Road model generation method, system, terminal and medium

Abstract

The application discloses a road model generation method, system, terminal and medium, relates to the technical field of three-dimensional modeling, and comprises the following steps: acquiring a road center line point sequence and a road age parameter; creating a path node, configuring attributes of the path node according to the sequence and the road age parameter; creating a road surface node, acquiring a road surface width parameter, a road surface type parameter and a road surface thickness parameter, generating a road surface grid in combination with the center line attributes and associating the road surface grid to the road surface node as geometric body data, and configuring attributes of the road surface node according to the road surface type parameter; creating a detail node, acquiring a style label, determining a target detail type and a target detail generation weight in combination with the road age attribute, determining a coordinate set of a target detail placement point according to the road surface grid, the target detail type and the target detail generation weight, and configuring attributes of the detail node according to the target detail type and the coordinate set of the target detail placement point; and generating a three-dimensional road model through rendering according to the road surface node and the detail node.

Description

Technical Field

[0001] This application relates to the field of 3D modeling technology, and in particular to a method, system, terminal and medium for generating road models. Background Technology

[0002] With the rapid development of digital twins and 3D Geographic Information Systems (GIS), city modeling has become crucial for constructing virtual geographic environments. As roads are one of the essential infrastructure components of a city, road modeling is also receiving increasing attention.

[0003] Currently, data-driven reconstruction techniques are commonly used to generate road models. Specifically, data collected from remote sensing imagery is used to fully restore the texture and geometry of the road. The problem with existing technologies is that, when generating road models using data-driven reconstruction techniques, the essence is to digitally record the road's appearance at a specific moment based on data from that moment. The resulting road model is entirely dependent on the corresponding remote sensing imagery, which is not conducive to flexibly generating the required road model.

[0004] Therefore, the relevant technologies still need to be improved and developed. Summary of the Invention

[0005] The main purpose of this application is to provide a road model generation method, system, terminal and medium, which aims to solve the technical problem that when using data-driven reconstruction technology to generate road models in related technologies, the final generated road model depends entirely on the corresponding remote sensing image, which is not conducive to flexibly generating the required road model.

[0006] To achieve the above objectives, the first aspect of this application provides a road model generation method, wherein the method includes: Obtain the road centerline point sequence and road age parameters; Create path nodes and configure centerline and road age attributes for the path nodes based on the above road centerline point sequence and the above road age parameters. Create lower-level road surface nodes for the above path nodes, obtain road surface width parameters, road surface type parameters, and road surface thickness parameters, generate road surface mesh based on the above centerline attributes, the above road surface width parameters, and the above road surface thickness parameters, associate the above road surface mesh as geometric data with the above road surface nodes, and configure road surface type attributes for the above road surface nodes according to the above road surface type parameters. Create lower-level detail nodes for the above road surface nodes, obtain style tags, determine the target detail type and the target detail generation weight corresponding to each of the above target detail types based on the above style tags and the above road age attribute, determine the coordinate set of the target detail placement point corresponding to each target detail type based on the above road surface mesh, the above target detail type and the above target detail generation weight, and configure detail identification attributes for the above detail nodes based on the above target detail type and the coordinate set of the above target detail placement point. Based on the geometric data and road type attributes associated with the aforementioned road surface nodes, as well as the detail identifier attributes associated with the aforementioned detail nodes, a three-dimensional road model is generated through rendering.

[0007] Optionally, the above-mentioned generation of the road surface mesh based on the centerline attribute and the road surface width parameter includes: Lateral geometric extrusion is performed along the road centerline indicated by the aforementioned centerline attributes to generate a continuous road surface mesh with a width matching the aforementioned road surface width parameters.

[0008] Optionally, the above-mentioned acquisition of style tags, and determination of target detail types and corresponding target detail generation weights based on the style tags and road age attributes, include: Obtain the style tags input by the user, and based on the style tags, search from the preset parameter mapping rule library to determine the target detail type that matches the style tags and the target detail generation weight corresponding to the target detail type; The aforementioned preset parameter mapping rule library stores multiple preset styles, the activated detail types under each preset style, and the detail generation weights corresponding to the aforementioned detail types under different road ages.

[0009] Optionally, the aforementioned path nodes, road surface nodes, and detail nodes constitute a directed acyclic graph structure with multiple node levels from top to bottom.

[0010] Optionally, the above-mentioned directed acyclic graph structure also includes road segment nodes set between the levels of the above-mentioned road surface nodes and the above-mentioned detail nodes; The above process involves creating lower-level detail nodes for the aforementioned road surface nodes, obtaining style tags, determining the target detail type and corresponding target detail generation weights based on the style tags and road age attributes, determining the coordinate set of target detail placement points for each target detail type based on the road surface mesh, target detail type, and target detail generation weights, and configuring detail identifier attributes for the detail nodes based on the target detail type and the coordinate set of target detail placement points. Obtain the distance interval, and according to the distance interval, cut the road surface grid along the road centerline to obtain multiple independent road segment sub-grids. Based on the road segment sub-grids, create multiple independent road segment nodes at the lower level for the road surface nodes. The road segment nodes correspond one-to-one with the road segment sub-grids, and associate the road segment sub-grids as geometric data with the corresponding road segment nodes. For each road segment node, determine the detail generation density according to the geometric curvature of the road segment centerline corresponding to the associated road segment sub-grid, and configure the detail density attribute for the road segment node according to the detail generation density. For each road segment node, create a lower-level detail node for the road segment node. Determine the target detail type and the target detail generation weight corresponding to each target detail type based on the style tag and the road age attribute. Determine the coordinate set of the target detail placement point corresponding to each target detail type based on the road segment sub-grid, the detail density attribute, the target detail type, and the target detail generation weight. Configure detail identification attributes for the detail node based on the target detail type and the coordinate set of the target detail placement point.

[0011] Optionally, the aforementioned detail generation density is used to control the total number of target detail placement points under the aforementioned road segment subgrid; The aforementioned target detail generation weights are used to control the number of target detail placement points corresponding to the target detail type.

[0012] Optionally, the above determination of the coordinate set of target detail placement points corresponding to each target detail type based on the above road segment sub-grid, the above detail density attribute, the above target detail type, and the above target detail generation weight includes: The target sampling algorithm corresponding to each of the above target detail types is determined from a variety of preset sampling algorithms; For each target detail type, based on the aforementioned detail density attribute and target detail generation weight, the target sampling algorithm is used to sample and determine the target detail placement point corresponding to the aforementioned target detail type in the aforementioned road segment subgrid, and a coordinate set of the target detail placement point corresponding to the aforementioned target detail type is constructed.

[0013] A second aspect of this application provides a road model generation system, wherein the system includes: The data acquisition module is used to acquire the road centerline point sequence and road age parameters; The path node creation module is used to create path nodes and configure centerline attributes and road age attributes for the path nodes based on the above road centerline point sequence and the above road age parameters. The road surface node creation module is used to create lower-level road surface nodes for the aforementioned path nodes, obtain road surface width parameters, road surface type parameters, and road surface thickness parameters, generate a road surface mesh based on the aforementioned centerline attributes, road surface width parameters, and road surface thickness parameters, associate the aforementioned road surface mesh as geometric data with the aforementioned road surface nodes, and configure road surface type attributes for the aforementioned road surface nodes according to the aforementioned road surface type parameters. The detail node creation module is used to create lower-level detail nodes for the above road surface nodes, obtain style tags, determine the target detail type and the target detail generation weight corresponding to each of the above target detail types based on the above style tags and the above road age attribute, determine the coordinate set of the target detail placement point corresponding to each target detail type based on the above road surface mesh, the above target detail type and the above target detail generation weight, and configure detail identification attributes for the above detail nodes based on the above target detail type and the coordinate set of the above target detail placement point. The rendering module is used to generate a 3D road model by rendering based on the geometric data and road type attributes associated with the aforementioned road surface nodes, as well as the detail identifier attributes associated with the aforementioned detail nodes.

[0014] A third aspect of this application provides a terminal, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of any of the road model generation methods described above.

[0015] A fourth aspect of this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the road model generation methods described above.

[0016] As can be seen from the above, the present application provides a road model generation method, which includes: obtaining a road centerline point sequence and road age parameters; creating path nodes, configuring centerline attributes and road age attributes for the path nodes according to the road centerline point sequence and road age parameters; creating lower-level pavement nodes for the path nodes, obtaining pavement width parameters, pavement type parameters, and pavement thickness parameters, generating a pavement mesh based on the centerline attributes, pavement width parameters, and pavement thickness parameters, associating the pavement mesh as geometric data with the pavement nodes, and configuring pavement type attributes for the pavement nodes according to the pavement type parameters. The process involves: creating lower-level detail nodes for the aforementioned road surface nodes; obtaining style tags; determining the target detail type and corresponding target detail generation weights based on the style tags and road age attributes; determining the coordinate set of target detail placement points for each target detail type based on the road surface mesh, target detail type, and target detail generation weights; configuring detail identifier attributes for the detail nodes based on the target detail type and target detail placement point coordinate set; and generating a 3D road model through rendering based on the geometric data and road surface type attributes associated with the aforementioned road surface nodes, as well as the detail identifier attributes associated with the aforementioned detail nodes.

[0017] Therefore, this application's approach to road model generation does not simply rely on static reconstruction of remote sensing imagery at a specific moment. Instead, it abstracts the road into a computable and editable object-oriented representation by constructing a hierarchical node structure (including path nodes, road surface nodes, and detail nodes). Simultaneously, it introduces style tags and road age parameters as stylization drivers, ensuring that the selection of detail types and the determination of generation weights are no longer fixed data recovery processes but are dynamically calculated based on semantic input. Finally, the geometric data associated with hierarchical nodes and detail identifier attributes are jointly rendered to generate a 3D road model. This allows the generated road model to no longer be entirely dependent on remote sensing imagery data at a specific moment, but to flexibly generate road models with differentiated visual expressions based on different style semantics and road age parameters. This effectively overcomes the shortcomings of traditional data-driven reconstruction methods in terms of generation flexibility and controllability, facilitating the flexible generation of the required road models. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1This is a schematic flowchart of a road model generation method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the constituent modules of a road model generation system provided in an embodiment of this application; Figure 3 This is a block diagram illustrating the internal structure of a terminal provided in an embodiment of this application. Detailed Implementation

[0020] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0021] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0022] It should also be understood that the terminology used in this application specification is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this application specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to classification to [the described condition or event]."

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0026] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0027] Currently, the visual realism of road modeling directly determines the immersiveness and practicality of 3D GIS systems. Early road modeling methods mainly relied on manual geometric modeling and texture mapping techniques. While these methods could meet basic requirements for morphological expression and spatial accuracy, they often neglected the realistic road appearance details shaped by long-term use and diverse environmental factors. These limitations resulted in models that often failed to describe spatiotemporal context and struggled to support controllable stylized road generation schemes.

[0028] In existing technologies, data-driven reconstruction techniques are typically used to generate road models. For example, data collected from remote sensing imagery, street view images, or mobile mapping systems can be used to fully restore the texture and geometry of roads. However, this technology can only generate static road models at a specific moment. These models lack deep semantic descriptions and cannot reflect the road material properties, functional roles, and long-term morphological evolution under environmental and traffic influences. The models also have extremely poor reusability and modifiability. Furthermore, the final generated road model is entirely dependent on the collected data (e.g., remote sensing imagery), which is not conducive to flexibly generating the required road models.

[0029] In some application scenarios, rule-based generation techniques can be used to generate road textures and geometric shapes by relying on predefined rule sets such as shape grammars and fractal systems. However, this technique only focuses on topological consistency and macroscopic geometric reconstruction, and cannot simulate the uneven and gradual changes in roads under the coupled effects of traffic load, climate exposure, and material aging. The generated road model textures are neat but repetitive, lacking the visual texture of real-world scenes.

[0030] Therefore, existing road modeling technologies cannot simultaneously achieve both physical logic and visual realism, making it difficult to realize a road stylization modeling scheme that is both highly realistic and parametrically controllable.

[0031] To address at least one of the aforementioned technical problems, this application proposes a road model generation method. The method includes: obtaining a road centerline point sequence and road age parameters; creating path nodes and configuring centerline and road age attributes for the path nodes based on the road centerline point sequence and road age parameters; creating lower-level pavement nodes for the path nodes, obtaining pavement width, pavement type, and pavement thickness parameters, generating a pavement mesh based on the centerline attributes, pavement width, and pavement thickness parameters, associating the pavement mesh as geometric data with the pavement nodes, and configuring pavement type attributes for the pavement nodes based on the pavement type parameters. For the aforementioned road surface nodes, create lower-level detail nodes, obtain style tags, determine the target detail type and the corresponding target detail generation weight based on the style tags and the road age attribute, determine the coordinate set of the target detail placement points for each target detail type based on the road surface mesh, the target detail type, and the target detail generation weight, configure detail identifier attributes for the aforementioned detail nodes based on the target detail type and the coordinate set of the target detail placement points, and generate a 3D road model through rendering based on the geometric data and road surface type attributes associated with the aforementioned road surface nodes, as well as the detail identifier attributes associated with the aforementioned detail nodes.

[0032] Therefore, this application's approach to road model generation does not simply rely on static reconstruction of remote sensing imagery at a specific moment. Instead, it abstracts the road into a computable and editable object-oriented representation by constructing a hierarchical node structure (including path nodes, road surface nodes, and detail nodes). Simultaneously, it introduces style tags and road age parameters as stylization drivers, ensuring that the selection of detail types and the determination of generation weights are no longer fixed data recovery processes but are dynamically calculated based on semantic input. Finally, the geometric data associated with hierarchical nodes and detail identifier attributes are jointly rendered to generate a 3D road model. This allows the generated road model to no longer be entirely dependent on remote sensing imagery data at a specific moment, but to flexibly generate road models with differentiated visual expressions based on different style semantics and road age parameters. This effectively overcomes the shortcomings of traditional data-driven reconstruction methods in terms of generation flexibility and controllability, facilitating the flexible generation of the required road models.

[0033] like Figure 1 As shown in the embodiments of this application, a road model generation method is provided. Specifically, the method includes the following steps: Step S100: Obtain the road centerline point sequence and road age parameters; Step S200: Create path nodes and configure centerline attributes and road age attributes for the path nodes according to the above road centerline point sequence and the above road age parameters. Step S300: Create a lower-level road surface node for the above path node, obtain the road surface width parameter, road surface type parameter and road surface thickness parameter, generate a road surface mesh based on the above centerline attribute, the above road surface width parameter and the above road surface thickness parameter, associate the above road surface mesh as geometric data with the above road surface node, and configure the road surface type attribute for the above road surface node according to the above road surface type parameter. Step S400: Create lower-level detail nodes for the above road surface nodes, obtain style tags, determine the target detail type and the target detail generation weight corresponding to each of the above target detail types based on the style tags and the above road age attribute, determine the coordinate set of the target detail placement points corresponding to each target detail type based on the above road surface mesh, the above target detail type and the above target detail generation weight, and configure detail identification attributes for the above detail nodes based on the above target detail type and the coordinate set of the above target detail placement points. Step S500: Based on the geometric data and road type attributes associated with the above road surface nodes, and the detail identifier attributes associated with the above detail nodes, a three-dimensional road model is generated through rendering.

[0034] The aforementioned road centerline point sequence is a three-dimensional spatially ordered set of points used to define the road's geometric skeleton, serving as the spatial reference for subsequent generation of the pavement mesh and calculation of detail distribution. The aforementioned road age parameter is a time parameter input by the user, used to characterize the number of years the road has been in use, and influences the generation weight of aging details (such as cracks, potholes, etc.) during the generation stage.

[0035] It should be noted that in this embodiment, a predefined topology structure is defined, including multiple node levels from top to bottom. For example, it may include path nodes, road surface nodes, and detail nodes. In this embodiment, the path nodes, road surface nodes, and detail nodes constitute a directed acyclic graph (DAG) structure with multiple node levels from top to bottom. The relationship between nodes in each layer is a directed dependency: the attributes of the upper-level nodes serve as input parameters for the generation of lower-level nodes, and the edges are unidirectional from top to bottom without loops, thus forming a directed acyclic graph. The role of the DAG is to organize the parameter transfer relationship between nodes in each layer, ensuring that style parameters are injected from top to bottom. Changes in the attributes of upper-level nodes will drive the regeneration of their lower-level nodes, but changes in the attributes of lower-level nodes will not affect the upper-level nodes in the reverse direction. Each layer is configured independently, and the local parameters of a certain layer (such as the number of lanes in a road segment or the random seed of details) can be adjusted individually without redefining the node generation logic of other layers.

[0036] Specifically, based on the acquired road centerline point sequence and road age parameters, top-level path nodes are created, and centerline and road age attributes are configured for these path nodes. The centerline attribute is an ordered sequence of points in three-dimensional space, serving as the benchmark for all subsequent road geometry calculations; the road age attribute is a quantified value used to characterize the degree of road aging. It should be noted that other attributes can also be set for path nodes, such as road markings, which can be input by the user or retrieved from GIS data to establish a unique link with external data; these are not specifically limited here.

[0037] Furthermore, lower-level road surface nodes are created for the path nodes, reading the road surface width, thickness, and type parameters input by the user. Lateral geometric extrusion is performed along the road centerline corresponding to the centerline attribute, generating a continuous road surface mesh with a width matching the road surface width parameter and a thickness matching the road surface thickness parameter. The geometric data of the road surface mesh is associated with the road surface nodes. Simultaneously, road surface type attributes are configured for the road surface nodes according to the road surface type parameters. Road surface types include asphalt, concrete, rubber, etc., used for matching the corresponding base materials and textures in subsequent rendering stages.

[0038] In a specific application scenario, using the centerline point sequence of path nodes, user-preset road width, and road thickness as input, a continuous road surface mesh with realistic cross-sectional volume is generated by performing lateral geometric extrusion along the centerline combined with cross-sectional expansion in the thickness direction. Road width and thickness are only input parameters during the mesh generation stage; after generation, they are fully reflected in the geometric data of the road surface mesh and are no longer retained as persistent attributes of the road surface nodes. The attributes of the road surface nodes are global attributes of the entire node set, including only the road surface type (preset by the user).

[0039] Specifically, the process of obtaining style tags, determining target detail types and corresponding target detail generation weights based on these style tags and road age attributes, includes: Obtain the style tags input by the user, and based on the style tags, search from the preset parameter mapping rule library to determine the target detail type that matches the style tags and the target detail generation weight corresponding to the target detail type; The aforementioned preset parameter mapping rule library stores multiple preset styles, the activated detail types under each preset style, and the detail generation weights corresponding to the aforementioned detail types under different road ages.

[0040] The system retrieves user-input style tags, which can be at least one of several preset styles, including new, moderately aged, old, rainy, snowy, industrial, rural, and park. It then calls a preset parameter mapping rule library to retrieve target detail types that match the current style tag, along with the initial target detail generation weights for each type. Simultaneously, it can adaptively fine-tune the generation weights for aging details such as cracks, deformation, potholes, looseness, and oil seepage by incorporating the road age attribute of path nodes. The older the road, the higher the weight of aging details, ultimately determining the target detail generation weights for each type. The parameter mapping rule library presets the detail activation states and generation weights for various styles and can automatically filter contradictory detail combinations; for example, it automatically disables snow accumulation detail generation permissions under the high-temperature style.

[0041] It should be noted that, in the embodiments of this application, the above-mentioned directed acyclic graph structure also includes road segment nodes set between the levels of the above-mentioned road surface nodes and the above-mentioned detail nodes; The above process involves creating lower-level detail nodes for the aforementioned road surface nodes, obtaining style tags, determining the target detail type and corresponding target detail generation weights based on the style tags and road age attributes, determining the coordinate set of target detail placement points for each target detail type based on the road surface mesh, target detail type, and target detail generation weights, and configuring detail identifier attributes for the detail nodes based on the target detail type and the coordinate set of target detail placement points. Obtain the distance interval, and according to the distance interval, cut the road surface grid along the road centerline to obtain multiple independent road segment sub-grids. Based on the road segment sub-grids, create multiple independent road segment nodes at the lower level for the road surface nodes. The road segment nodes correspond one-to-one with the road segment sub-grids, and associate the road segment sub-grids as geometric data with the corresponding road segment nodes. For each road segment node, determine the detail generation density according to the geometric curvature of the road segment centerline corresponding to the associated road segment sub-grid, and configure the detail density attribute for the road segment node according to the detail generation density. For each road segment node, create a lower-level detail node for the road segment node. Determine the target detail type and the target detail generation weight corresponding to each target detail type based on the style tag and the road age attribute. Determine the coordinate set of the target detail placement point corresponding to each target detail type based on the road segment sub-grid, the detail density attribute, the target detail type, and the target detail generation weight. Configure detail identification attributes for the detail node based on the target detail type and the coordinate set of the target detail placement point.

[0042] The aforementioned distance interval is used to control the interval of road segment division and can be set or adjusted by the user according to actual needs, without specific limitations. In this embodiment, by cutting continuous road surfaces into independent road segment nodes, fine-grained control is achieved to assign differentiated style parameters and detail density (such as automatically assigning higher weight to curves than straight sections) to different sections, enabling the road model to present physical wear characteristics that vary with spatial location on the same road network. Simultaneously, it supports independent style adjustment and detail regeneration for any section without regenerating the entire road, improving the flexibility and editing efficiency of road model generation.

[0043] The aforementioned detail identifier attribute is used to map each target detail type to a unique entry in the preset decal asset library, enabling each detail node to call the corresponding decal resource from the asset library during rendering.

[0044] In some specific application scenarios, the road surface mesh and user-preset distance intervals are used as input. The road surface mesh is longitudinally cut along the centerline at preset distance intervals to generate several independent road segment sub-meshes. Each road segment sub-mesh is matched with an independent road segment node. After generating the road segment node, the road segment node exists as an independent geometric unit, and the original road surface node is no longer used as the source of rendering geometry when road segment expansion is enabled. The system automatically calculates the geometric curvature of each road segment node (derived from the degree of curvature of the road segment's centerline point sequence) and automatically assigns a corresponding detail generation density based on the curvature (the greater the curvature, the higher the detail generation density).

[0045] It should be noted that the road segment node is an optional extension level. When enabled, the road surface node only serves as the geometric parent body before the road segment node is cut, and the generation of detail nodes is driven by the road segment node; when disabled, the road surface node directly drives the generation of detail nodes.

[0046] It should be further explained that other attributes can also be set for road segment nodes, such as the number of lanes and lane ID (preset by the user, which can realize independent control of multi-lane roads), and lane width (preset by the user, which allows for flexible expression of widened road segments), etc., which are not specifically limited here.

[0047] Specifically, the aforementioned detail generation density is used to control the total number of target detail placement points under the aforementioned road segment sub-grid; The aforementioned target detail generation weights are used to control the number of target detail placement points corresponding to the target detail type.

[0048] It's important to note that the detail generation density and the target detail generation weight work together to control the final number of road details generated. Specifically, the detail generation density is a global control parameter at the road segment level. Its value depends on the geometric curvature of the road segment's centerline; the greater the curvature, the higher the value. It controls the total number of detail placement points of all types within that road segment. It determines how many details are generated for the entire road segment, reflecting the physical law that curves experience more wear. The target detail generation weight, on the other hand, is a type-level individual allocation parameter. It allocates a proportionate share of each specific detail type (such as cracks or water accumulation) within the determined total detail quantity, determining how many of these specific types can be allocated. Multiplying the two yields the effective generation density for that type within the road segment: the detail generation density uniformly amplifies or reduces the total (controlling the total number), while the target detail generation weight determines the individual share of specific types (controlling the composition). This ensures that the total detail quantity adapts to the road's curvature while maintaining the physical proportions of various details under different styles, thus achieving a unity of visual realism and physical consistency.

[0049] It should be further noted that, without dividing the road into segments, the density of detail generation for the entire road can be considered the same, but no specific limitation is made here.

[0050] Specifically, based on the aforementioned road segment sub-grid, the aforementioned detail density attribute, the aforementioned target detail type, and the aforementioned target detail generation weight, the coordinate set of target detail placement points corresponding to each target detail type is determined, including: The target sampling algorithm corresponding to each of the above target detail types is determined from a variety of preset sampling algorithms; For each target detail type, based on the aforementioned detail density attribute and target detail generation weight, the target sampling algorithm is used to sample and determine the target detail placement point corresponding to the aforementioned target detail type in the aforementioned road segment subgrid, and a coordinate set of the target detail placement point corresponding to the aforementioned target detail type is constructed.

[0051] Among them, the aforementioned preset sampling algorithms are sampling algorithms pre-set for different detail types. Taking the attributes of the road segment node (including the injected target detail type set and detail generation weight) as input, the detail generation algorithm is called to calculate the spatial coordinates of the decal placement point within the road surface area of ​​each road segment node, and each placement point is a detail node.

[0052] In some application scenarios, the attributes of detail nodes include: distribution pattern (e.g., point distribution, line distribution, polygon distribution, etc., determined by the type of algorithm called), detail identifier (mapped to a specific entry in the decal asset library, determined by the injected detail type), and random seed (automatically generated by the system to ensure the reproducibility and controllable variability of the results).

[0053] During final rendering, the rendering engine reads the geometric mesh and material sources (road surface nodes when road segment expansion is disabled, and individual road segment nodes when road segment expansion is enabled), as well as the decal placement points and decal parameters provided by the detail nodes, together forming the final rendering output of the 3D road model. Road surface nodes and road segment nodes play the same functional role in rendering, and are used interchangeably. When road segment expansion is disabled, the geometric mesh and materials provided by the road surface nodes are used; when road segment expansion is enabled, the geometric mesh and materials provided by the segment nodes obtained from the cut-out process are used. Road surface nodes and road segment nodes do not overlap in the rendering process.

[0054] It should be noted that, in this embodiment, the specific explanation is based on enabling road segment nodes, generating detail nodes based on road segment nodes, and determining the placement position of each detail. In actual applications, if road segment nodes are not enabled, detail nodes can be directly generated based on the road surface stage, and the placement position of each detail can be determined.

[0055] Specifically, in this embodiment of the application, 11 detail types in 5 categories are pre-constructed, as shown in Table 1 below.

[0056] Table 1

[0057] As shown in Table 1, the detailed classifications specifically include cracks (including block cracks, strip cracks, and alligator cracks, caused by tensile stress or aging), deformations (including depressions and bulges, caused by material subgrade damage), potholes (an advanced stage of localized material loss), defects (including loosening, bleeding, and rutting, representing wear layer degradation), and weather (including water accumulation and snow accumulation, reflecting transient surface changes caused by weather factors). However, the above detailed classifications and specific detailed type divisions are not intended as specific limitations.

[0058] In some application scenarios, the preset parameter mapping rule library directly stores multiple style tags. For each style tag, it stores the corresponding activated target detail type. For each target detail type, it directly stores the target detail generation weights corresponding to different road ages.

[0059] In other application scenarios, the preset parameter mapping rule base directly stores multiple style tags. For each style tag, it stores the corresponding activated target detail type. For each target detail type, it stores only one target detail generation weight. However, for this target detail generation weight, road age can be further used as an adjustment factor to adjust the target detail generation weight based on road age.

[0060] In this embodiment, the user pre-builds a parameter mapping rule library offline to establish a mapping relationship between style tags, target detail type sets, and corresponding detail generation weights. Table 2 shows the correspondence between styles and detail types built in this embodiment.

[0061] Table 2

[0062] In Table 2, numbers 1-11 correspond to the IDs of detail types in Table 1. As shown in Table 2, the rule base presets multiple style tags based on three physical dimensions: aging, environment, and function. Each style tag corresponds to an activated subset of detail types (activated by presence or absence, with presence indicating activation) and its detail generation weight (used to control the generation density of that detail type in the road surface area). The rule base has a built-in conflict resolution mechanism that automatically resolves conflicting detail combinations (e.g., the snow accumulation activation state is automatically set to 0 under the "high temperature" style). It should be noted that the specific content of the rule base mapped to the above parameters can be set and adjusted according to actual needs, and is not specifically limited here.

[0063] Furthermore, in this embodiment, three algorithm rules are defined: point distribution, line distribution, and polygon distribution, and a sampling algorithm corresponding to each detail type is predefined. Taking the road segment node attributes (including the set of detail types, the detail generation weight of each target detail type, and the detail generation density) as input, for each road segment node, the corresponding sampling algorithm is selected according to the activated detail type, and the spatial coordinates of the decal placement point are calculated within the road surface area of ​​the road segment to construct the detail node (associated with the decal placement coordinates and decal parameters).

[0064] All three sampling algorithms require calculating the effective density of this detail type. . The result is obtained by multiplying two parts: detail generation density × target detail generation weight corresponding to the detail type. The detail generation density is automatically calculated by the system based on the geometric curvature of the road segment nodes. The greater the curvature, the higher the detail generation density value, which is used to control the total amount of detail generation for the entire road segment. The target detail generation weight is obtained by combining the style tag with road age correction, and is used to control the distribution ratio of each detail type in the total amount.

[0065] Point distribution algorithm: Input: Effective density of this detail type Half the width of the road Initial size / Opacity / Roughness attenuation coefficient . It is a preset fixed constant, and does not change. Changes are only used to control the curve. The speed of ascent from the center to the edge, The larger it is, the faster it rises.

[0066] Calculation: First, use a Poisson disk to sample within the road segment, along the road width direction, from 0 to... A batch of candidate points is scattered within the area. Each candidate point is ranked according to its lateral distance from the center line. Calculate the density value : ; in, .

[0067] It is a line that starts from 0.3 and follows... Increase the curve's smooth upward movement, eventually approaching 1. 0.3 is the base value at the centerline (even in the very middle of the road, cracks, potholes, etc., have a base density of about 30%, they won't be completely absent), as... Gradually increase the value to near the maximum of 1 (the closer to the road edge, the more concentrated the tire pressure and water accumulation, resulting in higher density). Whether to retain a candidate point depends on... Is it greater than a random number within (0, 1), and the number of points of this type already reserved for this road segment does not exceed the road segment area and... The product of the two. The remaining points, decal size / opacity / roughness are... It should be noted that each candidate point has a unique random number. Poisson disk sampling first evenly distributes a batch of candidate points within the road segment, ensuring only reasonable point spacing and preventing clumping; it does not reflect the effect of more edge details and fewer center details. Then, a unique random number is drawn for each candidate point and compared with that point's random number. In comparison, if If it is larger, keep it. The larger the value (the closer to the edge), the higher the probability of retention. As a result, the remaining points naturally exhibit a gradual distribution with denser edges and sparser centers.

[0068] Output: A batch of placement points with coordinates and decal parameters, each point creating a detail node.

[0069] Line distribution algorithm: Input: Path centerline Half the width of the road Effective density of this detail type .

[0070] Calculation: Offset by half the road width on each side of the centerline to obtain two baselines: ;in, It is the centerline normal vector Around a small angle The result after rotation . The larger the rut size, the greater the allowable natural swaying deviation from the centerline. Smaller ruts are closer to being parallel to the centerline.

[0071] Output: A pair of trajectory lines with natural curvature, along which placement points are generated at intervals, and each point creates a detail node.

[0072] Polygonal distribution algorithm: Input: Road segment area range, effective density of this detail type and grid size Among them, will Normalized to the range (0, 1), denoted as .

[0073] Calculation: First, divide the road segment into several grids, and randomly select the number of grids... A proportional grid serves as the center of the core area; each center is centered by a radius... Draw a sampling circle, where, Randomly sprinkle dots within the circle, the number of dots also corresponds to... Proportional. The attenuation factor for each sampling point is proportional to its distance from the center of the circle. (the closer to the edge) (The smaller) determine the size / opaqueness / roughness. . The larger the curve (the more curved the road), the greater the number of core areas, the radius of the sampling circle, and the number of sampling points, which corresponds to the physical intuition that the curve is more likely to accumulate large areas of water and snow.

[0074] Output: A batch of placement points with coordinates and decal parameters, each point creating a detail node.

[0075] It should be noted that, in the point distribution algorithm, in addition to Poisson disk sampling, Gaussian distribution sampling or image-based sampling can also be used, without making specific limitations here.

[0076] After the detailed nodes are constructed, a three-dimensional road model is generated by rendering based on the geometric data and attributes associated with the aforementioned road surface nodes (or road segment nodes) and the detailed identifier attributes associated with the aforementioned detailed nodes.

[0077] It should be further noted that the rendering engine used can be set and adjusted according to actual needs, and no specific restrictions are made here.

[0078] This application provides a road model generation method, which includes: obtaining a road centerline point sequence and road age parameters; creating path nodes, configuring centerline attributes and road age attributes for the path nodes based on the road centerline point sequence and road age parameters; creating lower-level pavement nodes for the path nodes, obtaining pavement width parameters, pavement type parameters, and pavement thickness parameters, generating pavement meshes based on the centerline attributes, pavement width parameters, and pavement thickness parameters, associating the pavement meshes as geometric data with the pavement nodes, and configuring pavement type attributes for the pavement nodes based on the pavement type parameters; creating lower-level detail nodes for the pavement nodes, obtaining style tags, determining target detail types and target detail generation weights corresponding to each target detail type based on the style tags and road age attributes, determining the coordinate set of target detail placement points corresponding to each target detail type based on the pavement meshes, target detail types, and target detail generation weights, configuring detail identifier attributes for the detail nodes based on the target detail types and target detail placement point coordinate sets; and generating a 3D road model through rendering based on the geometric data and pavement type attributes associated with the pavement nodes, and the detail identifier attributes associated with the detail nodes.

[0079] Therefore, this application's approach to road model generation does not simply rely on static reconstruction of remote sensing imagery at a specific moment. Instead, it abstracts the road into a computable and editable object-oriented representation by constructing a hierarchical node structure (including path nodes, road surface nodes, and detail nodes). Simultaneously, it introduces style tags and road age parameters as stylization drivers, ensuring that the selection of detail types and the determination of generation weights are no longer fixed data recovery processes but are dynamically calculated based on semantic input. Finally, the geometric data associated with hierarchical nodes and detail identifier attributes are jointly rendered to generate a 3D road model. This allows the generated road model to no longer be entirely dependent on remote sensing imagery data at a specific moment, but to flexibly generate road models with differentiated visual expressions based on different style semantics and road age parameters. This effectively overcomes the shortcomings of traditional data-driven reconstruction methods in terms of generation flexibility and controllability, facilitating the flexible generation of the required road models.

[0080] To address the shortcomings of existing road modeling technologies, such as the lack of a physical evolution mechanism, inability to effectively express spatiotemporal context, and difficulty in controlling stylistic generation, this application proposes a road semantic discretization representation technology based on a layered DAG graph structure. This technology abstracts roads into a four-layer topological structure of "path-surface-segment-detail," enabling parametric generation and hierarchical management of geometry. It constructs a style-to-detail parameter mapping rule, automatically converting style semantic labels into detail type sets and weight parameters based on three physical dimensions: aging, environment, and function. Furthermore, it employs a procedural detail generation algorithm that considers spatial context, designing spatially constrained distribution algorithms for point, line, and surface details. This ensures that details are concentrated on the roadside or wheel tracks according to a density decay function, eliminating the randomness of traditional texture mapping.

[0081] Based on the above scheme, this application embodiment achieves independent control of the style state of different sections through a layered DAG graph structure, ensures the physical self-consistency of the generated scene through a physical logic-driven parameter mapping mechanism, and accurately simulates the real road surface wear patterns under long-term vehicle rolling and environmental erosion through a spatial geometric constraint generation algorithm. Simultaneously, it introduces parameterized control of the time dimension (aging) and the environmental dimension (weather), breaking through the limitations of traditional static simulation in GIS road modeling. In this way, physically consistent, detailed, and style-controllable 3D road models can be generated, reducing manual modeling costs, improving the automation level and visual realism of modeling, and the generated road models can serve as independent, reusable assets to support large-scale digital twin and 3D GIS applications.

[0082] like Figure 2 As shown in the figure, corresponding to the above-described road model generation method, this application embodiment also provides a road model generation system, which includes: Data acquisition module 210 is used to acquire the road centerline point sequence and road age parameters; The path node creation module 220 is used to create path nodes and configure centerline attributes and road age attributes for the path nodes according to the above road centerline point sequence and the above road age parameters. The road surface node creation module 230 is used to create lower-level road surface nodes for the aforementioned path nodes, obtain road surface width parameters, road surface type parameters, and road surface thickness parameters, generate a road surface mesh based on the aforementioned centerline attributes, road surface width parameters, and road surface thickness parameters, associate the aforementioned road surface mesh as geometric data with the aforementioned road surface nodes, and configure road surface type attributes for the aforementioned road surface nodes according to the aforementioned road surface type parameters. The detail node creation module 240 is used to create lower-level detail nodes for the above road surface nodes, obtain style tags, determine the target detail type and the target detail generation weight corresponding to each of the above target detail types based on the style tags and the above road age attribute, determine the coordinate set of the target detail placement points corresponding to each target detail type based on the above road surface mesh, the above target detail type and the above target detail generation weight, and configure detail identification attributes for the above detail nodes based on the above target detail type and the coordinate set of the above target detail placement points. The rendering module 250 is used to generate a three-dimensional road model by rendering based on the geometric data and road type attributes associated with the aforementioned road surface nodes, as well as the detail identifier attributes associated with the aforementioned detail nodes.

[0083] Therefore, this application's approach to road model generation does not simply rely on static reconstruction of remote sensing imagery at a specific moment. Instead, it abstracts the road into a computable and editable object-oriented representation by constructing a hierarchical node structure (including path nodes, road surface nodes, and detail nodes). Simultaneously, it introduces style tags and road age parameters as stylization drivers, ensuring that the selection of detail types and the determination of generation weights are no longer fixed data recovery processes but are dynamically calculated based on semantic input. Finally, the geometric data associated with hierarchical nodes and detail identifier attributes are jointly rendered to generate a 3D road model. This allows the generated road model to no longer be entirely dependent on remote sensing imagery data at a specific moment, but to flexibly generate road models with differentiated visual expressions based on different style semantics and road age parameters. This effectively overcomes the shortcomings of traditional data-driven reconstruction methods in terms of generation flexibility and controllability, facilitating the flexible generation of the required road models.

[0084] It should be noted that the specific structure and implementation of the road model generation system and its various modules or units can be referred to the corresponding descriptions in the above method embodiments, and will not be repeated here.

[0085] It should be noted that the division of the various modules in the road model generation system described above is not unique and is not intended as a specific limitation.

[0086] Based on the above embodiments, this application also provides a terminal, the principle block diagram of which can be as follows: Figure 3As shown. The terminal includes a processor, memory, network interface, and display screen connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps of any of the road model generation methods described above. The display screen can be a liquid crystal display (LCD) or an e-ink display.

[0087] Those skilled in the art will understand that Figure 3 The block diagram shown is only a partial structural diagram related to the solution of this application and does not constitute a limitation on the terminal on which the solution of this application is applied. The specific terminal may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements.

[0088] In one embodiment, a terminal is provided, the terminal including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of any of the road model generation methods provided in the embodiments of this application.

[0089] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the road model generation methods provided in this application.

[0090] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0091] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the above device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0092] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0093] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0094] In the embodiments provided in this application, it should be understood that the disclosed systems / terminal devices and methods can be implemented in other ways. For example, the system / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units described above is merely a logical functional division, and in actual implementation, it can be divided in other ways. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0095] If the integrated modules / units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, and software distribution media, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0096] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions are not in essence a departure from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for generating road models, characterized in that, The method includes: Obtain the road centerline point sequence and road age parameters; Create path nodes and configure centerline and road age attributes for the path nodes based on the road centerline point sequence and the road age parameter; Create a lower-level road surface node for the path node, obtain road surface width parameters, road surface type parameters, and road surface thickness parameters, generate a road surface mesh based on the centerline attribute, the road surface width parameters, and the road surface thickness parameters, associate the road surface mesh as geometric data with the road surface node, and configure the road surface type attribute for the road surface node according to the road surface type parameter; Create lower-level detail nodes for the road surface nodes, obtain style tags, determine target detail types and target detail generation weights corresponding to each target detail type based on the style tags and the road age attribute, determine the coordinate set of target detail placement points corresponding to each target detail type based on the road surface mesh, the target detail type and the target detail generation weights, and configure detail identification attributes for the detail nodes based on the target detail type and the coordinate set of the target detail placement points; A 3D road model is generated by rendering based on the geometric data and road type attributes associated with the road surface nodes, as well as the detail identifier attributes associated with the detail nodes.

2. The road model generation method according to claim 1, characterized in that, The process of generating a road surface mesh based on the centerline attribute and the road surface width parameter includes: Lateral geometric extrusion is performed along the road centerline indicated by the centerline attribute to generate a continuous road surface mesh with a width matching the road surface width parameter.

3. The road model generation method according to claim 1, characterized in that, The process of obtaining style tags and determining target detail types and corresponding target detail generation weights based on the style tags and the road age attribute includes: Obtain the style tag input by the user, and based on the style tag, search from the preset parameter mapping rule library to determine the target detail type that matches the style tag and the target detail generation weight corresponding to the target detail type; The preset parameter mapping rule library stores multiple preset styles, the activated detail types under each preset style, and the detail generation weights corresponding to the detail types under different road ages.

4. The road model generation method according to any one of claims 1 to 3, characterized in that, The path nodes, the road surface nodes, and the detail nodes constitute a directed acyclic graph structure with multiple node levels from top to bottom.

5. The road model generation method according to claim 4, characterized in that, The directed acyclic graph structure also includes road segment nodes set between the levels of the road surface nodes and the detail nodes; The process involves creating lower-level detail nodes for the road surface nodes, obtaining style tags, determining target detail types and corresponding target detail generation weights based on the style tags and the road age attribute, determining the coordinate set of target detail placement points for each target detail type based on the road surface mesh, the target detail type, and the target detail generation weights, and configuring detail identifier attributes for the detail nodes based on the target detail type and the coordinate set of target detail placement points. Obtain the distance interval, and according to the distance interval, cut the road surface grid along the road centerline to obtain multiple independent road segment sub-grids. Based on the road segment sub-grids, create multiple independent road segment nodes at the lower level for the road surface nodes. The road segment nodes correspond one-to-one with the road segment sub-grids, and associate the road segment sub-grids as geometric data with the corresponding road segment nodes. For each road segment node, determine the detail generation density according to the geometric curvature of the road segment centerline corresponding to the associated road segment sub-grid, and configure the detail density attribute for the road segment node according to the detail generation density. For each road segment node, a lower-level detail node is created for the road segment node. The target detail type and the target detail generation weight corresponding to each target detail type are determined according to the style tag and the road age attribute. The coordinate set of the target detail placement point corresponding to each target detail type is determined according to the road segment sub-grid, the detail density attribute, the target detail type and the target detail generation weight. The detail identification attribute is configured for the detail node according to the target detail type and the coordinate set of the target detail placement point.

6. The road model generation method according to claim 5, characterized in that, The detail generation density is used to control the total number of all target detail placement points under the road segment subgrid; The target detail generation weight is used to control the number of target detail placement points corresponding to the target detail type.

7. The road model generation method according to claim 5, characterized in that, The step of determining the coordinate set of target detail placement points corresponding to each target detail type based on the road segment sub-grid, the detail density attribute, the target detail type, and the target detail generation weight includes: The target sampling algorithm corresponding to each target detail type is determined from a set of preset sampling algorithms; For each target detail type, based on the detail density attribute and the target detail generation weight, the target sampling algorithm is used to sample and determine the target detail placement point corresponding to the target detail type in the road segment subgrid, and a set of coordinates of the target detail placement point corresponding to the target detail type is constructed.

8. A road model generation system, characterized in that, The system includes: The data acquisition module is used to acquire the road centerline point sequence and road age parameters; The path node creation module is used to create path nodes and configure centerline attributes and road age attributes for the path nodes according to the road centerline point sequence and the road age parameter. The road surface node creation module is used to create lower-level road surface nodes for the path nodes, obtain road surface width parameters, road surface type parameters, and road surface thickness parameters, generate a road surface mesh based on the centerline attribute, the road surface width parameters, and the road surface thickness parameters, associate the road surface mesh as geometric data with the road surface nodes, and configure road surface type attributes for the road surface nodes according to the road surface type parameters. The detail node creation module is used to create lower-level detail nodes for the road surface nodes, obtain style tags, determine target detail types and target detail generation weights corresponding to each target detail type based on the style tags and the road age attribute, determine the coordinate set of target detail placement points corresponding to each target detail type based on the road surface mesh, the target detail type and the target detail generation weights, and configure detail identification attributes for the detail nodes based on the target detail type and the coordinate set of target detail placement points. The rendering module is used to generate a three-dimensional road model by rendering based on the geometric data and road type attributes associated with the road surface nodes, as well as the detail identifier attributes associated with the detail nodes.

9. A terminal, characterized in that, The terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the road model generation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the road model generation method as described in any one of claims 1 to 7.

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