Research on the 3D Fine Modeling Method of In-Service Road

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Introduction
With the continuous development of China's economic construction, transportation highway construction has made great achievements. According to the statistical bulletin of transportation industry development in 2021, the total national highway mileage was 5.2807 million kilometers. In the outline of digital transportation development plan issued by the Ministry of Transport, it is clearly pointed out that the full-factor and full-cycle digitization of transportation infrastructure should be realized, and the threedimensional digitization of physical facilities should be realized. Under the premise of reducing data redundancy and ensuring the quality of the model, the rapid realization of three-dimensional reverse reconstruction of large-scale trafc infrastructure has become a research hotspot.
At present, the most common three-dimensional road model is the BIM model created in the design stage, which is widely used in facilities management, energy performance analysis, project planning, and other aspects [1][2][3]. However, the BIM model created in the design stage is often inconsistent with the actual construction situation, which cannot well support the follow-up management services. At the same time, some stock road networks also lack BIM design data. Terefore, it is necessary to carry out reverse three-dimensional rapid reconstruction of this road infrastructure and establish a new digital base for road infrastructure.
Tree-dimensional laser scanning technology has been widely used in high-precision digital map construction, semantic segmentation of road objects in the whole scene, and other felds [4][5][6] because of its advantages of high precision, fast speed, noncontact, and rapid acquisition of accurate three-dimensional space information of the measured object. Currently, the method of using laser scanning point cloud for road 3D measurement is also relatively mature. While providing road 3D coordinate information, intensity information, and echo information, it can efectively distinguish various targets on the road and provide high-precision and high-density basic data for road 3D fne reduction.
Some scholars have carried out relevant research on road feature extraction, which is based on the characteristics of road 3D laser scanning data. Fang et al. [7] proposed an attention model of vehicle laser point cloud line classifcation map based on spatial context information. Te attention mechanism is constructed based on the geometry, topology, and spatial structure of the line to dynamically update the node characteristics and realize the fne classifcation and vector extraction of the line. A road boundary extraction method of vehicle-borne laser point cloud based on discrete point Snake is put forward [8], which realized the extraction of road boundary with diferent shapes in various complex urban environments. In addition, the existing LiDAR360, MultiPointCloud, TopoDOT, and other software can also realize the road three-dimensional target feature extraction, which provides a new fne data support for road automation modeling [9]. It solves the problem that the discrete point cloud itself does not have semantic information [10,11], and it cannot be based on the spatial coordinates of point cloud data and other attributes, such as intensity and color to reach semantic understanding, recognition of structured semantics, and classifcation information. However, there are relatively few studies on 3D rapid reconstruction of extracted feature data. Elberink [12] proposes an automated 3D modelling method for complex road interchanges based on airborne point cloud data combined with 2D topographic map data to automatically acquire 3D topological objects. Wang et al. [13] uses 2D GIS road centreline data to generate a 3D road network model, but it requires high data accuracy and is less adaptable for roads with certain undulations or width variations. Cura et al. [14] generated a road model, which is based on GIS data. It can generate a road network according to the road axis and generate multilevel roads with diferent widths according to custom materials. Wilkie et al. [15] proposed an efective method to enhance the road map from the GIS database. It can quickly create a 3D road model with consistent geometry and topology, which provide important road features for trafc simulation. Although the above two methods can quickly generate the road model with the help of simple point-line characteristics, the accuracy of the model is low. Besides, the general adaptability to undulating roads or complex intersections is not ignored.
In view of the above problems, this paper designs a levels of detail (LOD) model for road target reconstruction based on the vehicle laser point cloud data and the structural distribution characteristics of the target in the road scene. Te required feature data are extracted according to the reconstruction details of the model to realize the threedimensional reverse modeling of the road.

Road LOD Model Design
2.1. Feature Description of Ground Objects. Road scenes are complex and diverse, involving the geometric position, feature shape, and relationship of multiple ground objects. Terefore, before the road model reconstruction, it is necessary to decompose the road into independent model objects and determine the feature data of each target. Te ground feature data mainly include the spatial distribution characteristics and geometric structure characteristics of the target [16]. Spatial characteristics include the overall distribution of ground objects in three-dimensional scenes, the geographical location and orientation of ground objects, and the topological relationship between objects. A geometric feature is the surface geometry of various geographic entities, such as size, structure, etc. Specifc features are shown in Table 1.

LOD Model Design.
Te traditional 3D data model abstracts the spatial object into four spatial geometric types, namely point, line, surface, and body, with few concepts of multilevel details. With the wide application of 3D models, the real performance of more and more complex scenes poses challenges to the system's hardware and software performance, and there is also a higher demand for manpower, material resources, and other costs. It is difcult to establish a general LOD model with a series of scales to meet diferent application requirements. Te feasible way is to design a specifc LOD model for specifc application requirements [17].
Te key problem in establishing a LOD model is to determine the reasonable level of detail content, including the level of detail, precision, and modelling method. In diferent BIM standards, building information is usually organized in a layered structure. In the city geographic markup language (CityGML) standard all models can be divided into fve diferent levels of coherence detail (LOD), and with the improvement of the level of detail, we can get more details about geometry and theme. It can not only show the graphic appearance of the city model but also take into account the semantic expression [18]. Te urban 3D geographic information model data standard divides the model into LOD1-4 four levels. LOD1 level is the super fne model, whose fne degree is the highest, the LOD4 level is the frame model, whose fne degree is the lowest. Te model manifestation divides into three kinds: the detail model performance, the main model performance, and the standard model performance three kinds. However, there is no detailed description of the road details in the existing standards.

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Terefore, combined with the distribution characteristics of road target structure and the actual modelling requirements, this paper designs a multidetail level modelling specifcation for road. Te model is divided into agent model, the standard model, and detail model. According to the structural integrity of the overall model, the road detail level is divided into LOD1, LOD2, and LOD3 levels, corresponding to three discrete models which are the road network model, road rough model, and road fne model. Te extraction and modelling rules of multilevel point cloud feature data obtained on demand are formed to reduce unnecessary vertices and patches redundancy, reduce the amount of model data, and efectively support the rapid modelling of road 3D models at diferent levels of detail. Te efect is shown in Figure 1.
Among them, LOD3 level requires the highest fneness of the model, while ensuring the fneness of a single model size, considering the structural integrity of the whole road scene. Terefore, each target model requires detailed model performance, which is suitable for fne management and analysis of a single road. Te LOD2 tier has been reduced in terms of individual model size granularity and overall model structural integrity, retaining the detailed models of road surfaces, pavements, and green belts in the LOD3 tier but with a slightly lower model size granularity, with kerbs, guardrails, markings, signage, and street lights suitable for 3D visualisation of roads in larger scenes. Te LOD1 level has the lowest precision, which only retains the three main features of road surface, curb stone, and sidewalk in the LOD2 level, and is suitable for the three-dimensional visualization of road model at a large-scale road network level. In addition, based on the model manifestations at diferent LOD levels, the structural data needed for modeling are further clarifed, such as node spacing, main structure, detail structure, and other parameters. Te detailed design of the LOD model is shown in Table 2.

Feature Extraction.
In order to realize the one-time extraction of road space feature data and a variety of demand production, it is necessary to extract the fnest feature data structure in the road scene based on the point cloud data processing software, establish the feature database, and output the feature data of various targets to the standardized modeling data after preprocessing the 3D laser scanning data.
According to the structural feature data extraction requirements of each target in the road scene, it is necessary to preprocess the vehicle three-dimensional laser scanning data, including point cloud denoising, point cloud segmentation, point cloud strength enhancement, etc. Ten extract all kinds of feature data based on the point cloud, mainly divided into point target, line target, and surface target. Te point target mainly includes the supporting pole and street lamp pole of the sign board. It is necessary to extract the spatial position, azimuth, and height information and store the information in the GIS vector point layer data. Line target mainly includes road boundary, green belt and sidewalk boundary, curb, etc. It is necessary to extract three-dimensional boundary line, target height, and other information and save it in GIS vector line layer data. Te surface target mainly includes the marking line and the label contour. Te closed line is collected in the three-dimensional scene, extracted by converting it into surface data, and saved in the GIS vector surface layer data. Te efect is shown in Figure 2. Finally, according to diferent LOD requirements, the GIS vector data is outputted as structural modeling feature data to support automatic modeling.
In the fgure, the white vector line is the boundary of the surface features, the contour of the road marking line, and the contour of the signboard; the blue represents various types of support rods and the green represents various types of equipment.

Consecutive Quadrilateral Pavement Reconstruction.
In order to solve the problems of difcult elevation control, large error, and low efciency in manual modeling of pavement, this section aims to analyze the data structure of the pavement model and realize the automatic modeling of pavement at diferent LOD levels. Te road plane alignment structure and road elevation information are combined, and the simple three-dimensional road alignment is composed of the edge line and the road center line. Te continuous quadrilateral reconstruction of pavement and the smoothing method for curves are given.
Consecutive quadrilateral reconstruction is the process of constructing a quadrilateral mesh by grouping the three-dimensional point data on both sides of the road and connecting them sequentially according to the closed quadrilateral rules. As shown in Figure 3, the outer edge points of the upper and lower roads are stored in arrays A and B, respectively. Te data of the above rows are taken as an example. Firstly, the spline line is created from the starting point i � 1, and the points A [1], A [2], B [2], and B [1] are connected into a quadrilateral, i � i + 1. Ten traverse the remaining points in turn until the completion of the last set of data quadrilateral connections; fnally, the spline line is transformed into an editable polygon and the actual pavement material is given. Te principle of downward road reconstruction is the same as that of upward road.
In the modeling of curved roads or road intersections, there may be unsmooth phenomena in the curved area. In order to improve the visual efect of the model, this paper interpolates the data of the road side line and the center line by solving the circular parameters of the curved space to achieve a smooth curved road. As shown in Figure 4, a, b, and c are three points on the center line of the bend; O is the center of the space circle where the three points are; R is the radius of the space circle; α1, α2, and α3 are the three azimuth angles from the center O to the center line points a, b, and c, respectively; and the cross points between a, b, and c are the encrypted interpolation points on the circular curve (see Algorithm 1).

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Te specifc smoothing algorithm is as follows:

Tree-Dimensional Linear Design of Intersection.
For intelligent transportation and high-precision navigation application services, it is necessary to accurately extract road-connected intersections for road digitalization and interrupt at road corners. Road intersections usually include T-shaped intersection, Y-shaped intersection, and cross intersection. In highway engineering design specifcations, road intersections are generally composed of straight roads, curved roads, and quasi-triangles. Te key to the design of these components is the middle line of the driving lane. Based on this, this paper designs an intersection segmentation and reconstruction structure as the basic structure of road three-dimensional linear reconstruction. As shown in Figure 5, the center point of the cross intersection is set as O, A, B, C, and D are the center points of the intersection and the common side of the straight road, and a, b, c, and d are the road confict points. A, a, and B are regarded as the three points on the circle with O′ as the center, and O′A as the radius, and they are connected by curves to form a circular curve AB. Te points with the same properties are the same. Intersections are divided into eight parts I-VIII, of which parts I-IV are a triangle-like region surrounded by a circular curve and two straight lines, and parts V-VIII is a four-section curved road.
Te plane structures of a T-shaped intersection, Y-shaped intersection, and cross-shaped intersection are similar. Tshaped intersection is divided into fve parts I-V. Region I is a straight road; regions II and III are triangular regions; and regions IV and V are curved roads. Y-shaped intersection is divided into six parts I-VI. Regions I, II, and III are curved roads, and regions IV, V, and VI are triangular regions.

Topological Structure Relation Reconstruction.
According to the 3D linear structure design of the three intersections, each intersection is divided into several curved roads A, triangular area B and straight roads C, and processed, respectively, as shown in Figure 6(a). Cross intersections consist of four A regions and four B regions, T intersections consist of two A, two B, and one C regions and Y intersections consist of three A regions and three B regions, as shown in Figure 6 6 Advances in Civil Engineering

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Te specifc algorithm steps are as follows (see Algorithm 2):

Linear Model Lofting
Modeling. Curbs, guardrails, and other linearly dispersed targets have topological relationships that are vertically next to the road surface, with sections that are either rectangular or circular in the vertical direction and an overall direction that varies with the undulation of the road surface. In order to avoid the problem of manual interactive capture of wrong nodes leading to rework, the linear model lofting modeling technology based on characteristic lines and local monomers is studied to achieve rapid reconstruction of the target. Tis reconstruction method duplicates a 2D body object or monolithic model along a 3D reference line that can be set in diferent shapes on diferent road segments. Te use of linear lofting technology can realize the rapid construction of a large number of linear models, which can better adapt to the data reconstruction and processing of bending and ramp positions. In this paper, the section of the curb linear target is used as the two-dimensional shape of lofting, and the outer edge of the road is used as the lofting reference path to automatically construct the target model. Te edge cutting angle is automatically adjusted to make the model more realistic and ensure the seamless connection between the model and the road surface. Te principle is shown in Figure 7.
Te specifc algorithm steps are as follows (see Algorithm 3):

Instance Modeling of the Single Model.
For dotted ground objects such as street lights, sign street lamp and so on, frstly, the models of facilities, equipment and signs are constructed based on the target feature structure data. Ten, batch replication and rotation are carried out based on the three-dimensional coordinates X, Y, Z and azimuth α.
Finally, the actual texture map is given to each sign, and the principle is shown in Figure 8.
Te specifc algorithm steps are as follows (see Algorithm 4):

Application of the Modeling Scene.
In this paper, two sets of point cloud scanning data under diferent scenes obtained by vehicle mobile measurement system are used for experiments. Te average point spacing of the experimental data is 5-10 cm. As shown in Figure 9, the point cloud data of road 1 contains 6312383 points; the road is relatively fat; as shown in Figure 10, the point cloud data of road 2 contains 4449024 points, which is a steep slope section, and the slope is about 4%, as shown in Figure 11, the point cloud data of road 3 contains 2591106 points, which are highway sections.
Te road three-dimensional laser scanning data is imported into the multi-source point cloud data processing software for fltering and other preprocessing, and the required feature data are extracted from the high-precision and high-density vehicle laser point cloud data. Ten based on 3Ds Max development platform, using the proposed method, the LOD3 level road model is automatically established, and two angles are selected to show the reconstruction efect. Experiments show that this method can quickly reconstruct a fat road scene, and it is also applicable to roads with a certain slope. Te reconstruction rate is 100%. Te results are shown in Figures 12-14.
In order to further verify the feasibility of this method, according to the detailed rules of road LOD model in Table 2, based on the road 1 point cloud data to extract structured feature data, using continuous quadrilateral pavement reconstruction, intersection topology structure relationship reconstruction and 3D release technology method, and the road target models at LOD1, LOD2 and LOD3 levels are established respectively. Te models are compared under the wireframe model and the real model, as shown in Figure 15.

Modeling Quality Evaluation.
In order to verify the accuracy of the model, the number of vertices, edges, polygons and triangles of the models with diferent levels of fneness are counted in the experiment, and the specifc statistics are shown in Table 3. Te LOD1 level model has the  Advances in Civil Engineering highest precision, LOD2 and LOD3 level model vertex number, edge number decreased in turn, precision is also reduced.
In addition to the detailed features of the statistical model, this paper uses the Hausdorf Distance Method [19] to calculate the similarity between the fltered point cloud data and the road 1 model at diferent levels. Te statistical results are shown in Figure 16. After calculation, when the model fneness level is LOD3, the nearest point distance of 5818,123 data points in the point cloud of road 1 is between ±10 cm, accounting for 92.17% of the total number of point clouds. When the model fneness level is LOD2, the nearest

Conclusions
In this paper, a fast 3D reverse modeling method for roads based on vehicle-borne laser scanning is proposed. Tis method is based on a variety of features of road 3D laser scanning data and designs a specifc LOD model for road targets as the reconstruction details specifcation. With the help of point cloud processing software, the process from discrete point cloud data without semantic information to structured feature data is realized. Finally, the road 3D models with diferent levels of fneness are quickly established. Compared with the existing research, this paper applies the road data of vehicle laser scanning to the digital construction of trafc infrastructure and proposes a new LOD conceptual model for road targets, which is used for accurate expression, on-demand customization and personalized service applications. Te three key modeling technologies proposed in this paper can achieve high-precision automatic reconstruction of road models, adapt to diferent types of road scenes, and provide a feasible technical route for realizing the full-element and full-cycle digitalization of trafc infrastructure. However, there are also some limitations, specifcally the following: (1) Te road intersection and reconstruction algorithm explored in this research only focuses on three simple and popular intersection types, without taking into account additional roads with complicated structures in depth. Furthermore, when the intersection and the pavement joint are shared, the junction locations must be shared, and the reconstruction data format is highly necessary. To address this issue, the terrain reconstruction algorithm can be included in future work, and the intersection road network model can be generated automatically based on the intersection feature structure data, improving reconstruction efciency. (2) Te instanced reconstruction algorithm of point targets examined in this research is only applicable to targets of the same specifcation for facilities and equipment, and grouping reconstruction is required for diferent types of facilities, which impacts modeling efciency. To address this issue, we may incorporate the model size scaling technique into future research, allowing us to fully utilize the feature structure data, automatically scale the model, and restore a more realistic and detailed road scene.

Data Availability
Some necessary research data can be disclosed. If necessary, please send an e-mail to liurufei_2007@126.com.

Conflicts of Interest
Te authors declare that there are no conficts of interest.  Advances in Civil Engineering 13