## 1 Introduction  
The digitization of road infrastructure is not only the future development direction of the transportation industry but also one of the critical processes that need continuous attention and solutions to achieve long-term, high-quality road development and improve road service resilience [1, 2]. At present, the road industry is entering the maintenance stage from the large-scale construction stage. How to efficiently complete road detection without damage has become a hot research issue [3]. Due to traffic, environmental, and other factors, roads are prone to defects such as cracks, cavities, and pits. These defects can generally be divided into road surface defects and internal structural defects [4]. Ruts, pits, and other surface defects are easy to detect and identify among them. However, because hidden cracks, cavities, and other structural defects are located in the interior of the road, it is not easy to detect them. If not found and treated in time, they may extend to the surface, causing more severe damage, such as pavement collapse. Traditional detection methods for road structural defects, such as drilling coring and bending detection, are no longer suitable for large-scale road maintenance due to the disadvantages of destructiveness, high cost, and incomplete detection. Ground-Penetrating Radar (GPR) technology has been gradually used in road detection since the 1980s. Because of its nondestructive, fast detection speed and high degree of automation, it has developed relatively rapidly [5; 6; 7]. It transmits electromagnetic waves to the interior of the road structure. These electromagnetic waves will have different degrees of reflection and refraction when they meet the two media with different dielectric constants during propagation, which can be used for the assessment. At present, through the efforts of many researchers all over the world, GPR technology has made significant progress in various aspects, such as pavement thickness measurement [8; 9; 10; 11], underground pipeline detection [12; 13; 14], road moisture content and porosity detection [15; 16; 17], and road structural defects detection [4; 18; 19; 20], and has been gradually applied to practical projects. Among them, GPR-based 3D reconstruction technology can quickly and intuitively acquire the location, scale, and other important information of road structural defects, which has excellent development potential [21]. [PERSON] et al. [22; 23] used the Finite-Difference Time-Domain (FDTD) principle to build the dielectric constant model of asphalt mixture and analyzed the GPR image characteristics of crack defects on this basis, that is, the reflected image presents a double-curve shape, and the amplitude intensity increases with the increase in the crack width. [PERSON] et al. [24; 25] used the results of an indoor road structural defects model test and numerical simulations to analyze GPR images of cracks and cavities, where they demonstrated the influence of the defect size on waveform images. However, this research only made a simple two-dimensional determination of defects from the features of simulated or detected GPR images. They cannot obtain more information, such as the form, scale, and depth of defects. Using the F-K migration algorithm, [PERSON] et al. [26] performed clutter removal and scattering strength repair for GPR images of structural defects on a bridge deck, effectively restoring the initial position of steel bars and holes on the bridge deck, but the error was relatively large. [PERSON] et al. [27] proposed a process of generating 3D models based on the original data from 3D GPR and successfully applied it to underground targets of three different sizes and shapes. [PERSON] et al. [28] used 3D GPR to detect the size and distribution of tree roots, and realized 3D visualization of underground roots by automatically setting amplitude thresholds. [PERSON] et al. [29] studied the 3D imaging technology of tunnel slump GPR based on a half-space scanning measurement mode. [PERSON] et al.