International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Utilization of Deep Learning for LiDAR Point Cloud Classification

Abstract

With the increasing availability of LiDAR and comparable sensor technologies, the accessibility of point cloud data has significantly improved. Nevertheless, the magnitude and intricacy of point cloud data pose challenges for processing and interpretation. A point cloud classification model utilizing deep learning is proposed as a solution to the difficulties encountered in evaluating point cloud data, primarily acquired via LiDAR and comparable sensor technologies. The utilization of data augmentation technologies and meticulous preprocessing enhances the efficiency of the Point-Net model in processing and categorizing point cloud data. Empirical findings validate the significance of optimizing hyperparameters, such as number of epochs, batch size, and learning rate, in order to enhance the performance of the model. The enhanced Point-Net model demonstrated a notable enhancement in classification accuracy, reaching a maximum accuracy of 0.7097, which is a substantial improvement compared to the initial performance.

Country : Lebanon

1 Shahlaa Falah Hasan Al-Tameemi2 Alaa Ali Ghaith3 Ahmad Ghandour

  1. Department of Computer and Communications, Faculty of Engineering, Islamic University of Lebanon, Wardanieh, Lebanon
  2. Faculty of Sciences 1, Lebanese University, Beirut, Lebanon
  3. Department of Computer and Communications, Faculty of Engineering, Islamic University of Lebanon, Wardanieh, Lebanon

IRJIET, Volume 8, Issue 3, March 2024 pp. 167-172

doi.org/10.47001/IRJIET/2024.803022

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References

  1. Ding. Z, Sun. Y, Xu. S, Pan. Y, Peng. Y, and Mao. Z, “Recent Advances and Perspectives in Deep Learning Techniques for 3D Point Cloud Data Processing.” Robotics, vol. 12, no. 4, 2023.
  2. Yao. X, Guo. J, Hu. J, and Cao. Q “Using deep learning in semantic classification for point cloud data.”IEEE Access, vol. 7, pp. 37121-37130, 2019.‏
  3. Qi. C. R, Su. H, Mo. K, and Guibas. L. J, “Pointnet: Deep learning on point sets for 3d classification and segmentation.”in Proc. IEEE conference on computer vision and pattern recognition, pp. 652-660, 2017.
  4. Khan. S. A, Shi. Y, Shahzad. M, and Zhu. X. X, “FGCN: Deep feature-based graph convolutional network for semantic segmentation of urban 3D point clouds.”in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops”, pp. 198-199, 2020.
  5. He. C, Zeng. H, Huang. J, Hua. X. S, and Zhang. L, “Structure aware single-stage 3d object detection from point cloud.” in Proc.IEEE/CVF conference on computer vision and pattern recognition, pp. 11873-11882, 2020.‏
  6. Dominik. W, Bożyczko. M, and Tułacz-Maziarz. K, “Deep learning for automatic LiDAR point cloud processing, 2021.‏
  7. Zhang. L., Li. Z, Li. A, and Liu. F,“Large-scale urban point cloud labeling and reconstruction,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 138, pp. 86-100, 2018.‏
  8. Z. Wang, L. Zhang, L. Zhang, R. Li, Y. Zheng and Z. Zhu, "A Deep Neural Network With Spatial Pooling (DNNSP) for 3-D Point Cloud Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 8, pp. 4594-4604, Aug. 2018.
  9. W. Wang, R. Yu, Q. Huang and U. Neumann, "SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation," IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 2569-2578, 2018.
  10. Qi, C. R., Yi, L., Su, H., and Guibas, L. J, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space.”Advances in neural information processing systems, 2017.‏
  11. Seidel, D., Annighöfer. P., Seifert. Q. E., and Ammer. C. “Predicting tree species from 3D laser scanning point clouds using deep learning.”Frontiers in Plant Science, 2021.
  12. ‏Hamraz. H, Jacobs. N.B, Contreras, Marco. A, Clark and Chase. H, "Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees." ISPRS Journal of Photogrammetry and Remote Sensing, vol. 158, pp. 219-230, 2019.
  13. Liu. W, Sun. J, Li. W, Hu. T and Wang. P. “Deep Learning on Point Clouds and Its Application: A Survey.”Sensors (Basel), vol. 19, no. 19, 2019.

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