The 3D reconstruction of asphalt pavement macrotexture holds significant engineering value for pavement quality assessment and performance m
The 3D reconstruction of asphalt pavement macrotexture holds significant engineering value for pavement quality assessment and performance monitoring. However, conventional 3D reconstruction methods face challenges, such as high equipment costs and operational complexity, limiting their widespread application in engineering practice. Meanwhile, current deep learning-based monocular image reconstruction for pavement texture remains in its early stages. To address these technical limitations, this study systematically prepared four types of asphalt mixture specimens (AC, SMA, OGFC, and PA) with a total of 14 gradations. High-precision equipment was used to simultaneously capture 2D RGB images and 3D RGB-D point cloud data of the surface texture. An innovative multi-scale feature fusion CNN model was developed based on an encoder–decoder architecture, along with an optimized training strategy for model parameters. For performance evaluation, multiple metrics were employed, including root mean square error (RMSE = 0.491), relative error (REL = 0.102), and accuracy at different thresholds (δ = 1/2/3: 0.931, 0.979, 0.990). The results demonstrate strong correlations between the reconstructed texture’s mean texture depth (MTD) and friction coefficient (f8) with actual measurements (0.913 and 0.953, respectively), outperforming existing methods. This confirms that the proposed CNN model achieves precise 3D reconstruction of asphalt pavement macrotexture, effectively supporting skid resistance evaluation. To validate engineering applicability, field tests were conducted on pavements with various gradations. The model exhibited excellent robustness under different conditions. Furthermore, based on extensive field data, this study established a quantitative relationship between MTD and friction coefficient, developing a more accurate pavement skid resistance evaluation system to support maintenance decision-making.
