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

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

Info

Publication number
CN116863137A
CN116863137A CN202310813777.9A CN202310813777A CN116863137A CN 116863137 A CN116863137 A CN 116863137A CN 202310813777 A CN202310813777 A CN 202310813777A CN 116863137 A CN116863137 A CN 116863137A
Authority
CN
China
Prior art keywords
iron tower
factor
characteristic region
characteristic
folding
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202310813777.9A
Other languages
Chinese (zh)
Inventor
陈树平
李成
王彦海
石习双
苗红璞
陈飞
黄学能
全浩
张云
刘康林
严剑锋
张存德
韦保荣
韩玉康
温才权
牛晓雷
黄飞
伍泓乐
陈占翰
申余彪
丁琦
吴锋
彭朋
莫厚鑫
李洪文
甘戈炎
司增威
陆衍任
朱张华
莫铭光
吴海迪
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wuzhou Bureau Csg Ehv Power Transimission Co
Original Assignee
Wuzhou Bureau Csg Ehv Power Transimission Co
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wuzhou Bureau Csg Ehv Power Transimission Co filed Critical Wuzhou Bureau Csg Ehv Power Transimission Co
Priority to CN202310813777.9A priority Critical patent/CN116863137A/en
Publication of CN116863137A publication Critical patent/CN116863137A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G06V10/273—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion removing elements interfering with the pattern to be recognised
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/04—Interpretation of pictures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/42—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
    • G06V10/422—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation for representing the structure of the pattern or shape of an object therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/54—Extraction of image or video features relating to texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/17—Terrestrial scenes taken from planes or by drones
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/176—Urban or other man-made structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G06T2207/10012—Stereo images
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Remote Sensing (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computer Graphics (AREA)
  • Geometry (AREA)
  • Software Systems (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Image Generation (AREA)

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 and device for three-dimensional model of transmission tower and computer equipment
Technical Field
The application relates to the technical field of oblique photography modeling, in particular to an optimization method, an optimization device, computer equipment, a storage medium and a computer program product of a three-dimensional model of a power transmission tower.
Background
Oblique photography is a photographic technology developed in the field of international photogrammetry in recent decades, and a sensor is mounted on an unmanned aerial vehicle to acquire a target image from a vertical angle and four oblique angles, so as to acquire high-precision textures of the top surface and the side view of a target object. The technology can reflect the real ground object situation, acquire high-precision texture information of the target object, and can generate a real three-dimensional model through positioning, fusion, modeling and other technologies.
With the development and innovation of oblique photography modeling technology, the data volume of the oblique photography live-action three-dimensional model is larger and larger, and the ultrahigh data volume not only prolongs the time of rendering the model by a computer, but also brings challenges to the later storage and management of the model. Most of the methods for simplifying the three-dimensional model in the traditional technology are aimed at simplifying a point cloud model or a building information model, and no method special for simplifying the three-dimensional model of the transmission tower oblique photography live-action is available.
Disclosure of Invention
Based on this, it is necessary to provide an optimization method, an apparatus, a computer device, a storage medium and a computer program product for a three-dimensional model of a transmission tower, which are specially used for simplifying the three-dimensional model of the transmission tower oblique photography, so as to reduce the number of triangular meshes of the three-dimensional model of the transmission tower oblique photography, keep the geometric details of the characteristic area of the model of the transmission tower as much as possible, and avoid the texture distortion phenomenon of the simplified model.
In a first aspect, the application provides an optimization method for a three-dimensional model of a power transmission tower. The method comprises 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 a feature region of the iron tower;
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 surface based on the second texture factor, so as to update the second triangular surface number 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.
In one embodiment, 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, such that updating the first number of triangles of the pylon feature area includes:
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 the first error matrix, the first new vertex and the 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.
In one embodiment, obtaining a second texture factor of the non-characteristic area of the iron tower, and performing a folding operation, a screening operation and an updating operation on the second triangular surface based on the second texture factor, so that updating the second triangular surface number of the non-characteristic area includes:
Acquiring a second texture factor of the 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 the first non-characteristic target edge according to the second edge folding price, and carrying out edge folding operation on the first non-characteristic target edge;
updating the second error matrix, the second new vertex and the second folding price of the affected non-characteristic region side, and screening all sides of the non-characteristic region according to the updated second folding cost to obtain a second non-characteristic target side;
and performing folding operation on the second non-characteristic target edge until the number of the second triangular faces of the non-characteristic region is reduced to a second expected value.
In one embodiment, obtaining the first sharpness factor and the 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.
In one embodiment, 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:
Introducing a first sharpness factor and a first texture factor of the iron tower characteristic region into a secondary error measurement algorithm, and obtaining a first error matrix and a first edge folding cost of the iron tower characteristic region.
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:
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.
In a second aspect, the application further provides an optimization device for the three-dimensional model of the transmission tower. The device comprises:
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 surfaces based on the second texture factor so as to update the number of the second triangular surfaces of the non-characteristic region;
and the power transmission tower three-dimensional model processing module is used for obtaining an optimized power transmission tower three-dimensional model based on the first triangular surface number and the second triangular surface number.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize 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 a feature region of the iron tower;
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 surface based on the second texture factor, so as to update the second triangular surface number 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.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
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 a feature region of the iron tower;
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 surface based on the second texture factor, so as to update the second triangular surface number 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.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, performs the steps of:
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 a feature region of the iron tower;
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 surface based on the second texture factor, so as to update the second triangular surface number 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.
According to the optimization method, the device, the computer equipment, the storage medium and the computer program product of the three-dimensional model of the power transmission tower, the initial oblique photography model is obtained, the initial oblique photography model is segmented, and the characteristic region and the non-characteristic region of the power transmission tower are obtained, wherein the characteristic region of the power transmission tower comprises a first triangular surface, the non-characteristic region of the power transmission tower comprises a second triangular surface, different influence factors can be introduced into the two large regions according to specific requirements, and meanwhile, the simplification degree of each region can be controlled respectively according to actual requirements, so that the power transmission tower is more flexible. The first triangular surface is subjected to folding operation, screening operation and updating operation based on the first sharpness factor and the first texture factor by acquiring the first sharpness factor and the first texture factor of the iron tower characteristic region, so that the first triangular surface number of the iron tower characteristic region is updated, the sharpness factor is introduced, and geometric details of the iron tower model characteristic region can be reserved more while the model is simplified. And (3) acquiring a second texture factor of the non-characteristic area of the iron tower, and carrying out folding operation, screening operation and updating operation on the second triangular surface based on the second texture factor, so that the number of the second triangular surfaces of the non-characteristic area is updated, and the texture factor is introduced, so that the phenomenon of texture distortion after the three-dimensional model of the oblique photography live-action of the power transmission iron tower is simplified can be avoided. 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, reducing the triangular grid number of the power transmission tower model, and improving the quality of the three-dimensional model of the power transmission tower oblique photography live-action.
Drawings
FIG. 1 is a flow chart of a method for optimizing a three-dimensional model of a pylon in one embodiment;
FIG. 2 is a flow chart of a method for optimizing a three-dimensional model of a pylon in another embodiment;
FIG. 3 is a schematic block diagram of an optimization device for a three-dimensional model of a pylon in one embodiment;
fig. 4 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In the traditional three-dimensional model simplification technology, algorithms such as a vertex clustering method, an enveloping grid method, a region merging method, a wavelet decomposition method, an edge folding method and the like are generally used for simplifying the triangular surface of the model, and the simplified algorithm can improve the deformation of the model caused by the simplification process to a certain extent, but a unified error threshold is used for the model, so that the detail of the characteristic region of the model is easily lost. In addition, as the oblique photography live-action three-dimensional model also contains texture information, the direct use of the traditional simplification algorithm can cause the phenomenon of texture distortion of the transmission tower model.
Oblique photography is a photographic technology developed in the field of international photogrammetry for more than ten years, and is used for acquiring target images from vertical angles and four oblique angles through a sensor carried by an unmanned aerial vehicle and acquiring high-precision textures of the top surface and side view of a target object. The technology can reflect the real ground object situation, acquire high-precision texture information of the target object, and can generate a real three-dimensional model through positioning, fusion, modeling and other technologies.
At present, the oblique photography modeling technology is widely applied to various fields such as cadastral mapping, engineering measurement, building construction, agriculture forestry, smart city, traffic planning, BIM design and the like, and meanwhile, the technology is increasingly applied to power engineering, and mainly has application directions such as topographic mapping, power line route planning, power line inspection, rapid inspection of surrounding geological conditions of a power transmission line and the like in the early stage of power design. The live-action three-dimensional model constructed by the traditional technology has higher precision, but the data volume of the model is huge, which is unfavorable for loading and rendering by a computer.
With the development and innovation of oblique photography modeling technology, the data volume of the oblique photography live-action three-dimensional model is larger and larger, and the ultrahigh data volume not only prolongs the time of rendering the model by a computer, but also brings challenges to the later storage and management of the model. Therefore, how to reduce the data size of the oblique photography live-action three-dimensional model and reduce the time for rendering the model by a computer is a problem to be solved. Many three-dimensional model simplification methods are proposed by students at home and abroad, however, most of the methods are aimed at simplifying a point cloud model or a building information model, and no method special for simplifying a live-action three-dimensional model of the transmission tower oblique photography exists. In the traditional method, although some methods can simplify the processing of a live-action three-dimensional model, detailed characteristics of the model cannot be reserved; some methods can effectively retain detailed characteristics of the model, but texture warping phenomenon can occur in the model after simplifying the processing.
The application provides an optimization method for improving the quality of a three-dimensional model of a transmission tower oblique photography live-action, which can reduce the number of triangular grids of the transmission tower model, keep geometric details of a characteristic region of the transmission tower model as far as possible, and avoid the phenomenon of texture distortion of the optimized transmission tower model.
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In one embodiment, as shown in fig. 1, an optimization method of a three-dimensional model of a power transmission tower is provided, and the embodiment is applied to a terminal for illustration by the method, and it is understood that the method can also be applied to a server, can also be applied to a system comprising the terminal and the server, and is realized through interaction between the terminal and the server. In this embodiment, the method includes the steps of:
step 102, an initial oblique photography model is acquired.
In one embodiment, a three-dimensional model of the transmission tower oblique photography live-action is read, namely an initial oblique photography model.
104, dividing an initial oblique photography model to obtain a characteristic region of the iron tower and a non-characteristic region of the iron tower; the pylon feature area includes a first triangular face and the pylon non-feature area includes a second triangular face.
In one embodiment, the initial oblique photography model is segmented using an interactive model segmentation tool, and the final segmentation goal is to segment the angle-steel intersection region and the angle-steel end connection region in the initial oblique photography model. Wherein, angle steel intersection region and angle steel end connection region are iron tower characteristic region.
And 106, acquiring a first sharpness factor and a first texture factor of the iron tower characteristic region.
In one embodiment, a first triangular surface normal vector and an optimized first vertex normal vector of a feature area of the iron tower are obtained. Specifically, it is assumed that three vertexes of a first triangular surface of the iron tower characteristic region are P respectively 1 ＝(x 1 ,y 1 ,z 1 )，P 2 ＝(x 2 ,y 2 ,z 2 )，P 3 ＝(x 3 ,y 3 ,z 3 ). Let the normal vector of the first triangular surface be n k ＝(n x ,n y ,n z ) Solving for the normal vector of the first triangular face according to equation (1):
wherein x is 1 、x 2 、x 3 Respectively representing the abscissa of three vertexes of the first triangular surface; y is 1 、y 2 、y 3 Respectively represent a first triangular surface threeThe ordinate of each vertex; z 1 、z 2 、z 3 Vertical coordinates of three vertexes of the first triangular surface are respectively represented; n is n x 、n y 、n z Representing the spatial coordinates of the normal vector of the first triangular surface.
Further, considering the influence of the first triangular area and the first triangular surface shape on normal vector calculation, introducing the first triangular surface area S and the internal angle theta of the first triangular surface associated with the vertex in the vertex first order field into a vertex normal vector calculation formula to obtain an optimized first vertex normal vectorThe calculation formula is shown as formula (2):
wherein plane (p) i ) Is the vertex p i Is a first-order domain first triangular face set; s is S k Is the vertex p i The area of the kth first triangular surface in the first-order field; θ k Is the kth first triangular surface and the vertex p i An associated interior angle; n is n k Is the vertex p i Normal vector of kth first triangular surface in first order field.
Further, a first sharpness factor and a first texture factor are calculated for the pylon feature area.
And step 108, performing 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.
In one embodiment, a first sharpness factor and a first texture factor for a pylon feature region are introduced into a quadratic error metric algorithm (Quadric Error Metrics, QEM) to calculate a first error matrix and a first edge folding cost. Further, the edge folding cost of the iron tower characteristic region is ordered from low to high, and the edge with the minimum cost is selected for folding operation, namely the first characteristic target edge. Updating a first error matrix, a first new vertex and a first folding cost of the affected edge, sorting all edges according to the folding cost, and selecting the edge with the minimum folding cost to carry out folding operation, namely a second characteristic target edge, until the number of the first triangular faces of the iron tower characteristic area is reduced to a first expected value.
Step 110, obtaining a second texture factor of the non-characteristic area of the iron tower, and performing 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 area.
In one embodiment, for the non-characteristic region, a second texture factor is obtained by calculation, the second texture factor is introduced into a secondary error measurement algorithm, a secondary error matrix and a side folding cost after optimization of the non-characteristic region, namely, a second error matrix and a second side folding cost are obtained, all sides are ordered from low to high according to the second side folding cost, and a side with the minimum cost is selected for folding operation, namely, a first non-characteristic target side. And after each folding is completed, updating the folding error of the affected edge, the second new vertex and the second folding cost, wherein the folding cost is the second error matrix. And repeating the steps of acquiring a second error matrix and second edge folding cost, sequencing all edges from low to high according to the folding cost, and selecting the edge with the minimum cost for folding operation until the number of triangular faces of the non-characteristic area of the iron tower is reduced to a second expected value.
And step 112, obtaining an optimized three-dimensional model of the power transmission tower based on the first triangular surface number and the second triangular surface number.
According to the optimization method for the three-dimensional model of the power transmission tower, the initial oblique photography model is acquired, the initial oblique photography model is segmented, the iron tower characteristic area and the iron tower non-characteristic area are obtained, wherein the iron tower characteristic area comprises the first triangular surface, the iron tower non-characteristic area comprises the second triangular surface, the first sharpness factor and the first texture factor of the iron tower characteristic area are acquired, the first triangular surface is subjected to folding operation, screening operation and updating operation based on the first sharpness factor and the first texture factor, the first triangular surface number of the iron tower characteristic area is updated, the second triangular surface is acquired, the second triangular surface number of the non-characteristic area is acquired based on the second texture factor, the optimized three-dimensional model of the power transmission tower is obtained based on the first triangular surface number and the second triangular surface number, the optimization method can keep geometric details of the iron tower of the characteristic area of the power transmission tower three-dimensional model, the texture distortion phenomenon of the simplified power transmission tower is avoided, meanwhile, the triangular surface number of the three-dimensional model of the power transmission tower is reduced, and the three-dimensional model of the power transmission tower is supported in a lightweight mode is modeled. The triangular surface area and the internal angle related to the vertex in the vertex first-order field are introduced into a vertex normal vector calculation formula, so that the influence of the triangular surface area and the triangular surface shape on the vertex normal vector calculation can be avoided, and the accuracy of the sharpness factor is improved. The triangular surface area and the internal angle related to the vertex in the vertex first-order field are introduced into a vertex normal vector calculation formula, so that the influence of the triangular surface area and the triangular surface shape on vertex normal vector calculation is avoided, and the accuracy of the sharpness factor is improved.
In one embodiment, 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, such that updating the first number of triangles of the pylon feature area includes:
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 the first error matrix, the first new vertex and the 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.
In one embodiment, a first sharpness factor and a first texture factor for a pylon feature area are obtained.
Further, introducing a first sharpness factor and a first texture factor of the iron tower characteristic region into a secondary error measurement algorithm, and calculating an optimized folding edge secondary error matrix and edge folding cost, namely a first error matrix and a first edge folding cost.
Specifically, the optimized folded edge quadratic error matrix is recorded asRepresenting edge (p) i ,p j ) Is marked as +.>
Further, the first error matrix is introduced into an edge folding Cost calculation formula to obtain an optimized edge folding Cost, namely a first edge folding Cost, which is marked as Cost (p i ,p j )。
Further, sorting all edges from low to high according to the folding cost of the first edge, screening out the edge with the minimum cost, namely the first characteristic target edge, and carrying out folding operation on the first characteristic target edge. Specifically, the edge folding cost calculation formula is used for solving the bias derivative to obtain the coordinate of the first new vertex after edge folding, and the calculation method is shown as a formula (3):
when (when)Reversible time, ->If it isIrreversible, then the first new vertex coordinates are calculated according to equation (4):
wherein omega i And omega j Respectively represent the vertexes p i And vertex p j Weight of (2); s is S i Representing vertex p i The sum of the areas of the first triangular surfaces in the first-order field; s is S j Representing vertex p j The sum of the areas of the first triangular surfaces in the first-order field.
Further, after each folding is completed, updating the folding error, the first new vertex and the first folding cost of the affected edge, repeatedly introducing the first sharpness factor and the first texture factor of the iron tower characteristic region into a secondary error measurement algorithm, calculating a first error matrix and first edge folding cost, sorting all edges from low to high according to the folding cost, selecting the edge with the minimum cost, namely a second characteristic target edge, and carrying out folding operation on the second characteristic target edge. First triangular surface number N in iron tower characteristic region f The iteration is terminated after equation (5) is satisfied. The folding error is the first error matrix.
N f ≤E (5)
Wherein E represents a preset first triangular face quantity expected value.
In the embodiment, a first error matrix and a first edge folding cost of the iron tower characteristic region are obtained according to a first sharpness factor and a 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 the first error matrix, the first new vertex and the 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, and carrying out simplified operation on the iron tower characteristic region. Aiming at the characteristic that the angle steel intersection area and the angle steel end connection area of the three-dimensional model of the transmission tower oblique photography have more sharp parts, the method for introducing the sharpness factor can simplify the model and simultaneously retain more geometric details of the characteristic area of the transmission tower model.
In one embodiment, obtaining a second texture factor of the non-characteristic area of the iron tower, and performing a folding operation, a screening operation and an updating operation on the second triangular surface based on the second texture factor, so that updating the second triangular surface number of the non-characteristic area includes:
acquiring a second texture factor of the 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 the first non-characteristic target edge according to the second edge folding price, and carrying out edge folding operation on the first non-characteristic target edge; updating the second error matrix, the second new vertex and the second folding price of the affected non-characteristic region side, and screening all sides of the non-characteristic region according to the updated second folding cost to obtain a second non-characteristic target side; and performing folding operation on the second non-characteristic target edge until the number of the second triangular faces of the non-characteristic region is reduced to a second expected value.
For the non-characteristic region of the iron tower, a second texture factor is calculated and is introduced into a calculation formula of edge folding cost, a secondary error matrix and the edge folding cost after the optimization of the non-characteristic region of the iron tower are obtained, namely the second error matrix and the second edge folding cost, and the calculation method is shown as a formula (6):
Wherein Q (p) i ) And Q (p) j ) Respectively represent p i 、p j A secondary error matrix of two points;representing edge (p) i ,p j ) The optimized secondary error matrix is a second error matrix; />Is a side (p) i ,p j ) Folding to a new second vertex position; texture (p) i ,p j ) Representing edge (p) i ,p j ) Is a second texture factor of (2); FCost (p) i ,p j ) Representing edge (p) i ,p j ) The optimized folding cost is the second side folding cost.
Further, all edges are ordered from low to high according to the folding cost of the second edge, and the edge with the smallest cost, namely the first non-characteristic target edge, is selected for folding operation. Updating a second error matrix, a second new vertex and a second folding cost of the edges of the non-characteristic area affected by the folding operation, sorting all edges from low to high according to the updated second folding cost, obtaining a second non-characteristic target edge, and performing the folding operation on the second non-characteristic target edge until the number of second triangular faces of the non-characteristic area is reduced to a second expected value.
In this embodiment, for the non-characteristic region of the tower, only the second texture factor is calculated to reduce the calculation amount of the algorithm. And then combining the second texture factor and the secondary error measure to serve as the edge folding valence of the region, so that the purpose of simplifying the transmission tower oblique photography model can be achieved.
In one embodiment, obtaining the first sharpness factor and the 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.
Specifically, a first sharpness factor of a pylon feature region is calculated, first requiring calculation of an edge (p i ,p j ) As shown in equation (7):
wherein C (p) i ,p j ) Representing edge (p) i ,p j ) Is a similar curvature of (a);representing vertex p i Normal vector and vertex p of (2) j Included angle of normal vector of (2); ||p i -p j I represents vertex p i And vertex p j Is a euclidean distance of (c).
Then obtaining the vertex p according to the approximate curvature calculation i As shown in equation (8):
wherein,,representing vertex p i Is a first sharpness factor of (2); p is p j Is p i Is a point within the first order domain; m is a vertex p i Is the number of edges of the sheet.
Further, a first texture factor of the iron tower characteristic region is obtained. Specifically, firstly, the degree of the distribution of the texture density of the triangular mesh in the vertex first-order field needs to be calculated, and the calculation method is shown in a formula (9):
Wherein, density (p i ) Representing vertex p i The degree of distribution of the texture density of the triangular mesh in the first-order field; a represents the vertex p i Is a triangular mesh average texture density; n represents the vertex p i The number of triangular meshes in the first-order field; k represents the vertex p i A kth first triangular surface in the first order domain; s is S kt Representing vertex p i Texture area of kth first triangular surface in first-order field.
Further, a texture distortion degree distriction (p) i ,p j ) The calculation method is shown in the formula (10):
distortion(p i ,p j )＝density(p i )+density(p j ) (10)
wherein, density (p i ) Representing vertex p i Texture twist degree, density (p j ) Vertex p j Is a texture twist level of (a).
To prevent texture warping after model simplification, edge (p) is calculated according to equation (11) i ,p j ) Is a first texture factor of:
texture(p i ,p j )＝||p i -p j ||*distortion(p i ,p j ) (11)
wherein, ||p i -p j I represents vertex p i And vertex p j Is a Euclidean distance of (2); disorders (p) i ,p j ) Representing the edge (p) i ,p j ) Degree of texture distortion after folding.
In this embodiment, the approximate curvature of the edge where the target vertex is located is obtained; 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 obtaining a first texture factor of the target edge according to the texture distortion degree of the target edge where the target vertex is, wherein the sharpness factor is an approximate curvature average value of the edge connected with the vertex, and defining the product of the edge length and the texture distortion degree after the edge is folded as the texture factor, wherein the texture distortion degree after the edge is folded is defined as the sum of the distribution degree of the triangle mesh texture density in the first-order field of the two end points of the edge. The sharpness factors and the texture factors are introduced into the characteristic areas of the power transmission towers, so that the geometric details of the characteristic areas of the three-dimensional model of the power transmission towers can be reserved, and the phenomenon of texture distortion of the simplified power transmission tower model can be avoided.
In one embodiment, 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:
introducing a first sharpness factor and a first texture factor of the iron tower characteristic region into a secondary error measurement algorithm, and obtaining a first error matrix and a first edge folding cost of the iron tower characteristic region.
First error matrixThe calculation method is shown in the formula (12):
wherein Q (p) i ) And Q (p) j ) Respectively represent p i 、p j A secondary error matrix of two points;representing edge (p) i ,p j ) The optimized quadratic error matrix, i.e. the first error matrix, is denoted +.> Is a side (p) i ,p j ) Folded to a new vertex position, denoted +.>I.e. the first new vertex.
Introducing the optimized folded edge secondary error matrix into an edge folding valence calculation formula to obtain an optimized edge folding valence, namely a first edge folding cost, wherein the calculation method of the first edge folding cost is shown as a formula (13):
in this embodiment, a first sharpness factor and a first texture factor of a feature area of the iron tower are introduced into a secondary error measurement algorithm to obtain a first error matrix and a first edge folding cost of the feature area of the iron tower, and the first sharpness factor and the first texture factor are fully utilized to calculate errors and edge folding cost, so as to pave the coordinates of a new vertex 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:
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.
In this embodiment, all sides of the iron tower feature area are ordered according to the first side folding cost, the first feature target side with the minimum folding cost is selected for folding operation, and the most suitable first feature target side is selected for laying a mat for subsequent folding operation and updating operation.
In one embodiment, as shown in fig. 2, a method for optimizing a three-dimensional model of a pylon is provided.
Step 202, an initial oblique photography model is acquired.
Step 204, 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 pylon feature area includes a first triangular face and the pylon non-feature area includes a second triangular face.
Step 206, obtaining the approximate curvature of the edge where the target vertex is located.
Step 208, obtaining a first sharpness factor for the target vertex based on the approximate curvature.
Step 210, obtaining the texture distortion degree of the target edge where the target vertex is located.
Step 212, obtaining a first texture factor of the target edge according to the texture distortion degree of the target edge where the target vertex is located.
Step 214, introducing a first sharpness factor and a first texture factor of the iron tower characteristic region into a secondary error measurement algorithm, and obtaining a first error matrix and a first edge folding cost of the iron tower characteristic region.
And step 216, sorting 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.
And step 218, updating the first error matrix, the first new vertex and the 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 220, performing folding operation on the second characteristic target edge until the number of the first triangular faces of the iron tower characteristic area is reduced to a first expected value.
Step 222, obtaining a second texture factor of the non-characteristic region of the iron tower, and obtaining a second error matrix and a second edge folding cost by the second texture factor.
Step 224, screening the first non-characteristic target edge according to the second edge folding valence, and performing edge folding operation on the first non-characteristic target edge.
And 226, updating the second error matrix, the second new vertex and the second folding cost of the affected non-characteristic region edge, and screening all the non-characteristic region edges according to the updated second folding cost to obtain a second non-characteristic target edge.
And step 228, performing a folding operation on the second non-characteristic target edge until the second triangular surface number of the non-characteristic region is reduced to a second desired value.
And 230, obtaining an optimized three-dimensional model of the power transmission tower based on the first triangular surface number and the second triangular surface number.
In this embodiment, an initial oblique photography model of the power transmission tower is first read, and the model is segmented by using an interactive model segmentation tool, and the final segmentation target is to segment an angle steel intersection region and an angle steel end connection region in the initial oblique photography model, and the subsequent steps are collectively called as a tower feature region. After model segmentation is completed, calculating sharpness factors and texture factors for the iron tower characteristic region, and combining the sharpness factors and the texture factors with the secondary error measure to serve as edge folding valence of the iron tower characteristic region; for the non-feature region of the pylon, only the texture factor is calculated, and then the texture factor is combined with the quadratic error measure as the edge folding valence of the region. And finally, sequencing the edge folding values, and carrying out folding operation on the edges according to the sequence from low to high, thereby achieving the purpose of simplifying the oblique photography model of the power transmission tower.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an optimization device for the three-dimensional model of the power transmission tower, which is used for realizing the optimization method of the three-dimensional model of the power transmission tower. The implementation scheme of the solution to the problem provided by the device is similar to the implementation scheme recorded in the method, so the specific limitation in the embodiments of the optimization device for the three-dimensional model of the power transmission tower provided below can be referred to the limitation of the optimization method for the three-dimensional model of the power transmission tower, and the description is omitted herein.
In one embodiment, as shown in fig. 3, there is provided an optimizing apparatus for a three-dimensional model of a power pylon, including: an initial oblique photography model acquisition module 302, an initial oblique photography model segmentation module 304, a sharpness factor and texture factor acquisition module 306, a pylon feature area processing module 308, a pylon non-feature area processing module 310, and a pylon three-dimensional model processing module 312, wherein:
an initial oblique photography model acquisition module 302 for acquiring an initial oblique photography model;
the initial oblique photography model segmentation module 304 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 pylon feature area includes a first triangular face and the pylon non-feature area includes a second triangular face.
A sharpness factor and texture factor acquisition module 306 for acquiring a first sharpness factor and a first texture factor for the pylon feature area.
The iron tower feature region processing module 308 is configured to perform a folding operation, a filtering operation, and an updating operation on the first triangular surface based on the first sharpness factor and the first texture factor, so as to update the first number of triangular surfaces of the iron tower feature region.
The tower non-characteristic region processing module 310 is configured to obtain a second texture factor of the tower non-characteristic region, and perform a folding operation, a screening operation, and an updating operation on the second triangular surface based on the second texture factor, so as to update the number of the second triangular surfaces of the non-characteristic region.
The power pylon three-dimensional model processing module 312 is configured to obtain an optimized power pylon three-dimensional model based on the first number of triangular surfaces and the second number of triangular surfaces.
In one embodiment, pylon feature area processing module 308 further comprises:
the first error matrix and first edge folding cost acquisition module is used for acquiring a first error matrix and first edge folding cost of the iron tower characteristic region according to the first sharpness factor and the first texture factor;
the first characteristic target edge screening module is used for screening the first characteristic target edge according to the first edge folding cost and carrying out folding operation on the first characteristic target edge;
the second characteristic target edge screening module is used for updating the first error matrix, the first new vertex and the first folding price of the iron tower characteristic region edge affected by the folding operation, and screening all edges of the iron tower characteristic region according to the updated first folding cost to obtain a second characteristic target edge;
and the first triangular surface number updating module is used for carrying out folding operation on the second characteristic target edge until the first triangular surface number of the iron tower characteristic area is reduced to a first expected value.
In one embodiment, the tower non-feature area processing module 310 further includes:
The second error matrix and second side folding cost acquisition module is used for acquiring a second texture factor of the non-characteristic region of the iron tower and acquiring a second error matrix and second side folding cost according to the second texture factor;
the first non-characteristic target edge screening module is used for screening the first non-characteristic target edge according to the second edge folding price and carrying out edge folding operation on the first non-characteristic target edge;
the second non-characteristic target edge screening module is used for updating the second error matrix, the second new vertex and the second folding price of the affected non-characteristic region edge, and screening all edges of the non-characteristic region according to the updated second folding cost to obtain a second non-characteristic target edge;
and the second triangular surface number updating module is used for carrying out folding operation on the second non-characteristic target edge until the second triangular surface number of the non-characteristic area is reduced to a second expected value.
In one embodiment, the sharpness factor and texture factor acquisition module 306 further comprises:
the approximate curvature acquisition module is used for acquiring the approximate curvature of the edge where the target vertex is located;
a first sharpness factor calculation module for obtaining a first sharpness factor of the target vertex from the approximate curvature;
The texture distortion degree calculation module is used for obtaining the texture distortion degree of the target edge where the target vertex is located;
the first texture factor calculation module is used for obtaining a first texture factor of the target edge according to the texture distortion degree of the target edge where the target vertex is located.
In one embodiment, pylon feature area processing module 308 further comprises:
the reference module of the secondary error measurement algorithm is used for introducing the first sharpness factor and the first texture factor of the iron tower characteristic region into the secondary error measurement algorithm to obtain a first error matrix and a first edge folding cost of the iron tower characteristic region.
The ordering module is used for ordering 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.
All or part of each module in the optimization device of the three-dimensional model of the power transmission tower can be realized by software, hardware and a combination of the software and the hardware. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program, when executed by the processor, implements a method for optimizing a three-dimensional model of a pylon. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of 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.
CN202310813777.9A 2023-07-04 2023-07-04 Optimization method and device for three-dimensional model of transmission tower and computer equipment Pending CN116863137A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310813777.9A CN116863137A (en) 2023-07-04 2023-07-04 Optimization method and device for three-dimensional model of transmission tower and computer equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310813777.9A CN116863137A (en) 2023-07-04 2023-07-04 Optimization method and device for three-dimensional model of transmission tower and computer equipment

Publications (1)

Publication Number Publication Date
CN116863137A true CN116863137A (en) 2023-10-10

Family

ID=88229749

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310813777.9A Pending CN116863137A (en) 2023-07-04 2023-07-04 Optimization method and device for three-dimensional model of transmission tower and computer equipment

Country Status (1)

Country Link
CN (1) CN116863137A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117853659A (en) * 2024-01-11 2024-04-09 国网山东省电力公司电力科学研究院 Three-dimensional laser point cloud-based power transmission line modeling processing method and AI system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117853659A (en) * 2024-01-11 2024-04-09 国网山东省电力公司电力科学研究院 Three-dimensional laser point cloud-based power transmission line modeling processing method and AI system

Similar Documents

Publication Publication Date Title
CN113781667B (en) Three-dimensional structure simplified reconstruction method and device, computer equipment and storage medium
CN104835202A (en) Quick three-dimensional virtual scene constructing method
CN113628331B (en) Data organization and scheduling method for photogrammetry model in illusion engine
KR20120122957A (en) Navigation device, method of determining a height coordinate and method of generating a database
CN108717729A (en) A kind of online method for visualizing of landform multi-scale TIN of the Virtual earth
CN115631317B (en) Tunnel lining ortho-image generation method and device, storage medium and terminal
US20160180586A1 (en) System and method for data compression and grid regeneration
CN116071519A (en) Image processing method and device for generating grid model based on harmonic mapping
CN116863137A (en) Optimization method and device for three-dimensional model of transmission tower and computer equipment
CN115409957A (en) Map construction method based on illusion engine, electronic device and storage medium
CN115239784A (en) Point cloud generation method and device, computer equipment and storage medium
CN110533764B (en) Fractal quadtree texture organization method for building group
US20200211256A1 (en) Apparatus and method for generating 3d geographic data
CN116109799B (en) Method, device, computer equipment and storage medium for training adjustment model
Li Real-world large-scale terrain model reconstruction and real-time rendering
CN115409960A (en) Model construction method based on illusion engine, electronic device and storage medium
CN115409958A (en) Plane construction method based on illusion engine, electronic device and storage medium
CN115409962A (en) Method for constructing coordinate system in illusion engine, electronic equipment and storage medium
CN116295031B (en) Sag measurement method, sag measurement device, computer equipment and storage medium
Masood et al. A novel method for adaptive terrain rendering using memory-efficient tessellation codes for virtual globes
CN116229005B (en) Geodesic determining method and device for three-dimensional roadway model
CN116310226B (en) Three-dimensional object hierarchical model generation method, device, equipment and storage medium
CN115601512B (en) Interactive three-dimensional reconstruction method and device, computer equipment and storage medium
CN117523036B (en) Planar house type graph structured reconstruction method, device, equipment and medium
CN117689832B (en) Traffic sign generation method, device, equipment and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination