## 1 Introduction  
The pavement texture refers to the characteristics of the concave-convex structure that exists on the surface of pavements. Several studies have shown [1; 2; 3] that the pavement texture has a significant relationship with skid resistance, noise mitigation, compaction uniformity, and even driving safety [4; 5; 6]. Among the many phenomena observed in research and experimentation, the quality of the compaction will affect the surface texture condition of the pavement [7; 8]. Furthermore, early damage of asphalt pavement is closely related to the quality of pavement compaction [9]. Despite post-construction testing methods used to ensure proper texture depth, preserving appropriate surface texture during compaction continues to remain a significant challenge in compaction quality control. Hence, it is important to investigate the evolution law of pavement surface texture during the compaction process. The categorization of pavement surface textures into four groups, namely micro-texture, macro-texture, mega-texture, and unevenness, based on their wavelengths, was proposed by PIARC [10]. Typically, field measurements are used to detect pavement texture; however, the precision of these methods is often inadequate and varied. Variations in the testing methods and texture indicators induce variations in the decision-making process during compact quality control. For instance, to gauge macro-texture, which covers wavelengths between 0.5 mm and 50 mm, the sand patch test method is employed. For evaluating micro-texture within a range of 1 \(\upmu\)m to 0.5 mm, testing methods, such as the British Pendulum Tester, Grip Tester, or Locked Wheel Skid Tester, are utilized. Static testing methods can provide measurements of Mean Texture Depth, which are both location-specific and time-consuming. It is also challenging to use these texture indicators without proper validation and site inspection in the construction [11]. Hence, it is important to achieve a more accurate and efficient characterization of the pavement texture using the laser profiler method during the site compaction process. Various sensors, such as high-definition cameras, Lidar, or 3D laser scanners, have been introduced to obtain data more efficiently [12; 13]. Among these, the 3D laser scanner stands out as it captures the pavement profile and generates point cloud data containing depth information that describes pavement texture characterization [14]. Indices, such as mean texture depth (MTD) and mean profile depth (MPD), calculated from laser-based data, exhibit a strong correlation with those tested from the sand patch test [15]. Furthermore, high-resolution point cloud data obtained from 3D laser scanners have been utilized to predict friction [16], aggregate gradation [17], and skid resistance [18]. However, current research primarily focuses on the evolution of pavement texture during the maintenance phase, and there is a dearth of studies examining the evolution law of texture during compaction. Different signal processing methods have been employed to investigate the point cloud data of pavement texture. While Fourier transform methods are widely used, they tend to mask the spatial correlation of the data, which limits their interpretability [19]. Some researchers have applied the Hilbert-Huang transform to investigate the correlation between pavement texture and friction [20]. In addition, pavement texture has been characterized using diverse approaches, including fractal analysis [21], entropy theory [22], power spectral analysis [23], and wavelet analysis [24]. Moreover, the availability of high-density point cloud data, obtained through high-resolution measurements, has made it possible to apply deep learning methods to pavement analysis. For instance, the deep fusion network has emerged as a new tool for efficient and large-scale pavement quality evaluation [3; 24], while a domain knowledge-based deep learning network has been used to estimate pavement service life through vast high-definition image data [25; 26].