## 3 Results and Discussion
### Evaluation of Macroscopic Texture Accuracy of Asphalt After Reconstruction  
The experimental protocol incorporates stratified subgroup analysis of the road texture RGB-D test set, categorized by NMAS and aggregate gradation classifications, to systematically evaluate the CNN's predictive capability across asphalt mixture typologies. As depicted in Figure 11, the quantitative evaluation metrics (MAE, RMSE, REL) reveal distinct performance patterns among gradation categories. Experimental data analysis reveals distinct gradient characteristics in prediction accuracy among different types of asphalt mixtures. Both dense-graded AC and SMA mixtures demonstrate superior predictive performance: AC mixtures maintain stable MAE below 0.7, RMSE consistently under 1.18, and REL strictly controlled within 0.85. SMA mixtures exhibit even more stable predictive capability, with average MAE reaching 0.68, all RMSE values below 1.1, and REL stably around 0.85. In comparison, OGFC mixtures show slightly reduced prediction accuracy. While MAE remains below 0.8 and RMSE does not exceed 1.18, their REL approaches 1. Notably, PA mixtures exhibit significantly higher error metrics across the board--MAE fluctuates between 0.9 and 1.35, RMSE ranges from 1.4 to 1.8, and REL reaches as high as 1.3-1.7. This comprehensive performance gap clearly highlights the inherent limitations of PA mixtures in terms of prediction model applicability.  
Figure 10: Training process under different training strategies: (**a**) Transfer learning is not used; (**b**) Using transfer learning.  
The main reasons for this phenomenon are as follows: on the one hand, AC and SMA-type asphalt mixtures are made of aggregates with smaller pores, whereas OGFC and PA-type asphalt mixtures are made of aggregates with larger pores, especially PA mixtures. The design porosity is greater than 18%, and the proportion of fine macro-texture is relatively small, which leads to CNN being more inclined to extract coarse macro-texture features during training and to ignore the extraction of fine macro-texture features. On the other hand, the number of PA-type asphalt mixture data samples is relatively small compared with other low-porosity mixtures, making the number of CNNs constructed when training PA-type samples lower than for the other three types of asphalt mixtures. In summary, the proposed CNN in this paper can accurately reconstruct the macroscopic texture of AC, SMA and OGFC but, when reconstructing the macroscopic texture of PA, the accuracy is slightly lower than that of the other three types of mixture.  
In order to show the reconstruction effect of the proposed CNN on the macroscopic texture of asphalt more intuitively, Figure 11 lists the reconstruction results of four different types of asphalt mixtures. Among them, Figure 12a is the input two-dimensional image, Figure 12b is the reconstructed macro texture depth map, and Figure 12c is the collected macro texture depth map of asphalt. It can be seen from Figure 12 that the proposed CNN  
Figure 11: MAE, RMSE and REL results of different types of mixtures: (**a**) AC mixture; (**b**) SMA mixture; (**c**) OGFC mixture; (**d**) PA mixture.  
in this paper can accurately reconstruct the macro texture of AC, SMA, OGFC and PA asphalt mixture.