This GitHub repository has been created for the research project titled "Improving Aerial Targeting Precision: A Study on Point Cloud Semantic Segmentation with Advanced Deep Learning Algorithms."
This repository contains the code for the Color-Enhanced PointNet++ and RandLA-NET algorithms. Additionally, the dataset produced within the scope of the study is also provided in this repository.
Access to the photogrammetrically produced 3D Point Cloud dataset can be obtained from the DATA folder within the repository. The DublinCity dataset can be accessed from the following address: https://v-sense.scss.tcd.ie/dublincity/
All rights to this study and the data produced within the study are reserved. Unauthorized commercial use is strictly prohibited.
2024-05-16
MSc. Geomatics Engineer / Software Engineer
Salih Bozkurt
eMail: [email protected]
References
1. Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017). Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems, 30.
2. Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., ... & Markham, A. (2020). 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).
3. The RandLA-NET algorithm was utilized from the Open3D-ML repository. You can access the entire code structure at https://github.com/isl-org/Open3D-ML.
4. DublinCity LiDAR dataset : https://v-sense.scss.tcd.ie/dublincity/