Utilization of Deep Learning for LiDAR Point Cloud Classification

Shahlaa Falah Hasan Al-TameemiDepartment of Computer and Communications, Faculty of Engineering, Islamic University of Lebanon, Wardanieh, LebanonAlaa Ali GhaithFaculty of Sciences 1, Lebanese University, Beirut, LebanonAhmad GhandourDepartment of Computer and Communications, Faculty of Engineering, Islamic University of Lebanon, Wardanieh, Lebanon

Vol 8 No 3 (2024): Volume 8, Issue 3, March 2024 | Pages: 167-172

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 01-04-2024

doi Logo doi.org/10.47001/IRJIET/2024.803022

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.

Keywords

LiDAR, Deep Learning, Point-Net Network, Fine-Tuning Hyperparameter, Point Cloud Classification


Citation of this Article

          

Shahlaa Falah Hasan Al-Tameemi, Alaa Ali Ghaith, Ahmad Ghandour, “Utilization of Deep Learning for LiDAR Point Cloud Classification” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 8, Issue 3, pp 167-172, March 2024. Article DOI https://doi.org/10.47001/IRJIET/2024.803022

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.