Haochun Yang
Haochun Yang has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
Hongxu Yang
1 shared publication- Hongxu Yang
BWTON Technology Co., Ltd., Hangzhou 311121, China
1 shared publication - Peiyan Chen
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
1 shared publication - Qingli Shi
BWTON Technology Co., Ltd., Hangzhou 311121, China
1 shared publication - Yunyi Liang
Business School, University of Shanghai for Science and Technology, Shanghai, China
1 shared publication Zhizhou Wu
1 shared publication
Latest Publications
- Open Access
- 23 May 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (11) , 5244
Predicting Pavement Three-Dimensional Texture from Reflectance Intensity Images Using a Conditional Generative Adversarial Network
The three-dimensional (3D) texture of pavement surfaces critically influences skid resistance, noise, and rolling resistance, but high-resolution 3D acquisition is time-consuming and requires specialized equipment. This study investigates the use of a conditional generative adversarial network (cGAN) to predict 3D pavement texture from more efficiently acquired 2D reflectance intensity images. Co-registered 3D height maps and intensity data were captured using a high-precision line laser scanner. The intensity images were preprocessed into three representations: raw intensity, histogram-equalized, and watershed-segmented images. Four input configurations, each stacking three channels of these representations, were evaluated to determine the optimal input. Additionally, the proposed cGAN was compared with mainstream image-to-image translation models using the best-performing input. Model performance was assessed using root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The results show that the configuration using only histogram-equalized images achieved the best overall performance (SSIM = 0.4065). In the model comparison, the proposed cGAN attained the highest SSIM. These findings indicate that the proposed approach can produce 3D texture maps that capture the main structural features of pavement surfaces, suggesting its potential for efficient surface characterization.
- 23 May 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (11) , 5244
Open AccessPredicting Pavement Three-Dimensional Texture from Reflectance Intensity Images Using a Conditional Generative Adversarial Network
The three-dimensional (3D) texture of pavement surfaces critically influences skid resistance, noise, and rolling resistance, but high-resolution 3D acquisition is time-consuming and requires specialized equipment. This study investigates the use of a conditional generative adversarial network (cGAN) to predict 3D pavement texture from more efficiently acquired 2D reflectance intensity images. Co-registered 3D height maps and intensity data were captured using a high-precision line laser scanner. The intensity images were preprocessed into three representations: raw intensity, histogram-equalized, and watershed-segmented images. Four input configurations, each stacking three channels of these representations, were evaluated to determine the optimal input. Additionally, the proposed cGAN was compared with mainstream image-to-image translation models using the best-performing input. Model performance was assessed using root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The results show that the configuration using only histogram-equalized images achieved the best overall performance (SSIM = 0.4065). In the model comparison, the proposed cGAN attained the highest SSIM. These findings indicate that the proposed approach can produce 3D texture maps that capture the main structural features of pavement surfaces, suggesting its potential for efficient surface characterization.