Research on Low-Altitude Aircraft Point Cloud Generation Method Using Single Photon Counting Lidar
Abstract
:1. Introduction
2. Proposed Method
2.1. APCG Method
2.2. Improved cGAN
2.2.1. Improved cGAN Based on Depth Image
2.2.2. Generator of Improved cGAN
2.2.3. Discriminator of Improved cGAN
2.2.4. Training Process
2.3. Standard Data Generator
2.3.1. Parameter Selection of SPC-Lidar
2.3.2. Simulation of Standard Point Cloud Data of Aircraft
2.3.3. Generation of Depth Image
2.3.4. Normalization of Target Pixel Occupancy
2.3.5. Aircraft Standard Data Generator
3. Experimental Results
3.1. Training of Improved cGAN
3.2. Ablation Experiment on Improved cGAN
3.3. Performance of APCG
4. Discussion
- 1.
- The improved cGAN possesses superior generalization ability. Convolutional layers, transposed convolutional layers, and self-attention layers are introduced into the basic framework of the cGAN, and the loss function based on Wasserstein distance is adopted to replace the traditional loss function based on binary cross-entropy. The results of the ablation experiments indicate that these improvements all facilitate the enhancement of the quality and diversity of the images generated by the cGAN.
- 2.
- APCG possesses the capability of generating aircraft point clouds efficiently based on specific conditional information. APCG constructs an aircraft point cloud generation model with the aircraft depth image generator as the core. The aircraft depth image generator is the generator in the successfully trained improved cGAN, and the training data of this improved cGAN are the aircraft depth image data converted from the characteristics of the aircraft point clouds collected by the SPC-Lidar system. Hence, APCG can rapidly generate aircraft point clouds that conform to the collection characteristics of the SPC-Lidar system by utilizing the type, position, and attitude information of the aircraft.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
APCG | Aircraft Point Cloud Generation |
SPC | Single Photon Counting |
cGAN | Conditional Generative Adversarial Network |
GAN | Generative Adversarial Network |
FPD | Frechet Point Cloud Distance |
MMD | Maximum Mean Discrepancy |
JSD | Jensen–Shannon Divergence |
KL | Kullback–Leibler |
FID | Frechet Inception Distance |
GPU | Graphic Processing Unit |
SA | Self-Attention |
FC | Fully Connected |
Appendix A. Average Values of MMD and JSD
Appendix A.1. Average Value of MMD
Appendix A.2. Average Value of JSD
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Parameter | Number | Range |
---|---|---|
5 | ||
10 | for aircraft | |
for drone | ||
10 | ||
5 | ||
5 |
Parameter | Value |
---|---|
Decay rate for first moment estimate | 0.5 |
Decay rate for second moment estimate | 0.9 |
Learning rate of generator | |
Learning rate of discriminator | |
Total number of training epochs | 1000 |
Batch size | 32 |
cGAN | FID | ||
---|---|---|---|
TConv/Conv | SA | Wasserstein Loss | |
— | — | — | 52.6 |
✓ | — | — | 45.6 |
— | ✓ | — | 48.4 |
— | — | ✓ | 46.7 |
✓ | ✓ | — | 41.9 |
— | ✓ | ✓ | 44.0 |
✓ | — | ✓ | 44.8 |
✓ | ✓ | ✓ | 40.6 |
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Su, Z.; Liang, S.; Hao, J.; Han, B. Research on Low-Altitude Aircraft Point Cloud Generation Method Using Single Photon Counting Lidar. Photonics 2025, 12, 205. https://doi.org/10.3390/photonics12030205
Su Z, Liang S, Hao J, Han B. Research on Low-Altitude Aircraft Point Cloud Generation Method Using Single Photon Counting Lidar. Photonics. 2025; 12(3):205. https://doi.org/10.3390/photonics12030205
Chicago/Turabian StyleSu, Zhigang, Shaorui Liang, Jingtang Hao, and Bing Han. 2025. "Research on Low-Altitude Aircraft Point Cloud Generation Method Using Single Photon Counting Lidar" Photonics 12, no. 3: 205. https://doi.org/10.3390/photonics12030205
APA StyleSu, Z., Liang, S., Hao, J., & Han, B. (2025). Research on Low-Altitude Aircraft Point Cloud Generation Method Using Single Photon Counting Lidar. Photonics, 12(3), 205. https://doi.org/10.3390/photonics12030205