CN116863137A - Optimization method, device and computer equipment for three-dimensional model of transmission tower - Google Patents

Abstract

The application relates to an optimization method, an optimization device, computer equipment, a storage medium and a computer program product for a three-dimensional model of a power transmission tower. Acquiring and dividing an initial oblique photography model to obtain a characteristic region and a non-characteristic region of the iron tower; the iron tower characteristic region comprises a first triangular surface, and the iron tower non-characteristic region comprises a second triangular surface; acquiring a first sharpness factor and a first texture factor of a feature region of the iron tower; folding, screening and updating the first triangular surfaces, and updating the number of the first triangular surfaces of the iron tower characteristic area; acquiring a second texture factor of a non-characteristic area of the iron tower, performing folding operation, screening operation and updating operation on the second triangular surfaces, and updating the number of the second triangular surfaces of the non-characteristic area; and optimizing the three-dimensional model of the power transmission tower based on the first triangular surface number and the second triangular surface number. By adopting the method, the geometric details of the characteristic region of the three-dimensional model of the power transmission tower can be reserved, and the phenomenon of texture distortion of the optimized power transmission tower model is avoided.

Description

Optimization method, device and computer equipment for three-dimensional model of transmission tower

Technical field

The present application relates to the technical field of oblique photography modeling, and in particular to an optimization method, device, computer equipment, storage medium and computer program product for a three-dimensional model of a transmission tower.

Background technique

Oblique photography is a photography technology developed in the field of international photogrammetry in the past ten years. UAVs are equipped with sensors to collect target images from vertical angles and four tilt angles, and obtain the top surface and side height of the target. Precision texture. This technology can not only reflect the real surface conditions and obtain high-precision target texture information, but also generate real-life three-dimensional models through positioning, fusion, modeling and other technologies.

With the development and innovation of oblique photography modeling technology, the data volume of oblique photography real-scene 3D models is also increasing. The ultra-high data volume will not only prolong the time for computer rendering of the model, but also affect the later storage and management of the model. Come challenge. Most methods for simplifying 3D models in traditional technologies are aimed at simplification of point cloud models or building information models. There is no method specifically used to simplify the real-life 3D model of transmission towers for oblique photography.

Contents of the invention

Based on this, it is necessary to address the above technical problems and provide an optimization method, device, computer equipment, storage medium and computer program product for a three-dimensional model of a transmission tower that is specifically used to simplify the real-life three-dimensional model of oblique photography of a transmission tower, which can reduce the number of transmission towers. While obliquely photographing the number of triangular meshes of the real-life 3D model, the geometric details of the characteristic areas of the transmission tower model should be preserved as much as possible, and the texture distortion of the simplified model should be avoided.

In the first aspect, this application provides an optimization method for a three-dimensional model of a transmission tower. The method includes:

Get the initial oblique photography model;

Segment the initial oblique photography model to obtain the characteristic area of the iron tower and the non-feature area of the iron tower; the characteristic area of the iron tower includes the first triangular surface, and the non-feature area of the iron tower includes the second triangular surface;

Obtain the first sharpness factor and the first texture factor of the tower characteristic area;

Based on the first sharpness factor and the first texture factor, perform folding operations, filtering operations and update operations on the first triangular surfaces, thereby updating the number of first triangular surfaces in the characteristic area of the tower;

Obtain the second texture factor of the non-feature area of the tower, and perform folding, filtering and updating operations on the second triangular surface based on the second texture factor, thereby updating the number of second triangular surfaces in the non-feature area;

An optimized three-dimensional model of the transmission tower is obtained based on the number of first triangular surfaces and the number of second triangular surfaces.

In one embodiment, based on the first sharpness factor and the first texture factor, performing a folding operation, a filtering operation and an updating operation on the first triangular surface, thereby updating the number of first triangular surfaces in the tower characteristic area includes:

Obtain the first error matrix and the first edge folding cost of the tower characteristic area according to the first sharpness factor and the first texture factor;

Screen the first feature target edge according to the first edge folding cost, and perform a folding operation on the first feature target edge;

The first error matrix, the first new vertex and the first folding cost of the edges of the iron tower characteristic area affected by the folding operation are updated, and all edges of the iron tower characteristic area are filtered according to the updated first folding cost to obtain the second Feature target edge;

A folding operation is performed on the second feature target edge until the number of first triangular faces in the tower feature area is reduced to the first expected value.

In one embodiment, obtaining the second texture factor of the non-feature area of the tower, and performing folding, filtering and updating operations on the second triangular surface based on the second texture factor, thereby updating the number of second triangular surfaces in the non-feature area includes: :

Obtain the second texture factor of the non-feature area of the tower, and obtain the second error matrix and the second edge folding cost based on the second texture factor;

Screen the first non-feature target edge according to the second edge folding cost, and perform an edge folding operation on the first non-feature target edge;

The second error matrix, the second new vertex and the second folding cost of the affected non-feature area edges are updated, and all edges of the non-feature area are filtered according to the updated second folding cost to obtain the second non-feature area. target edge;

A folding operation is performed on the second non-feature target edge until the number of second triangular faces in the non-feature area is reduced to a second expected value.

In one embodiment, obtaining the first sharpness factor and the first texture factor of the tower characteristic area includes:

Get the approximate curvature of the edge where the target vertex is located;

Obtain the first sharpness factor of the target vertex based on the approximate curvature;

Get the texture distortion degree of the target edge where the target vertex is located;

The first texture factor of the target edge is obtained according to the degree of texture distortion of the target edge where the target vertex is located.

In one embodiment, obtaining the first error matrix and the first edge folding cost of the tower characteristic area according to the first sharpness factor and the first texture factor includes:

The first sharpness factor and the first texture factor of the tower characteristic area are introduced into the quadratic error measurement algorithm to obtain the first error matrix and the first edge folding cost of the tower characteristic area.

In one embodiment, filtering the first feature target edge according to the first edge folding cost, and performing a folding operation on the first feature target edge includes:

Sort all the edges in the tower feature area according to the folding cost of the first edge, and select the first feature target edge with the smallest folding cost to perform the folding operation.

In the second aspect, this application also provides an optimization device for a three-dimensional model of a transmission tower. The device includes:

An initial oblique photography model acquisition module is used to obtain an initial oblique photography model;

The initial oblique photography model segmentation module is used to segment the initial oblique photography model to obtain the tower's characteristic area and the tower's non-feature area; the tower's characteristic area includes the first triangular surface, and the tower's non-feature area includes the second triangular surface;

The sharpness factor and texture factor acquisition module is used to obtain the first sharpness factor and the first texture factor of the tower characteristic area;

The tower feature area processing module is used to perform folding operations, filtering operations and update operations on the first triangular surface based on the first sharpness factor and the first texture factor, thereby updating the number of first triangular surfaces in the tower feature area;

The non-feature area processing module of the tower is used to obtain the second texture factor of the non-feature area of the tower, and perform folding, filtering and update operations on the second triangular surface based on the second texture factor, thereby updating the second triangle of the non-feature area. Number of sides;

The three-dimensional model processing module of the transmission tower is used to obtain an optimized three-dimensional model of the transmission tower based on the number of first triangular surfaces and the number of second triangular surfaces.

In a third aspect, this application also provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it implements the following steps:

Get the initial oblique photography model;

Segment the initial oblique photography model to obtain the characteristic area of the iron tower and the non-feature area of the iron tower; the characteristic area of the iron tower includes the first triangular surface, and the non-feature area of the iron tower includes the second triangular surface;

Obtain the first sharpness factor and the first texture factor of the tower characteristic area;

Based on the first sharpness factor and the first texture factor, perform folding operations, filtering operations and update operations on the first triangular surfaces, thereby updating the number of first triangular surfaces in the characteristic area of the tower;

Obtain the second texture factor of the non-feature area of the tower, and perform folding, filtering and updating operations on the second triangular surface based on the second texture factor, thereby updating the number of second triangular surfaces in the non-feature area;

An optimized three-dimensional model of the transmission tower is obtained based on the number of first triangular surfaces and the number of second triangular surfaces.

In a fourth aspect, this application also provides a computer-readable storage medium. The computer-readable storage medium has a computer program stored thereon. When the computer program is executed by the processor, the following steps are implemented:

Get the initial oblique photography model;

Segment the initial oblique photography model to obtain the characteristic area of the iron tower and the non-feature area of the iron tower; the characteristic area of the iron tower includes the first triangular surface, and the non-feature area of the iron tower includes the second triangular surface;

Obtain the first sharpness factor and the first texture factor of the tower characteristic area;

Based on the first sharpness factor and the first texture factor, perform folding operations, filtering operations and update operations on the first triangular surfaces, thereby updating the number of first triangular surfaces in the characteristic area of the tower;

Obtain the second texture factor of the non-feature area of the tower, and perform folding, filtering and updating operations on the second triangular surface based on the second texture factor, thereby updating the number of second triangular surfaces in the non-feature area;

An optimized three-dimensional model of the transmission tower is obtained based on the number of first triangular surfaces and the number of second triangular surfaces.

In a fifth aspect, this application also provides a computer program product. The computer program product includes a computer program that implements the following steps when executed by a processor:

Get the initial oblique photography model;

Segment the initial oblique photography model to obtain the characteristic area of the iron tower and the non-feature area of the iron tower; the characteristic area of the iron tower includes the first triangular surface, and the non-feature area of the iron tower includes the second triangular surface;

Obtain the first sharpness factor and the first texture factor of the tower characteristic area;

Based on the first sharpness factor and the first texture factor, perform folding operations, filtering operations and update operations on the first triangular surfaces, thereby updating the number of first triangular surfaces in the characteristic area of the tower;

Obtain the second texture factor of the non-feature area of the tower, and perform folding, filtering and updating operations on the second triangular surface based on the second texture factor, thereby updating the number of second triangular surfaces in the non-feature area;

An optimized three-dimensional model of the transmission tower is obtained based on the number of first triangular surfaces and the number of second triangular surfaces.

The above-mentioned optimization method, device, computer equipment, storage medium and computer program product of the three-dimensional model of the transmission tower obtains the tower characteristic area and the tower non-characteristic area by obtaining the initial oblique photography model and segmenting the initial oblique photography model, where the tower characteristic area includes the first The first triangular surface and the non-characteristic area of the tower include the second triangular surface. Different influencing factors can be introduced into the two areas according to specific needs. At the same time, the degree of simplification of each area can also be controlled separately according to actual needs, making it more flexible. By obtaining the first sharpness factor and the first texture factor of the iron tower characteristic area, based on the first sharpness factor and the first texture factor, folding operations, filtering operations, and updating operations are performed on the first triangular surface, thereby updating the characteristics of the iron tower characteristic area. The first number of triangles and the introduction of the sharpness factor can simplify the model while retaining more geometric details of the characteristic areas of the transmission tower model. Obtain the second texture factor of the non-feature area of the tower. Based on the second texture factor, perform folding operations, filtering operations and update operations on the second triangular surface, thereby updating the number of second triangular surfaces in the non-feature area. Introducing the texture factor can avoid The simplified real-life 3D model of the transmission tower with oblique photography appears to have texture distortion. An optimized three-dimensional model of the transmission tower is obtained based on the number of first triangular surfaces and the number of second triangular surfaces, reducing the number of triangular meshes of the transmission tower model and improving the quality of the real-life three-dimensional model of the transmission tower oblique photography.

Description of the drawings

Figure 1 is a schematic flow chart of an optimization method for a three-dimensional model of a transmission tower in one embodiment;

Figure 2 is a schematic flow chart of an optimization method for a three-dimensional model of a transmission tower in another embodiment;

Figure 3 is a module schematic diagram of an optimization device for a three-dimensional model of a transmission tower in one embodiment;

Figure 4 is an internal structure diagram of a computer device in one embodiment.

Detailed ways

In traditional 3D model simplification technology, algorithms such as vertex clustering method, envelope mesh method, region merging method, wavelet decomposition method, edge folding method, etc. are usually used to simplify the triangular faces of the model. Although the above simplification algorithms can be used to a certain extent, To a certain extent, the deformation of the model caused by simplification is improved, but using a uniform error threshold for the model can easily cause the loss of details in the model's feature areas. In addition, since the real-life 3D model of oblique photography also contains texture information, directly using the traditional simplified algorithm will cause texture distortion in the transmission tower model.

Oblique photography is a photography technology developed in the international field of photogrammetry in the past decade. This technology uses drones equipped with sensors to collect target images from vertical angles and four tilt angles, and obtain the top and side views of the target. of high-precision textures. This technology can not only reflect the real surface conditions and obtain high-precision target texture information, but also generate real-life three-dimensional models through positioning, fusion, modeling and other technologies.

At present, oblique photography modeling technology has been widely used in many fields such as cadastral surveying and mapping, engineering surveying, building construction, agriculture and forestry, smart cities, transportation planning, BIM design, etc. At the same time, this technology is also increasingly used in power engineering. , mainly including the application directions of terrain surveying and mapping in the early stage of power design, power line route planning, power line inspection, and rapid investigation of geological conditions around transmission lines. Although the real-life 3D models constructed with traditional technologies are highly accurate, the model's data volume is huge, which is not conducive to computer loading and rendering.

With the development and innovation of oblique photography modeling technology, the data volume of oblique photography real-scene 3D models is also increasing. The ultra-high data volume will not only prolong the time for computer rendering of the model, but also affect the later storage and management of the model. Come challenge. Therefore, how to reduce the data volume of the oblique photography real-life three-dimensional model and reduce the time for computer rendering of the model has become an urgent problem that needs to be solved. Domestic and foreign scholars have proposed many methods for simplification of 3D models. However, most of the methods are aimed at simplification of point cloud models or building information models. There is no method specifically used to simplify the real-life 3D model of transmission towers for oblique photography. Among traditional methods, although some methods can simplify the processing of real-life 3D models, they cannot preserve the detailed features of the model; some methods can effectively preserve the detailed features of the model, but the simplified model will suffer from texture distortion.

This application proposes an optimization method to improve the quality of the real-life three-dimensional model of the transmission tower oblique photography. It can reduce the number of triangular meshes of the transmission tower model while retaining the geometric details of the characteristic areas of the transmission tower model as much as possible, and avoid optimization. The transmission tower model has texture distortion.

In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

In one embodiment, as shown in Figure 1, a method for optimizing a three-dimensional model of a transmission tower is provided. This embodiment illustrates the application of this method to a terminal. It can be understood that this method can also be applied to a server. It can also be applied to systems including terminals and servers, and is implemented through the interaction between terminals and servers. In this embodiment, the method includes the following steps:

Step 102: Obtain the initial oblique photography model.

In one embodiment, the oblique photography real-view three-dimensional model of the transmission tower is read, which is the initial oblique photography model.

Step 104: Segment the initial oblique photography model to obtain the characteristic area of the iron tower and the non-feature area of the iron tower; the characteristic area of the iron tower includes the first triangular surface, and the non-feature area of the iron tower includes the second triangular surface.

In one embodiment, an interactive model segmentation tool is used to segment the initial oblique photography model. The final segmentation goal is to segment the angle steel intersection area and the angle steel end connection area in the initial oblique photography model. Among them, the angle steel intersection area and the angle steel end connection area are the characteristic areas of the tower.

Step 106: Obtain the first sharpness factor and the first texture factor of the tower characteristic area.

In one of the embodiments, the first triangular surface normal vector and the optimized first vertex normal vector of the tower characteristic area are obtained. Specifically, assume that the three vertices of the first triangular surface of the iron tower characteristic area are P ₁ =(x ₁ ,y ₁ ,z ₁ ), P ₂ =(x ₂ ,y ₂ ,z ₂ ), P ₃ =( x ₃ , y ₃ , z ₃ ). Assume the normal vector of the first triangular surface is n _k = (n _x , n _y , n _z ), and solve the normal vector of the first triangular surface according to formula (1):

Among them, x ₁ , x ₂ , x ₃ respectively represent the abscissa coordinates of the three vertices of the first triangular surface; y ₁ , y ₂ , y ₃ respectively represent the ordinate coordinates of the three vertices of the first triangular surface; z ₁ , z ₂ , z ₃ respectively represents the vertical coordinates of the three vertices of the first triangular surface; n _x , _ny , and n _z represent the spatial coordinates of the normal vector of the first triangular surface.

Furthermore, considering the influence of the first triangular surface area and the first triangular surface shape on the calculation of the normal vector, the first triangular surface area S in the first-order domain of the vertex and the interior angle θ associated with the first triangular surface and the vertex are introduced into In the vertex normal vector calculation formula, the optimized first vertex normal vector is obtained Calculation formula, as shown in formula (2):

Among them, plane(p _i ) is the first triangular surface set in the first-order domain of vertex p _i ; S _k is the area of the k-th first triangular surface in the first-order domain of vertex p _i ; θ _k is the k-th first triangle The interior angle angle associated with the surface and vertex p _i ; n _k is the normal vector of the k-th first triangular surface in the first-order domain of vertex p _i .

Further, the first sharpness factor and the first texture factor of the tower characteristic area are calculated and obtained.

Step 108: Based on the first sharpness factor and the first texture factor, perform a folding operation, a filtering operation, and an updating operation on the first triangular surface, thereby updating the number of first triangular surfaces in the characteristic area of the tower.

In one embodiment, the first sharpness factor and the first texture factor of the tower characteristic area are introduced into a quadratic error metric algorithm (Quadric Error Metrics, QEM) to calculate the first error matrix and the first edge folding cost. Further, the edge folding costs of the tower feature area are sorted from low to high, and the edge with the smallest cost is selected for the folding operation, that is, the first feature target edge. Update the first error matrix, the first new vertex and the first folding cost of the affected edges, then sort all the edges according to the folding cost, and select the edge with the smallest folding cost for the folding operation, that is, the second characteristic target edge, until the iron tower The number of first triangular faces in the feature area is reduced to the first expected value.

Step 110: Obtain the second texture factor of the non-feature area of the tower, and perform folding, filtering and updating operations on the second triangular surface based on the second texture factor, thereby updating the number of second triangular surfaces in the non-feature area.

In one embodiment, for the non-feature area, the second texture factor is calculated and introduced into the quadratic error measurement algorithm to obtain the optimized quadratic error matrix and edge folding cost of the non-feature area, That is, after the second error matrix and the second side folding cost, all edges are sorted from low to high according to the second side folding cost, and the edge with the smallest cost is selected for the folding operation, that is, the first non-feature target edge. After each folding is completed, the folding error of the affected edge, the second new vertex and the second folding cost are updated, where the folding cost is the second error matrix. Repeat the steps of obtaining the second error matrix and the second edge folding cost, sorting all edges from low to high according to the folding cost, and selecting the edge with the smallest cost for the folding operation until the number of triangular faces in the non-feature area of the tower drops to the second Expected value.

Step 112: Obtain an optimized three-dimensional model of the transmission tower based on the number of first triangular surfaces and the number of second triangular surfaces.

In the optimization method of the three-dimensional model of the transmission tower mentioned above, by obtaining the initial oblique photography model and segmenting the initial oblique photography model, the characteristic area of the tower and the non-feature area of the tower are obtained, where the characteristic area of the tower includes the first triangular surface, and the non-feature area of the tower includes the second triangular surface. For the triangular surface, by obtaining the first sharpness factor and the first texture factor of the characteristic area of the tower, based on the first sharpness factor and the first texture factor, the first triangular surface is folded, filtered and updated to update the tower. The number of the first triangular faces in the feature area is used to obtain the second texture factor of the non-feature area of the tower. Based on the second texture factor, the second triangular face is folded, filtered and updated to update the second triangle of the non-feature area. The number of faces, the optimized three-dimensional model of the transmission tower is obtained based on the number of the first triangular face and the number of the second triangular face. This optimization method can retain the geometric details of the characteristic area of the three-dimensional model of the transmission tower and avoid texture distortion in the simplified transmission tower model. At the same time, the number of triangular meshes in the real-life 3D model of the oblique photography of the transmission tower is reduced to support lightweight modeling. Among them, the area of the triangular surface in the first-order domain of the vertex and the internal angle angle associated with the triangular surface and the vertex are introduced into the calculation formula of the vertex normal vector, which can avoid the influence of the area and shape of the triangular surface on the calculation of the vertex normal vector and improve the sharpness Factor accuracy. Among them, the area of the triangular surface in the first-order domain of the vertex and the internal angle angle associated with the triangular surface and the vertex are introduced into the calculation formula of the vertex normal vector to avoid the influence of the area and shape of the triangular surface on the calculation of the vertex normal vector and improve the sharpness factor. accuracy.

In one embodiment, based on the first sharpness factor and the first texture factor, performing a folding operation, a filtering operation and an updating operation on the first triangular surface, thereby updating the number of first triangular surfaces in the tower characteristic area includes:

Obtain the first error matrix and the first edge folding cost of the tower characteristic area according to the first sharpness factor and the first texture factor; filter the first characteristic target edge according to the first edge folding cost, and perform a folding operation on the first characteristic target edge; The first error matrix, the first new vertex and the first folding cost of the edges of the iron tower characteristic area affected by the folding operation are updated, and all edges of the iron tower characteristic area are filtered according to the updated first folding cost to obtain the second Feature target edge; perform a folding operation on the second feature target edge until the number of first triangular faces in the tower feature area is reduced to the first expected value.

In one of the embodiments, the first sharpness factor and the first texture factor of the tower characteristic area are obtained.

Furthermore, the first sharpness factor and the first texture factor of the tower characteristic area are introduced into the quadratic error measurement algorithm, and the optimized folded edge quadratic error matrix and edge folding cost are calculated, that is, the first error matrix and the first Edge folding cost.

Specifically, the optimized folded edge quadratic error matrix is recorded as Represents the first error matrix of edge (p _i ,p _j ), denoted as/>

Furthermore, the first error matrix is introduced into the edge folding cost calculation formula to obtain the optimized edge folding cost, that is, the first edge folding cost, which is recorded as Cost (p _i , p _j ).

Further, all edges are sorted from low to high according to the folding cost of the first edge, and the edge with the smallest cost is selected, which is the first feature target edge, and a folding operation is performed on the first feature target edge. Specifically, the partial derivative of the edge folding cost calculation formula is obtained to obtain the coordinates of the first new vertex after edge folding. The calculation method is as shown in formula (3):

when When reversible,/> if Irreversible, then calculate the first new vertex coordinate according to formula (4):

Among them, ω _i and ω _j represent the weights of vertex p _i and vertex p _j respectively; S _i represents the total area of the first triangular surface in the first-order domain of vertex p _i ; S _j represents the first triangular surface in the first-order domain of vertex p _j The sum of the areas of triangular faces.

Further, after each folding is completed, the folding error, the first new vertex and the first folding cost of the affected edge are updated, and the first sharpness factor and the first texture factor of the tower characteristic area are repeatedly introduced into the quadratic error measurement algorithm , calculate the first error matrix and the first edge folding cost, sort all edges from low to high according to the folding cost, select the edge with the smallest cost, that is, the second feature target edge, and perform the folding operation on the second feature target edge A step of. The iteration is terminated after the first triangular surface number N _f in the tower characteristic area satisfies formula (5). Among them, the folding error is the first error matrix.

N _f ≤E (5)

Among them, E represents the preset expected value of the number of first triangle faces.

In this embodiment, the first error matrix and the first edge folding cost of the tower characteristic area are obtained according to the first sharpness factor and the first texture factor; the first characteristic target edge is screened according to the first edge folding cost, and the first characteristic target edge is filtered according to the first edge folding cost. The feature target edge is folded; the first error matrix, the first new vertex and the first folding cost of the tower feature area edges affected by the folding operation are updated, and all edges in the tower feature area are updated based on the updated first folding cost. Perform a filtering operation to obtain the second feature target edge; perform a folding operation on the second feature target edge until the number of first triangular faces in the tower feature area is reduced to the first expected value, and perform a simplified operation on the tower feature area. Aiming at the fact that the angle steel intersection area and the angle steel end connection area of the real-life three-dimensional model of the transmission tower oblique photography have the characteristics of many sharp parts, the method of introducing the sharpness factor can simplify the model while retaining more of the geometry of the characteristic area of the transmission tower model. Shape details.

In one embodiment, obtaining the second texture factor of the non-feature area of the tower, and performing folding, filtering and updating operations on the second triangular surface based on the second texture factor, thereby updating the number of second triangular surfaces in the non-feature area includes: :

Obtain the second texture factor of the non-featured area of the tower, obtain the second error matrix and the second edge folding cost based on the second texture factor; filter the first non-featured target edge based on the second edge folding cost, and classify the first non-featured target edge Perform an edge folding operation; update the second error matrix, second new vertex and second folding cost of the affected non-feature area edges, and perform a filtering operation on all edges in the non-feature area based on the updated second folding cost. Obtain the second non-feature target edge; perform a folding operation on the second non-feature target edge until the number of second triangular faces in the non-feature area is reduced to the second expected value.

For the non-feature area of the tower, the second texture factor is calculated and introduced into the calculation formula of the edge folding cost to obtain the optimized quadratic error matrix and edge folding cost of the non-feature area of the tower, which is the second error matrix and the second The edge folding cost is calculated as shown in formula (6):

Among them, Q(pi ₎ and Q(p _j ) represent the quadratic error matrices of points p _i and p _j respectively; Represents the quadratic error matrix after optimization of edge (p _i , p _j ), that is, the second error matrix;/> is the edge (p _i , p _j ) folded to the new second vertex position; texture (p _i , p _j ) represents the second texture factor of the edge (p _i , p _j ); FCost (p _i , p _j ) represents The optimized folding cost of edge (p _i , p _j ) is the folding cost of the second edge.

Further, all edges are sorted from low to high according to the folding cost of the second edge, and the edge with the smallest cost, that is, the first non-feature target edge, is selected for the folding operation. Update the second error matrix, the second new vertex and the second folding cost of the non-feature area edges affected by the folding operation, sort all edges from low to high according to the updated second folding cost, and obtain the second non-feature target Edge, perform a folding operation on the second non-feature target edge until the number of second triangular faces in the non-feature area is reduced to the second expected value.

In this embodiment, only the second texture factor is calculated for the non-feature area of the tower to reduce the calculation amount of the algorithm. Then the second texture factor and the quadratic error measure are combined as the edge folding cost of this area, which can achieve the purpose of simplifying the oblique photography model of the transmission tower.

In one embodiment, obtaining the first sharpness factor and the first texture factor of the tower characteristic area includes:

Obtain the approximate curvature of the edge where the target vertex is located; obtain the first sharpness factor of the target vertex based on the approximate curvature; obtain the texture distortion degree of the target edge where the target vertex is located; obtain the first texture of the target edge based on the texture distortion degree of the target edge where the target vertex is located factor.

Specifically, to calculate the first sharpness factor of the tower's characteristic area, we first need to calculate the approximate curvature of the edges ( _pi , p _j ), as shown in formula (7):

Among them, C( _pi ,p _j ) represents the approximate curvature of the edge ( _pi ,p _j ); Represents the angle between the normal vector of vertex p _i and the normal vector of vertex p _j ; ||p _i -p _j || represents the Euclidean distance between vertex p _i and vertex p _j .

Then calculate the first sharpness factor of vertex p _i based on the approximate curvature, as shown in formula (8):

in, Represents the first sharpness factor of vertex p _i ; p _j is a point in the first-order domain of p _i ; m is the number of edges containing vertex p _i .

Further, the first texture factor of the characteristic area of the tower is obtained. Specifically, it is first necessary to calculate the triangular mesh texture density distribution in the first-order domain of the vertex. The calculation method is as shown in formula (9):

Among them, density(p _i ) represents the degree of triangular mesh texture density distribution in the first-order domain of vertex p _i ; A represents the average texture density of the triangular mesh of vertex p _i ; N represents the number of triangular meshes in the first-order domain of vertex p _i ; k represents the k-th first triangular surface in the first-order domain of vertex p _i ; S _kt represents the texture area of the k-th first triangular surface in the first-order domain of vertex p _i .

Furthermore, the texture distortion degree distortion(p _i ,p _j ) is calculated and obtained. The calculation method is as shown in formula (10):

distortion(p _i ,p _j )=density(p _i )+density(p _j ) (10)

Among them, density(p _i ) represents the degree of texture distortion of vertex p _i , and density(p _j ) represents the degree of texture distortion of vertex p _j .

In order to prevent texture distortion after model simplification, the first texture factor of the edge ( _pi , p _j ) is calculated according to formula (11):

texture(p _i ,p _j )＝||p _i -p _j ||*distortion(p _i ,p _j ) (11)

Among them, ||p _i -p _j || represents the Euclidean distance between vertex p _i and vertex p _j ; distortion(p _i ,p _j ) represents the degree of texture distortion caused by folding edge (p _i ,p _j ).

In this embodiment, the approximate curvature of the edge where the target vertex is located is obtained; the first sharpness factor of the target vertex is obtained according to the approximate curvature; the degree of texture distortion of the target edge where the target vertex is located is obtained; and the degree of texture distortion of the target edge where the target vertex is located is obtained. The first texture factor of the target edge, where the sharpness factor is the approximate average curvature of the edge connected to the vertex, and the product of the edge length and the texture distortion degree after folding of the edge is defined as the texture factor, where the texture distortion degree after edge folding It is defined as the sum of the triangular mesh texture density distribution in the first-order domain at the two end points of the edge. Introducing sharpness factors and texture factors to the characteristic areas of the tower can retain the geometric details of the characteristic areas of the three-dimensional model of the transmission tower and avoid texture distortion in the simplified transmission tower model.

In one embodiment, obtaining the first error matrix and the first edge folding cost of the tower characteristic area according to the first sharpness factor and the first texture factor includes:

The first sharpness factor and the first texture factor of the tower characteristic area are introduced into the quadratic error measurement algorithm to obtain the first error matrix and the first edge folding cost of the tower characteristic area.

first error matrix The calculation method is as shown in formula (12):

Among them, Q(pi ₎ and Q(p _j ) represent the quadratic error matrices of points p _i and p _j respectively; Represents the quadratic error matrix after optimization of edge (p _i , p _j ), that is, the first error matrix, denoted as/> It is the edge (p _i , p _j ) that is folded to the new vertex position, recorded as/> That is the first new vertex.

The optimized folded edge quadratic error matrix is introduced into the edge folding cost calculation formula to obtain the optimized edge folding cost, that is, the first edge folding cost. The calculation method of the first edge folding cost is as shown in formula (13):

In this embodiment, by introducing the first sharpness factor and the first texture factor of the iron tower characteristic area into the secondary error measurement algorithm, the first error matrix and the first edge folding cost of the iron tower characteristic area are obtained, and the first sharpness factor is fully utilized. The degree factor and the first texture factor calculate the error and edge folding cost, paving the way for calculating the coordinates of the new vertices generated after the folding operation.

In one embodiment, filtering the first feature target edge according to the first edge folding cost, and performing a folding operation on the first feature target edge includes:

Sort all the edges in the tower feature area according to the folding cost of the first edge, and select the first feature target edge with the smallest folding cost to perform the folding operation.

In this embodiment, all the edges in the characteristic area of the tower are sorted according to the folding cost of the first edge, and the first characteristic target edge with the smallest folding cost is selected for the folding operation. The most suitable first characteristic target edge is selected for subsequent folding operations. Make preparations for the update operation.

In one embodiment, as shown in Figure 2, an optimization method for a three-dimensional model of a transmission tower is provided.

Step 202: Obtain the initial oblique photography model.

Step 204: Segment the initial oblique photography model to obtain the characteristic area of the iron tower and the non-feature area of the iron tower; the characteristic area of the iron tower includes the first triangular surface, and the non-feature area of the iron tower includes the second triangular surface.

Step 206: Obtain the approximate curvature of the edge where the target vertex is located.

Step 208: Obtain the first sharpness factor of the target vertex according to the approximate curvature.

Step 210: Obtain the texture distortion degree of the target edge where the target vertex is located.

Step 212: Obtain the first texture factor of the target edge according to the degree of texture distortion of the target edge where the target vertex is located.

Step 214: Introduce the first sharpness factor and the first texture factor of the tower characteristic area into the quadratic error measurement algorithm to obtain the first error matrix and the first edge folding cost of the tower characteristic area.

Step 216: Sort all the edges in the tower feature area according to the folding cost of the first edge, and select the first feature target edge with the smallest folding cost to perform the folding operation.

Step 218: Update the first error matrix, first new vertex and first folding cost of the edges of the tower characteristic area affected by the folding operation, and perform a screening operation on all edges of the tower characteristic area according to the updated first folding cost. Get the second feature target edge.

Step 220: Perform a folding operation on the second feature target edge until the number of first triangular faces in the tower feature area is reduced to the first expected value.

Step 222: Obtain the second texture factor, root, of the non-feature area of the tower. The second texture factor acquires the second error matrix and the second edge folding cost.

Step 224: Screen the first non-feature target edge according to the second edge folding cost, and perform an edge folding operation on the first non-feature target edge.

Step 226: Update the second error matrix, the second new vertex and the second folding cost of the affected non-feature area edges, perform a filtering operation on all edges of the non-feature area according to the updated second folding cost, and obtain the third Two non-feature target edges.

Step 228: Perform a folding operation on the second non-feature target edge until the number of second triangular faces in the non-feature area is reduced to the second expected value.

Step 230: Obtain an optimized three-dimensional model of the transmission tower based on the number of first triangular surfaces and the number of second triangular surfaces.

In this embodiment, the initial oblique photography model of the transmission tower is first read, and the interactive model segmentation tool is used to segment the model. The final segmentation goal is to segment the angle steel intersection area and the angle steel end connection area in the initial oblique photography model. In subsequent steps, the angle steel intersection area and the angle steel end connection area in the initial oblique photography model are collectively referred to as the tower feature area. After the model segmentation is completed, for the characteristic area of the tower, the sharpness factor and texture factor are calculated, and the sharpness factor and texture factor are combined with the quadratic error measure as the edge folding cost of the characteristic area of the tower; for the non-feature area of the tower, only the The texture factor is then combined with the quadratic error measure as the edge folding cost for the region. Finally, the edge folding costs are sorted, and the edges are folded in order from low to high, thereby achieving the purpose of simplifying the oblique photography model of the transmission tower.

It should be understood that although the steps in the flowcharts involved in the above-mentioned embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated in this article, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above embodiments may include multiple steps or stages. These steps or stages are not necessarily executed at the same time, but may be completed at different times. The execution order of these steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

Based on the same inventive concept, embodiments of the present application also provide an optimization device for a three-dimensional model of a transmission tower that is used to implement the above-mentioned optimization method for a three-dimensional model of a transmission tower. The solution to the problem provided by this device is similar to the solution recorded in the above method. Therefore, the specific limitations in the embodiments of the optimization device for one or more three-dimensional models of transmission towers provided below can be found in the above description of transmission towers. The limitations of the optimization method of the three-dimensional model will not be described again here.

In one embodiment, as shown in Figure 3, an optimization device for a three-dimensional model of a transmission tower is provided, including: an initial oblique photography model acquisition module 302, an initial oblique photography model segmentation module 304, and a sharpness factor and texture factor acquisition module. 306. Tower characteristic area processing module 308, tower non-characteristic area processing module 310 and transmission tower three-dimensional model processing module 312, wherein:

The initial oblique photography model acquisition module 302 is used to obtain the initial oblique photography model;

The initial oblique photography model segmentation module 304 is used to segment the initial oblique photography model to obtain the tower characteristic area and the tower non-feature area; the tower characteristic area includes the first triangular surface, and the tower non-feature area includes the second triangular surface.

The sharpness factor and texture factor obtaining module 306 is used to obtain the first sharpness factor and the first texture factor of the tower characteristic area.

The tower feature area processing module 308 is configured to perform a folding operation, a filtering operation and an update operation on the first triangular surface based on the first sharpness factor and the first texture factor, thereby updating the number of first triangular surfaces in the tower feature area.

The non-feature area processing module 310 of the tower is used to obtain the second texture factor of the non-feature area of the tower, and perform folding, filtering and updating operations on the second triangular surface based on the second texture factor, thereby updating the second texture factor of the non-feature area. Number of triangles.

The three-dimensional model processing module 312 of the transmission tower is used to obtain an optimized three-dimensional model of the transmission tower based on the number of first triangular surfaces and the number of second triangular surfaces.

In one embodiment, the tower characteristic area processing module 308 also includes:

The first error matrix and the first edge folding cost acquisition module is used to obtain the first error matrix and the first edge folding cost of the tower characteristic area according to the first sharpness factor and the first texture factor;

The first feature target edge screening module is used to screen the first feature target edge according to the first edge folding cost and perform a folding operation on the first feature target edge;

The second feature target edge screening module is used to update the first error matrix, the first new vertex and the first folding cost of the tower feature area edges affected by the folding operation, and classify the tower features according to the updated first folding cost. All edges in the area are filtered to obtain the second feature target edge;

The first triangular surface number update module is used to perform a folding operation on the second feature target edge until the first triangular surface number in the tower feature area is reduced to the first expected value.

In one embodiment, the tower non-characteristic area processing module 310 also includes:

The second error matrix and the second edge folding cost acquisition module is used to obtain the second texture factor of the non-feature area of the tower, and obtain the second error matrix and the second edge folding cost according to the second texture factor;

The first non-feature target edge screening module is used to screen the first non-feature target edge according to the second edge folding cost, and perform an edge folding operation on the first non-feature target edge;

The second non-feature target edge screening module is used to update the second error matrix, the second new vertex and the second folding cost of the affected non-feature area edges, and classify the non-feature area according to the updated second folding cost. Perform a filtering operation on all edges to obtain the second non-feature target edge;

The second triangular surface number updating module is used to perform a folding operation on the second non-feature target edge until the second triangular surface number of the non-feature area is reduced to the second expected value.

In one embodiment, the sharpness factor and texture factor acquisition module 306 also includes:

The approximate curvature acquisition module is used to obtain the approximate curvature of the edge where the target vertex is located;

The first sharpness factor calculation module is used to obtain the first sharpness factor of the target vertex based on the approximate curvature;

The texture distortion degree calculation module is used to obtain the texture distortion degree of the target edge where the target vertex is located;

The first texture factor calculation module is used to obtain the first texture factor of the target edge according to the degree of texture distortion of the target edge where the target vertex is located.

In one embodiment, the tower characteristic area processing module 308 also includes:

The quadratic error measurement algorithm reference module is used to introduce the first sharpness factor and the first texture factor of the tower characteristic area into the quadratic error measurement algorithm, and obtain the first error matrix and the first edge folding cost of the tower characteristic area.

The sorting module is used to sort all the edges of the tower feature area according to the folding cost of the first edge, and select the first feature target edge with the smallest folding cost to perform the folding operation.

Each module in the optimization device of the above-mentioned three-dimensional model of the transmission tower can be realized in whole or in part by software, hardware and their combination. Each of the above modules may be embedded in or independent of the processor of the computer device in the form of hardware, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. 4 . The computer device includes a processor, memory, input/output interface, communication interface, display unit and input device. Among them, the processor, memory and input/output interface are connected through the system bus, and the communication interface, display unit and input device are connected to the system bus through the input/output interface. Wherein, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile storage media and internal memory. The non-volatile storage medium stores operating systems and computer programs. This internal memory provides an environment for the execution of operating systems and computer programs in non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used for wired or wireless communication with external terminals. The wireless mode can be implemented through WIFI, mobile cellular network, NFC (Near Field Communication) or other technologies. The computer program is executed by a processor to implement an optimization method for a three-dimensional model of a transmission tower. The display unit of the computer equipment is used to form a visually visible picture, and may be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display or an electronic ink display. The input device of the computer device can be a touch layer covered on the display screen, or it can be a button, trackball or touch pad provided on the computer device casing, or it can be External keyboard, trackpad or mouse, etc.

Those skilled in the art can understand that the structure shown in Figure 4 is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied. Specific computer equipment can May include more or fewer parts than shown, or combine certain parts, or have a different arrangement of parts.

In one embodiment, a computer device is also provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, it implements the steps in the above method embodiments.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

In one embodiment, a computer program product is provided, including a computer program that implements the steps in each of the above method embodiments when executed by a processor.

It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all It is information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with the relevant laws, regulations and standards of relevant countries and regions.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage. In the media, when executed, the computer program may include the processes of the above method embodiments. Any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetic variable memory (Magnetoresistive Random) Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene memory, etc. Volatile memory may include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM). The databases involved in the various embodiments provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include blockchain-based distributed databases, etc., but are not limited thereto. The processors involved in the various embodiments provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to this.

The technical features of the above embodiments can be combined in any way. To simplify the description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, all possible combinations should be used. It is considered to be within the scope of this manual.

The above-described embodiments only express several implementation modes of the present application, and their descriptions are relatively specific and detailed, but should not be construed as limiting the patent scope of the present application. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all fall within the protection scope of the present application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims (10)

1. An optimization method of a three-dimensional model of a power transmission tower, which is characterized by comprising the following steps:

acquiring an initial oblique photography model;

dividing the initial oblique photography model to obtain a characteristic region of the iron tower and a non-characteristic region of the iron tower; the iron tower characteristic region comprises a first triangular surface, and the iron tower non-characteristic region comprises a second triangular surface;

acquiring a first sharpness factor and a first texture factor of the iron tower characteristic region;

Based on the first sharpness factor and the first texture factor, performing folding operation, screening operation and updating operation on the first triangular surface, so as to update the first triangular surface number of the iron tower characteristic region;

acquiring a second texture factor of the non-characteristic region of the iron tower, and performing folding operation, screening operation and updating operation on the second triangular surfaces based on the second texture factor, so as to update the number of the second triangular surfaces of the non-characteristic region;

and obtaining an optimized three-dimensional model of the power transmission tower based on the first triangular surface number and the second triangular surface number.

2. The method of claim 1, wherein the performing a folding operation, a filtering operation, and an updating operation on the first triangle based on the first sharpness factor and the first texture factor, thereby updating the first number of triangles of the pylon feature area comprises:

acquiring a first error matrix and a first edge folding cost of a characteristic region of the iron tower according to the first sharpness factor and the first texture factor;

screening a first characteristic target edge according to the first edge folding cost, and carrying out folding operation on the first characteristic target edge;

Updating a first error matrix, a first new vertex and a first folding cost of the iron tower characteristic region side affected by the folding operation, and screening all sides of the iron tower characteristic region according to the updated first folding cost to obtain a second characteristic target side;

and carrying out folding operation on the second characteristic target edge until the number of the first triangular faces of the iron tower characteristic region is reduced to a first expected value.

3. The method of claim 1, wherein the obtaining a second texture factor for the non-feature region of the pylon, and performing a folding operation, a filtering operation, and an updating operation on the second triangular surface based on the second texture factor, thereby updating a second number of triangular surfaces for the non-feature region comprises:

acquiring a second texture factor of a non-characteristic region of the iron tower, and acquiring a second error matrix and a second edge folding cost according to the second texture factor;

screening a first non-characteristic target edge according to the second edge folding price, and performing edge folding operation on the first non-characteristic target edge;

updating the second error matrix, the second new vertex and the second folded price of the affected non-characteristic region side, and screening all sides of the non-characteristic region according to the updated second folded price to obtain a second non-characteristic target side;

And carrying out folding operation on the second non-characteristic target edge until the number of the second triangular faces of the non-characteristic area is reduced to a second expected value.

4. The method of claim 1, wherein the obtaining a first sharpness factor and a first texture factor for the pylon feature area comprises:

obtaining the approximate curvature of the edge where the target vertex is located;

acquiring a first sharpness factor of a target vertex according to the approximate curvature;

obtaining the texture distortion degree of a target edge where a target vertex is located;

and acquiring a first texture factor of the target edge according to the texture distortion degree of the target edge where the target vertex is located.

5. The method of claim 2, wherein the obtaining a first error matrix and a first edge folding cost for the pylon feature area based on the first sharpness factor and the first texture factor comprises:

and introducing the first sharpness factor and the first texture factor of the iron tower characteristic region into a secondary error measurement algorithm to obtain a first error matrix and a first edge folding cost of the iron tower characteristic region.

6. The method of claim 2, wherein the filtering the first feature object edge according to the first edge folding cost, and performing a folding operation on the first feature object edge comprises:

And sequencing all sides of the iron tower characteristic region according to the first side folding cost, and selecting a first characteristic target side with the minimum folding cost for folding operation.

7. An optimization device for a three-dimensional model of a power transmission tower, which is characterized by comprising:

the initial oblique photography model acquisition module is used for acquiring an initial oblique photography model;

the initial oblique photography model segmentation module is used for segmenting the initial oblique photography model to obtain a characteristic region of the iron tower and a non-characteristic region of the iron tower; the iron tower characteristic region comprises a first triangular surface, and the iron tower non-characteristic region comprises a second triangular surface;

the sharpness factor and texture factor acquisition module is used for acquiring a first sharpness factor and a first texture factor of the iron tower characteristic region;

the iron tower characteristic region processing module is used for carrying out folding operation, screening operation and updating operation on the first triangular surface based on the first sharpness factor and the first texture factor so as to update the first triangular surface number of the iron tower characteristic region;

the iron tower non-characteristic region processing module is used for acquiring a second texture factor of the iron tower non-characteristic region, and carrying out folding operation, screening operation and updating operation on the second triangular surface based on the second texture factor so as to update the second triangular surface number of the non-characteristic region;

And the transmission tower three-dimensional model processing module is used for obtaining an optimized transmission tower three-dimensional model based on the first triangular surface number and the second triangular surface number.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.

9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.