## 1 Introduction  
Asphalt pavement is in contact with tires directly, and its texture affects friction for driving safely [1]. According to the International Organization for Standardization (ISO), pavement texture is divided into micro texture (0.001-0.5 mm), macro texture (0.5-50 mm) and large-scale structure based on the wavelength range [2]. Among these, macro texture plays a key role in determining the skid resistance and noise performance of asphalt pavement by influencing the hysteresis force and low-frequency noise between the tire and pavement [3]. Therefore, developing an accurate and efficient method for reconstructing the macro texture of asphalt pavement is crucial for road condition monitoring and pavement performance evaluation [4].  
The 3D reconstruction of asphalt pavement texture is essential for evaluating its texture characteristics. Currently, most equipment used for 3D reconstruction of asphalt pavement texture is industrial-grade, which is expensive and incurs high reconstruction costs, making widespread application difficult. Therefore, transforming of pavement texture 3D reconstruction equipment from industrial level to consumer level is an urgent challenge that needs to be addressed. Some researchers have attempted to use image-based methods to realize the 3D reconstruction of the macroscopic texture of asphalt pavement at a relatively low cost [5; 6]. Based on the number of perspectives needed to reconstruct the object to be measured from different perspectives, these methods can be categorized into monocular image 3D reconstruction [7], binocular image 3D reconstruction and multi-view image 3D reconstruction [8]. Among these, compared to monocular image-based 3D reconstruction, multi-view image has problems such as image calibration, high computational complexity and difficulty in dealing with dynamic scenes [9]. Furthermore, current research on the 3D reconstruction of monocular images mainly focuses on the 3D reconstruction of indoor and outdoor scenes, and its application in road engineering still needs further research.  
In recent years, with the continuous updating of machine learning algorithms, deep neural networks have played a crucial role in image classification [10], scene semantic segmentation [11], image super-resolution [12], target detection and recognition [13] and depth prediction owing to their strong feature learning capabilities. On this basis, in order to improve the accuracy of predicting depth maps from monocular images, researchers have carried out many studies from the perspectives of network structure [14], loss function [15], problem transformation [6] and have led to the proposal of numerous novel algorithms. Significant breakthroughs have been made in monocular image depth estimation based on deep learning and the convolutional neural network algorithm. However, at present, the monocular visual neural network model and method for the 3D reconstruction of asphalt pavement macro-texture are still far from optimal, and further research is urgently required.  
In view of this, the primary objective of this paper is to develop a novel CNN architecture for the macrotexture reconstruction of asphalt pavements based on monocular images, which in turn enables the detection of pavement skid resistance. Firstly, different gradation types of asphalt mixtures are used to establish asphalt macrotexture RGB-D dataset, a new encoder-decoder network architecture is designed to recover the depth information from asphalt macro-texture 2D images, and a series of training strategies are proposed to optimize the model. Secondly, the accuracy of the reconstructed texture is evaluated using MAE, RMSE, REL and Accuracies. Finally, the effectiveness of the reconstructed texture is analyzed based on MTD and \(f_{8}\) indicators, and the reconstructed texture is shown to be directly applicable for the evaluation of pavement skid resistance. The flowchart is shown in Figure 1.