Zhang, L., Tong, X., Mathiopoulos, P.T., Guo, B., Huang, X., Wang, Z., Wang, Y.: A multilevel point-cluster-based discriminative feature for als point cloud classification. IEEE Trans. Geosci. Remote Sens. 54(6), 3309–3321 (2016)

    Article  Google Scholar 

  11. Zhu, Q., Li, Y., Han, H., Bo, W.: Robust point cloud classification based on multi-level semantic relationships for urban scenes. ISPRS J. Photogram. Remote Sens. 129, 86–102 (2017)

    Article  Google Scholar 

  12. Quan, X., Tang, X., Li, G., Liu, Z., Xue, Y.: Reasearch progress on land cover classification application of satellite laser altimetry data. Remote Sens. Inf. 34(2), 4–10 (2019)

    Google Scholar 

  13. Li, W., Wang, F.-D., Xia, G.-S.: A geometry-attentional network for ALS point cloud classification. ISPRS J. Photogram. Remote Sens. 164, 26–40 (2020)

    Article  Google Scholar 

  14. Wen, C., Li, X., Yao, X., Peng, L., Chi, T.: Airborne lidar point cloud classification with global-local graph attention convolution neural network. ISPRS J. Photogram. Remote Sens. 173, 181–194 (2021)

    Article  Google Scholar 

  15. Nezhadarya, E., Taghavi, E., Razani, R., Liu, B., Luo, J.: Adaptive hierarchical down-sampling for point cloud classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

  16. Biasutti, P., Lepetit, V., Aujol, J.-F., Bredif, M., Bugeau, A.: Lu-net: An efficient network for 3d lidar point cloud semantic segmentation based on end-to-end-learned 3d features and u-net. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (2019)

  17. Wang, Y., Shi, T., Yun, P., Tai, L., Liu, M.: Pointseg: Real-time semantic segmentation based on 3d lidar point cloud (2018)

  18. Wu, B., Wan, A., Yue, X., Keutzer, K.: Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1887–1893 (2018)

  19. Biasutti, P., Bugeau, A., Aujol, J.-F., Brdif, M.: Riu-net: Embarrassingly simple semantic segmentation of 3d lidar point cloud (2019)

  20. Li, M.: Research on point cloud classification based on deep learning. Master’s thesis, North University of China (2020)

  21. Shapovalov, R., Er Velizhev, A., Barinova, O.: Nonassociative markov networks for 3d point cloud classification. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII, Part 3A. Citeseer (2010)

  22. Niemeyer, J., Rottensteiner, F., Soergel, U.: Contextual classification of lidar data and building object detection in urban areas. ISPRS J. Photogram. Remote Sens. 87, 152–165 (2014)

    Article  Google Scholar 

  23. Zhang, Z., Zhang, L., Tong, X., Guo, B., Zhang, L., Xing, X.: Discriminative-dictionary-learning-based multilevel point-cluster features for ALS point-cloud classification. IEEE Trans. Geosci. Remote Sens. 54(12), 7309–7322 (2016)

    Article  Google Scholar 

  24. Maturana, D., Scherer, S.: Voxnet: A 3d convolutional neural network for real-time object recognition. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 922–928 (2015)

  25. Zhang, Z., Zhang, L., Tan, Y., Zhang, L., Liu, F., Zhong, R.: Joint discriminative dictionary and classifier learning for ALS point cloud classification. IEEE Trans. Geosci. Remote Sens. 56(1), 524–538 (2018)

    Article  Google Scholar 

  26. 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 (CVPR) (2017)

  27. Zhang, Z., Sun, L., Zhong, R., Chen, D., Zhang, L., Li, X., Wang, Q., Chen, S.: Hierarchical aggregated deep features for ALS point cloud classification. IEEE Trans. Geosci. Remote Sens. 59(2), 1686–1699 (2021)

    Article  Google Scholar 

  28. Li, X., He, M., Li, H., Shen, H.: A combined loss-based multiscale fully convolutional network for high-resolution remote sensing image change detection. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022)

    Google Scholar 

  29. Fauzi, F., Permanasari, A. E., Akhmad Setiawan, N.: Butterfly image classification using convolutional neural network (cnn). In: 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA), pp. 66–70 (2021)

  30. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

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Acknowledgements

This research is supported by the National Natural Science Foundation of China under Grant 62202137, Zhejiang Provincial Natural Science Foundation of China under Grant LQ22F030004, National Natural Science Foundation of China (NSFC) under Grant 71872131, Starting Research Fund of Great Bay University under Grant YJKY220020 and the Research Foundation of Hangzhou Dianzi University under Grant KYS335622091 and KYH333122029M.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Hangli Weng, Xin Sheng & Liping Wang

  2. Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

    Guodao Zhang

  3. School of Information Science and Technology, Hangzhou Normal University, Hangzhou, 310023, China

    Ruyu Liu

  4. Haixi Institutes, Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing, Hangzhou, 362000, China

    Ruyu Liu

  5. Institute of Scientific Research, Great Bay University, Dongguan, 523800, China

    Ping-Kuo Chen

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  1. Hangli Weng
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Correspondence to Guodao Zhang or Ruyu Liu.

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Weng, H., Zhang, G., Sheng, X. et al. FeatureB2SENet: point cloud classification of large scenes. Vis Comput 40, 1037–1051 (2024). https://doi.org/10.1007/s00371-023-02830-0

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