CN112700538B - LOD generation method and system - Google Patents
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Abstract
The embodiment of the invention provides a LOD generation method and a LOD generation system, wherein the method comprises the following steps: extracting a horizontal contour of a building, and generating a wire frame structure of the building according to the horizontal contour and elevation information of the building; and mixing the wire frame structure with a digital elevation model DEM of the urban terrain to generate the LOD with the wire frame structure. The wire frame structure generated by combining the horizontal contour extracted by the deep learning technology and the elevation information represents the integral characteristics of the building, and the building of the wire frame structure has regular and regular appearance and simple geometric structure. Therefore, the LOD generation method based on the wire frame structure extraction and mixing can ensure that when the urban inclined simplified layer model is observed remotely, not only is browsing and refreshing smooth, but also the buildings of the scene are neat and uniform, and better visual experience is brought to people.
Description
Technical Field
The invention relates to the technical field of display, in particular to a LOD generation method and system.
Background
The LOD technology determines the resource allocation of object rendering according to the position and importance of an object model relative to an observer in a display environment, reduces the number of planes and detail of distant and non-important objects, and therefore obtains a high-efficiency rendering effect, which is an indispensable technology for three-dimensional display of big data. When the model is large, computer resources are limited, and data drawing cannot be performed in real time. To solve the above problems, LOD technology generally implements the following display strategy for models: I. and displaying models with different complexity of the same object according to the change of the distance between the observer and the model, calling the model with a low resolution level when the model is far away from the observer, and calling the model with a high resolution level when the model is close to the observer. And II, only loading the part of the model visible in the window range by the system, and eliminating the triangular surface of the model outside the screen.
Referring to FIG. 1, a schematic diagram of a greatly simplified LOD multi-level model structure is shown. In order to implement strategy I, different levels of simplification of the color model are required to be performed, and different levels of simplified color models are generated. Visually, when the tilted model is far from the observer, the local features of the model are indistinguishable and can be simplified. However, with respect to simplification of the color model, the triangle mesh is simplified to a certain extent, and the local feature cannot be simplified continuously while the global feature is maintained. For example, in the urban inclined model, a residential building is provided with a layer of raised balconies, each balcony is a local feature, and the overall outline of the residential building is an overall feature, as shown in fig. 2, which is a schematic diagram of the local feature and the overall feature of the residential building. If the model is simplified to a certain extent, if the model is forcedly simplified, local characteristics of the model can not be seen, but the whole characteristics of the model can be destroyed, the building is distorted, and the quality of the model is reduced when the model is observed at a long distance.
Fig. 3 is a simplified diagram of the effect of distortion of the building. However, if simplification is not continued, the geometric data size of the model is larger, and meanwhile, due to the existence of the local geometric detail features in a discretization form, the data size of textures is also increased in the process of expanding the three-dimensional grid into a two-dimensional UV plane and carrying out UV mapping. According to the display principle of the near-far-small of the screen view cone and the display strategy II of LOD, the system loads a large-scale model when observing urban scenes at a long distance. Therefore, when the urban model display with thousands of stories is performed, the overall data volume of all tiles with low fineness level is large and the computing resources are limited, so that the blocking during long-distance watching can be obviously caused, and the LOD browsing experience of users is affected.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a LOD generating method and system that overcomes or at least partially solves the above problems.
According to a first aspect of an embodiment of the present invention, there is provided a LOD generating method, including: extracting a horizontal contour of a building, and generating a wire frame structure of the building according to the horizontal contour and elevation information of the building; and mixing the wire frame structure with a digital elevation model DEM of the urban terrain to generate the LOD with the wire frame structure.
According to a second aspect of an embodiment of the present invention, there is provided an LOD generating system, the system comprising: the generation module is used for extracting the horizontal outline of the building and generating a wire frame structure of the building according to the horizontal outline and the elevation information of the building; and the mixing module is used for mixing the wire frame structure with the digital elevation model DEM of the urban terrain to generate the LOD with the wire frame structure.
According to a third aspect of embodiments of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the LOD generation method as provided by any one of the various possible implementations of the first aspect when the program is executed.
According to a fourth aspect of embodiments of the present invention, there is provided a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a LOD generation method as provided by any of the various possible implementations of the first aspect.
According to the LOD generation method and system provided by the embodiment of the invention, the overall characteristics of the building are represented by the wire frame structure generated by combining the horizontal contour extracted by the deep learning technology with the elevation information, and the building of the wire frame structure is regular in appearance and simple in geometric structure. Therefore, the LOD generation method based on the wire frame structure extraction and mixing can ensure that when the urban inclined simplified layer model is observed remotely, not only is browsing and refreshing smooth, but also the buildings of the scene are neat and uniform, and better visual experience is brought to people.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is apparent that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic diagram of a LOD multi-level model structure provided in the prior art;
FIG. 2 is a schematic diagram of the local and global features of a residential building provided in the prior art;
FIG. 3 is a diagram showing the effect of distortion of a building which is simplified by multiple times of high strength according to the prior art;
FIG. 4 is a graph of DOM (up) and DSM (down) effects for the same area of a city provided by an embodiment of the present invention;
FIG. 5 is a flow chart of a technique for extracting building areas using urban orthophotos according to an embodiment of the invention
FIG. 6 is a schematic view of a city building area using DOM extraction according to an embodiment of the present invention;
FIG. 7 is a schematic view of a horizontal outline of a city building according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a wire frame structure of a building according to an embodiment of the present invention;
FIG. 9 is a diagram showing the effect of mixing a wire frame structure and an inclination model when LOD is observed remotely according to an embodiment of the present invention;
fig. 10 is a flow chart of an LOD generating method according to an embodiment of the present invention;
FIG. 11 is a schematic diagram of an LOD generating system according to an embodiment of the present invention;
fig. 12 is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the prior art, due to the limitation of calculation accuracy and shooting quality, the inclination model automatically generated by utilizing the photo shot by the unmanned aerial vehicle has fluctuation on the outer wall surface and the wall edge of the original building model, and the fluctuation is further enlarged due to multiple simplification, so that the building in fig. 2 is distorted. Aiming at the problems in the prior art, the embodiment of the invention provides a LOD generation method, which aims to simplify the technology of removing local features under the condition of maintaining integral features in the color model simplification process of the LOD generation flow so as to realize smooth display of a large-scale city three-dimensional inclined model LOD.
First, the principle of the embodiment of the present invention will be explained. According to the schematic diagram of fig. 2, when a building is observed remotely, only the whole framework structure is needed to be seen. In the embodiment of the invention, the horizontal outline of the building is extracted by using a deep learning technology, then the triangular net of the building wire frame structure is generated by combining the elevation information of the building model, and finally the generated triangular net of the building is fused into the DEM representing the urban terrain.
To achieve the above object, an embodiment of the present invention may include the steps of:
1. the horizontal contour of the building is extracted using a deep learning technique.
2. Building wireframe structures are generated in conjunction with elevation information of the building itself.
3. A mix of building wireframe structures and urban terrain DEMs.
4. LOD generation of the wireframe structure simplified layer.
Based on this, the embodiment of the invention is characterized in that: the LOD data size is smaller, and network transmission is facilitated. 2. When the large-scale urban inclined model is watched in a long distance, the large-scale urban inclined model has better overall browsing experience.
Specifically, referring to fig. 10, the method includes, but is not limited to, the steps of:
and 101, extracting a horizontal outline of a building, and generating a wire frame structure of the building according to the horizontal outline and elevation information of the building.
Specifically, the generation of the oblique model LOD is to firstly use an original model to carry out multi-level textured simplification, generate models with continuously reduced textures and geometric resolutions, and then carry out the processes of spatial cutting and tree structure organization on the multi-level model according to the two LOD display strategies. In the embodiment of the invention, the color model simplification of a plurality of simplified levels with high resolution uses an automatic generation method of a color LOD model based on block decomposition, but the color model simplification of a plurality of levels with lower resolution is replaced by a method of mixing a building with a wire frame structure and a urban inclined mixed terrain DEM so as to realize the overall browsing experience of the urban inclined model with lower data volume of the low resolution level and better big data.
Based on the foregoing embodiment, as an alternative embodiment, the extracting the horizontal contour of the building includes: extracting building areas in a digital orthographic model DOM of the city model through a neural network model; regularizing said building area by means of a digital surface model DSM of the city model to obtain an accurate contour of said building area; and vectorizing the bitmap of the building area to obtain the contour line of the building.
Based on the foregoing embodiment, as an optional embodiment, vectorizing the bitmap of the building area includes: binarizing the bitmap of the building area; detecting an edge contour of a building to obtain an ordered series of edge points representing the contour of the building; edge points collinear with the front and rear points are removed to obtain corner points of a polygon representing the outline of the building.
First, the horizontal contour of the building is extracted using a deep learning technique.
The DOM (digital orthographic model) and the DSM (digital surface model) of the urban inclination model need to be prepared before the horizontal contour of the building is extracted. The DOM contains spectral information of the city building and the ground, and the DSM contains elevation information of the city building and the topography. FIG. 4 is a schematic diagram of DOM and DSM for the same region.
Since there is a clear demarcation between the building texture and the surrounding environment from above the city, the city orthographic image can be used to extract the building area of the entire city. The DSM image contains building elevation information, which can be used to further accurately determine the area of each building because the building elevation head is significant. And finally vectorizing the building area bitmap to obtain the building outline in a vector form. The actual image edge detection operator can also detect the contour of the building on the DOM, but can also detect a large number of unwanted non-building contours while obtaining the building edge.
Specifically, the step of extracting may further include the steps of:
(1) And extracting the building area by using a Mask RCNN neural network model.
Mask RCNN is the most popular current deep learning image instance segmentation framework that can be used to extract building regions in the city DOM. Compared with the traditional building area recognition algorithm based on the experience design characteristics, the deep learning method is higher in precision and stability, and is suitable for various buildings with various different sizes, shapes and changeable arrangement modes.
The embodiment of the invention directly uses a TensorFlow deep learning system of Google to build a Mask RCNN network. After the Mask RCNN network is built, we next make a training sample training network: firstly, preparing DOM images (1024 x 1024) of the city model; the approximate building area is then manually marked using a VIA data marking tool; and finally, respectively taking the urban orthographic image and the marked building area as an input training Mask RCNN network and an output training Mask RCNN network. After the Mask RCNN network parameters are trained, the urban orthophotos to be tested can be input into the Mask RCNN network, and the wanted building area is obtained through feature extraction of the Mask RCNN network feature extraction layer, classification of the full connection layer, frame positioning and Mask regression. Fig. 5 and 6 below are a technical flowchart of extracting a building area using urban orthophotos and an effect schematic of the extracted building area, respectively.
(2) Regularization of building areas using DSM
Building areas extracted using DOM are not very accurate building boundaries, on the one hand because the manually marked building areas themselves in the neural network training samples are not accurate, accurate marking requires more labor costs, and it is easy to put Mask RCNN networks into overfitting. On the other hand, the accuracy of the urban orthographic image DOM is limited, and too high accuracy can increase the complexity and the calculation time of the Mask RCNN neural network. To accurately obtain the area of each building and thus the exact contour, we use DSM to regularize each building area. Each building has a well-defined height Cheng Lacha relative to the surrounding environment, so that the building area calculated by the Mask RCNN network of the previous step is further regularized by a high-precision DSM image, using a pixel area growing method. Meanwhile, the regularization of the building area by using the DSM image is also beneficial to the vectorization of the building bitmap in the next step, and the outline of the building is represented by fewer edges and corner points.
(3) Vectorization of building area bitmaps
The foregoing results in a matrix of pixels representing the building area, and the bitmap must be vectorized to obtain a polygon that accurately describes the building horizontal outline in order to create the building wireframe structure. In the invention, the building area bitmap is binarized; then detecting the edge contour of the building by using a Canny operator of OpenCV to obtain a series of ordered edge points representing the contour of the building; and finally, eliminating each edge point collinear with the front point and the rear point to obtain the corner point of the polygon representing the outline of the building. Through the three steps, vectorization of the building area bitmap is achieved. Fig. 7 is a schematic view of a city building horizontal contour line.
Based on the foregoing embodiment, as an alternative embodiment, generating a wire frame structure of the building according to the horizontal profile and elevation information of the building includes: obtaining the up-down elevation information of the building by counting the maximum elevation and the minimum elevation of the triangular mesh vertexes in the horizontal outline of the building in the original model; and constructing a triangular net of the wire frame structure according to the up-down elevation information.
Based on the foregoing embodiments, as an optional embodiment, constructing the triangle mesh of the wire frame structure according to the elevation information includes: inputting a top layer angular point of a building and a top layer contour line of the building serving as a constraint condition based on a preset networking algorithm according to the up-down elevation information to obtain a top layer triangular network of the building; a triangular mesh of each wall of the building is constructed to construct the sides of the building.
In particular, the purpose of this step is to generate a building wireframe structure in combination with the elevation information of the building itself. After the horizontal contour of the building has been extracted in the previous step, it is then necessary to create the overall wire frame structure of the building. In computer graphics, we generally express the geometric topology of the tilt model with a triangular mesh, so we also express the wire frame structure of the building with a triangular mesh, facilitating the next step of mixing the building with the urban terrain. The up-down elevation information of the building can be obtained by counting the maximum elevation and the minimum elevation of the triangular mesh vertexes of the original model in the horizontal outline range of the building, and then combining the elevation information to construct the triangular mesh of the wire frame structure. Firstly, a two-dimensional Delaunay networking algorithm with constraint in a CGAL open source library is applied to construct a top layer of a building, and top layer corner points of the building and top layer contour lines of the building serving as constraint conditions are input to obtain a top layer triangle network of the constructed building. Next, a side triangulation of the building is constructed. The side of the building is composed of one wall, the upper and lower vertexes are in one-to-one correspondence, and each wall can be expressed as two triangular surfaces. By constructing a triangular net for each wall of the building, the entire side of the building can be constructed. The bottom of the building need not be constructed because it is buried deep in the earth and is not visible when viewed from a distance. Fig. 8 is a schematic diagram of a wire frame structure of a building.
Step 102, mixing the wire frame structure with a digital elevation model DEM of urban terrain to generate LOD with the wire frame structure.
Based on the foregoing embodiment, as an alternative embodiment, mixing the wire frame structure with a digital elevation model DEM of urban terrain includes: generating a top layer texture of the wire frame structure by an orthographic image; and adding the triangular surface of the wire frame structure and the top layer texture into the DEM.
In particular, the purpose of this step is a mix of building wireframe structures and urban terrain DEMs. The Digital Elevation Model (DEM) is a terrain model with earth surface buildings and trees removed, and expresses geometric and texture information of the ground. In the present invention, DEM may be generated by culling buildings in the original model. The first step is to generate a DEM, and if a triangle of the original model is completely within any building contour, we delete the triangle, thus obtaining a terrain DEM of a city. The second step uses the orthographic image to generate the top layer texture of the wireframe structure. And thirdly, adding triangular surfaces and textures of the wire frame structure into the DEM of the urban terrain by means of an open source grid library Openmesh to realize the mixing of the building wire frame structure and the DEM of the urban terrain. The final blending results as a simplified-level model of the LOD at some lower resolution.
Based on the foregoing embodiment, as an alternative embodiment, generating the LOD with the wire frame structure includes: obtaining a first simplified hierarchical image L2 generated by mixing the wire frame structure with the DEM; generating a second reduced level image L1 having a higher resolution with respect to the first reduced level image L2 from the first reduced level image L2; cutting the original model L0 from top to bottom to construct an LOD segmentation tree; according to the LOD segmentation tree, segmenting and filling each level model into LOD segmentation tree nodes of corresponding levels, and constructing an LOD data tree; each hierarchical model comprises the original model L0, the first simplified hierarchical image L2 and the second simplified hierarchical image L1; and outputting the data file of each node to obtain the LOD with the wire frame structure.
In particular, the purpose of this step is to simplify LOD generation of the layer with wire frame structure. By cycling through the above steps to generate several simplified levels of lower resolution than the original model (L2 in fig. 1), and combining with an automatic generation method of color LOD model based on block decomposition to generate several simplified levels of higher resolution than the above levels (L1 in fig. 1), we can generate a tilted model LOD with a mixture of wireframe structures according to LOD display strategy. All of the simplified hierarchical models and the original model (L0 in FIG. 1) are summed together to form the multi-level model to be used to generate the LOD. Firstly, continuously cutting an original model from top to bottom to construct an LOD segmentation tree, and determining a space tree organization mode of the whole multi-level model data. Then according to the LOD segmentation tree, segmenting and filling each level model into LOD segmentation tree nodes of corresponding levels from bottom to top to construct an LOD data tree; and finally outputting the osgb data file of each node. Fig. 9 below is a schematic diagram of a city inclination model LOD based on a wire frame structure mix.
In summary, according to the LOD generating method provided by the embodiment of the present invention, the overall characteristics of the building are represented by the wire frame structure generated by combining the horizontal contour extracted by the deep learning technology with the elevation information, and the building of the wire frame structure has a neat and regular appearance and a simple geometric structure. Therefore, the LOD generation method based on the wire frame structure extraction and mixing can ensure that when the urban inclined simplified layer model is observed remotely, not only is browsing and refreshing smooth, but also the buildings of the scene are neat and uniform, and better visual experience is brought to people.
Based on the content of the above embodiments, an embodiment of the present invention provides an LOD generating system for executing the LOD generating method in the above method embodiment. Referring to fig. 11, the system includes: a generating module 301, configured to extract a horizontal contour of a building, and generate a wire frame structure of the building according to the horizontal contour and elevation information of the building; the mixing module 302 is configured to mix the wire frame structure with a digital elevation model DEM of urban terrain, and generate an LOD with the wire frame structure.
An embodiment of the present invention provides an electronic device, as shown in fig. 12, including: a processor (processor) 501, a communication interface (Communications Interface) 502, a memory (memory) 503 and a communication bus 504, wherein the processor 501, the communication interface 502, and the memory 503 communicate with each other via the communication bus 504. The processor 501 may call a computer program on the memory 503 and executable on the processor 501 to perform the LOD generating method provided in the above embodiments, for example, including: extracting a horizontal contour of a building, and generating a wire frame structure of the building according to the horizontal contour and elevation information of the building; and mixing the wire frame structure with a digital elevation model DEM of the urban terrain to generate the LOD with the wire frame structure.
Further, the logic instructions in the memory 503 described above may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Embodiments of the present invention also provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the LOD generating method provided in the above embodiments, for example, including: extracting a horizontal contour of a building, and generating a wire frame structure of the building according to the horizontal contour and elevation information of the building; and mixing the wire frame structure with a digital elevation model DEM of the urban terrain to generate the LOD with the wire frame structure.
The above-described embodiments of electronic devices and the like are merely illustrative, in which elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product, which may be stored in a computer-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the various embodiments or some part of the methods of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A LOD generation method, comprising:
extracting a horizontal contour of a building, and generating a wire frame structure of the building according to the horizontal contour and elevation information of the building;
mixing the wire frame structure with a digital elevation model DEM of urban terrain to generate LOD with the wire frame structure;
the generating the LOD with the wire frame structure includes:
obtaining a first simplified hierarchical image L2 generated by mixing the wire frame structure with the DEM;
generating a second reduced level image L1 having a higher resolution with respect to the first reduced level image L2 from the first reduced level image L2;
cutting the original model L0 from top to bottom to construct an LOD segmentation tree;
according to the LOD segmentation tree, segmenting and filling each level model into LOD segmentation tree nodes of corresponding levels, and constructing an LOD data tree; each hierarchical model comprises the original model L0, the first simplified hierarchical image L2 and the second simplified hierarchical image L1;
and outputting the data file of each node to obtain the LOD with the wire frame structure.
2. The method of claim 1, wherein the extracting the horizontal contour of the building comprises:
extracting building areas in a digital orthographic model DOM of the city model through a neural network model;
regularizing said building area by means of a digital surface model DSM of the city model to obtain an accurate contour of said building area;
and vectorizing the bitmap of the building area to obtain the contour line of the building.
3. The method of claim 2, wherein vectorizing the bitmap of the building area comprises:
binarizing the bitmap of the building area;
detecting an edge contour of a building to obtain an ordered series of edge points representing the contour of the building;
edge points collinear with the front and rear points are removed to obtain corner points of a polygon representing the outline of the building.
4. The method of claim 1, wherein generating a wireframe structure of the building from the horizontal profile and elevation information of the building comprises:
obtaining the up-down elevation information of the building by counting the maximum elevation and the minimum elevation of the triangular mesh vertexes in the horizontal outline of the building in the original model;
and constructing a triangular net of the wire frame structure according to the up-down elevation information.
5. The method of claim 4, wherein constructing a triangle mesh of the wire frame structure based on the elevation information comprises:
inputting a top layer angular point of a building and a top layer contour line of the building serving as a constraint condition based on a preset networking algorithm according to the up-down elevation information to obtain a top layer triangular network of the building;
a triangular mesh of each wall of the building is constructed to construct the sides of the building.
6. The method of claim 1, wherein mixing the wire frame structure with a digital elevation model DEM of urban terrain comprises:
generating a top layer texture of the wire frame structure by an orthographic image;
and adding the triangular surface of the wire frame structure and the top layer texture into the DEM.
7. A LOD generation system, comprising:
the generation module is used for extracting the horizontal outline of the building and generating a wire frame structure of the building according to the horizontal outline and the elevation information of the building;
the mixing module is used for mixing the wire frame structure with a digital elevation model DEM of urban terrain to generate LOD with the wire frame structure;
the mixing module is further configured to obtain a first simplified-hierarchy image L2 generated by mixing the wire frame structure with the DEM; generating a second reduced level image L1 having a higher resolution with respect to the first reduced level image L2 from the first reduced level image L2; cutting the original model L0 from top to bottom to construct an LOD segmentation tree; according to the LOD segmentation tree, segmenting and filling each level model into LOD segmentation tree nodes of corresponding levels, and constructing an LOD data tree; each hierarchical model comprises the original model L0, the first simplified hierarchical image L2 and the second simplified hierarchical image L1; and outputting the data file of each node to obtain the LOD with the wire frame structure.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the LOD generation method of any one of claims 1 to 6 when the program is executed by the processor.
9. A non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the LOD generation method according to any one of claims 1 to 6.
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