A method for constructing a three-dimensional model of urban renewal based on UAV oblique photogrammetry

Abstract

The present application relates to the technical field of oblique photogrammetry, and specifically to a method for constructing a three-dimensional model of urban renewal based on oblique photogrammetry of unmanned aerial vehicles, the method comprising: using an unmanned aerial vehicle oblique photography system to collect image data of an urban renewal area, and converting it into point cloud data, generating a triangular mesh model, and recording it as an overall triangular mesh model; extracting point cloud data belonging to a building, and generating each triangular mesh model; obtaining each closed-loop polygon in each triangular mesh model; recording the closed-loop polygon corresponding to each hole in each triangular mesh model as each hole polygon; obtaining a set of hole polygons, and determining the confidence level that each hole polygon in the set of hole polygons is a non-defective hole; obtaining each hole polygon that is a defective hole in the overall triangular mesh model, and repairing it to construct a three-dimensional digital model. The present application aims to improve the integrity of the three-dimensional digital model while ensuring the authenticity of the three-dimensional digital model.

Description

Technical Field

[0001] The present application relates to the technical field of oblique photogrammetry, and in particular to a method for constructing a three-dimensional model of urban renewal based on oblique photogrammetry by unmanned aerial vehicle (UAV) Background Art

[0002] Drone oblique photogrammetry is a high-tech surveying and mapping technology with three-dimensional modeling as its main application. It has the advantages of being fast, efficient, and accurate. It is an emerging technology and an important technical means in the field of geographic information surveying and mapping. Compared with two-dimensional urban plane data, the three-dimensional digital model of the city built based on drone oblique photogrammetry technology is realistic and intuitive. The application of three-dimensional digital models in the field of urban renewal is very extensive, including urban physical examinations and refined renovation of buildings in urban renewal areas, street space improvement, and improvement of public activity space.

[0003] Since oblique photogrammetry requires the collection of image data on a UAV flight platform, wind speed, lighting conditions, UAV flight posture and shooting blind spots may cause holes in the final constructed three-dimensional digital model during image data collection. The three-dimensional digital model constructed by oblique photogrammetry needs to be combined with a GIS platform and database for actual application. The generation of holes will reduce the authenticity and completeness of the constructed three-dimensional digital model, and it will also be impossible to associate attributes with objects in the hole area, which will affect the feasibility of refined design and the accuracy of basic planning data during urban renewal. Therefore, it is necessary to accurately identify and repair the holes generated in the three-dimensional digital model. Summary of the invention

[0004] In view of the above, it is necessary to provide a method for constructing a three-dimensional model of urban renewal based on UAV oblique photogrammetry, which improves the authenticity and completeness of the three-dimensional digital model compared to the traditional three-dimensional digital model construction method.

[0005] The urban renewal three-dimensional model construction method based on drone oblique photogrammetry in this application adopts the following technical solutions:

[0006] An embodiment of the present application provides a method for constructing a three-dimensional model of urban renewal based on drone oblique photogrammetry, the method comprising the following steps:

[0007] Collect image data of the urban renewal area, convert it into point cloud data, and generate a triangular mesh model, which is recorded as the overall triangular mesh model;

[0008] Extracting point cloud data belonging to the building from the point cloud data, and generating triangular mesh models, which are recorded as local triangular mesh models; obtaining closed-loop polygons in each triangular mesh model based on the connection relationship between the edges belonging to only one triangular mesh in each triangular mesh model;

[0009] Based on the area of ​​the closed-loop polygon, a closed-loop polygon corresponding to each hole in each triangular mesh model is obtained, which is recorded as each hole polygon;

[0010] All the hole polygons in all the local triangular mesh models are grouped into a hole polygon set, and based on the difference in area between any hole polygon in the hole polygon set and the remaining hole polygons, and the similarity between the shape of any hole polygon and a rectangle, the confidence level that any hole polygon in the hole polygon set is a non-defect hole is determined;

[0011] Based on the confidence level and the hole polygons in the overall triangular mesh model, each hole polygon that is a defect hole in the overall triangular mesh model is obtained, and each hole polygon that is a defect hole is repaired using a hole repair method to construct a three-dimensional digital model.

[0012] In one embodiment, the process of obtaining the closed-loop polygon is as follows:

[0013] Using the edges belonging to only one triangular mesh as edges in each undirected graph;

[0014] Using the vertices of the edges belonging to only one triangular mesh as nodes in each undirected graph;

[0015] Each ring in each undirected graph is used as each closed polygon in each triangular mesh model.

[0016] In one embodiment, the closed-loop polygons corresponding to the holes are all closed-loop polygons except the closed-loop polygon with the largest area in each triangular mesh model.

[0017] In one embodiment, the confidence level is determined as follows:

[0018] Determine the shape approximation of each hollow polygon based on the similarity between the shape of each hollow polygon and the rectangle;

[0019] Determine the degree of area difference of any hole polygon in the hole polygon set based on the difference in area between any hole polygon in the hole polygon set and the remaining hole polygons;

[0020] The confidence level is determined by the area difference level and shape approximation value of any hole polygon in the hole polygon set.

[0021] In one embodiment, the shape approximation is the ratio of the area of ​​the largest inscribed rectangle to the area of ​​the smallest circumscribed rectangle of each hole polygon.

[0022] In one embodiment, the area difference degree is the average value of the area difference between any hole polygon and all other hole polygons in the hole polygon set.

[0023] In one embodiment, the expression of the confidence level is:

[0024] ; In the formula, Indicates the confidence level that the kth hole polygon in the hole polygon set is a non-defect hole; Represents the shape approximation of the kth hole polygon in the set of hole polygons; Indicates the degree of area difference of the kth hole polygon in the hole polygon set; β is a preset value greater than 0.

[0025] In one embodiment, the process of determining each hole polygon that is a defect hole is as follows:

[0026] Based on all the confidence levels, obtaining each hole polygon in the hole polygon set that is a non-defect hole;

[0027] The hole polygons corresponding to the hole polygons of the non-defective holes are removed from the hole polygons of the overall triangular mesh model to obtain the hole polygons of the defective holes.

[0028] In one embodiment, the process of obtaining each hole polygon of the non-defect hole is:

[0029] Using a threshold segmentation algorithm to obtain segmentation thresholds of all the confidence levels;

[0030] The hole polygons of the non-defect holes are the hole polygons in the hole polygon set that are larger than the segmentation threshold.

[0031] In one embodiment, the boundaries corresponding to all the hole polygons of the defect holes in the overall triangular mesh model are used as all the hole boundaries in the hole repair algorithm.

[0032] This application has at least the following beneficial effects:

[0033] The present application analyzes the distinguishing features between defective cavities in the three-dimensional digital model of the urban renewal area and non-defective cavities formed by the building structure itself, comprehensively considers the shape of each cavity polygon and the area difference between each cavity polygon and all other cavity polygons, obtains the confidence level that each cavity polygon is a non-defective cavity, reflects the possibility that each cavity polygon is a non-defective cavity, and improves the accuracy of the possibility that each cavity polygon is a non-defective cavity;

[0034] Furthermore, based on the confidence level that each hole polygon is a non-defective hole, the hole polygons that are non-defective holes are obtained, and then the hole polygons that are defective holes in the three-dimensional digital model of the urban renewal area can be obtained, thereby improving the accuracy of identifying defective holes; all hole polygons that are defective holes are repaired using a hole repair algorithm to obtain a complete three-dimensional digital model. While improving the integrity of the three-dimensional digital model of the urban renewal area, it avoids repairing non-defective holes formed by the structure of the building itself in the three-dimensional digital model, thereby ensuring the authenticity of the three-dimensional digital model, and thereby improving the feasibility of refined design and the accuracy of basic planning data in the subsequent urban renewal process. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 A flowchart of the steps of the method for constructing a three-dimensional model of urban renewal based on drone oblique photogrammetry provided in this application;

[0037] Figure 2 Schematic diagram of a triangular mesh model with holes;

[0038] Figure 3 Schematic diagram of the confidence level determination process. DETAILED DESCRIPTION

[0039] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. It should be understood that, unless otherwise specified, " / " means or.

[0041] It should also be noted that the terms "first" and "second" in the present application are used to distinguish similar objects rather than to describe a specific order or sequence.

[0042] The specific scheme of the method for constructing a three-dimensional model of urban renewal based on drone oblique photogrammetry provided by the present application is described in detail below with reference to the accompanying drawings.

[0043] An embodiment of the present application provides a method for constructing a three-dimensional model of urban renewal based on drone oblique photogrammetry. Specifically, the following method for constructing a three-dimensional model of urban renewal based on drone oblique photogrammetry is provided. Figure 1 , the method comprises the following steps:

[0044] Step 1: Collect image data of the urban renewal area, convert it into point cloud data, and generate a triangular mesh model, which is recorded as the overall triangular mesh model.

[0045] The UAV oblique photography system consists of a UAV, ground control personnel and instruments. The instruments include a 5-lens oblique photography camera, a GPS positioning device and an attitude positioning system. The 5-lens oblique photography camera is used for multi-angle shooting, the GPS positioning device is used to obtain location data, and the attitude positioning system is used to record the camera's attitude data at the moment of exposure.

[0046] This application takes any urban renewal area as an example, and uses the drone oblique photography system to collect the oblique photography image data of the urban renewal area. The staff will design the drone's flight route based on the urban renewal area to ensure that the oblique photography image data of the entire urban renewal area can be obtained.

[0047] In this embodiment, the flight altitude, lateral overlap and heading overlap of the drone are 60m, 70% and 75% respectively. The values ​​of the flight altitude, lateral overlap and heading overlap are preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0048] The collected oblique photography image data of the urban renewal area are processed for uniform light and color to reduce the impact of uneven lighting, different shooting angles and time differences when the drone is shooting on the subsequent image data processing, and the oblique photography image data after uniform light and color processing are processed for multi-view image joint adjustment and multi-view image matching in turn, and the processed oblique photography image data are converted into ultra-high dense point cloud data to improve the accuracy of the three-dimensional digital model of the urban renewal area generated by subsequent real-scene modeling based on the point cloud data. The uniform light and color processing of the oblique photography image data, the multi-view image joint adjustment and the multi-view image matching are all well-known technologies, and the specific process will not be repeated here.

[0049] The ultra-high density point cloud data is used as the input of Pix4d software to generate a three-dimensional digital model of the urban renewal area, wherein the three-dimensional digital model is expressed in an irregular triangular network, and the three-dimensional digital model is a triangular mesh model composed of a series of irregular triangular meshes connected, and the triangular mesh model of the urban renewal area is recorded as the overall triangular mesh model. Among them, using Pix4d software to generate a three-dimensional digital model is a well-known technology, and this application will not repeat it.

[0050] Step 2: extract the point cloud data belonging to the building from the point cloud data, and generate various triangular mesh models, recorded as local triangular mesh models; based on the connection relationship between the edges belonging to only one triangular mesh in each triangular mesh model, obtain each closed-loop polygon in each triangular mesh model.

[0051] The holes generated in the three-dimensional digital model constructed by image data collected by a drone usually include defect holes that need to be repaired and non-defect holes belonging to the three-dimensional digital model itself that do not need to be repaired. The defect holes are usually caused by shooting blind spots caused by changes in the drone's flight attitude and the inclination angle of the tilted camera during the image data collection process, while the non-defect holes are usually formed by partial structures of the target object in the three-dimensional digital model, such as windows, balconies and other structures of the houses and buildings in the urban renewal area. Therefore, this application distinguishes between defect holes and non-defect holes in the three-dimensional digital model of the urban renewal area by analyzing ultra-high dense point cloud data and the three-dimensional digital model of the urban renewal area, and repairs the defect holes.

[0052] The ultra-high-density point cloud data contains rich ground feature information, such as point clouds of houses and buildings in urban renewal areas, as well as point clouds of non-house buildings such as the ground, vegetation, street lights and electric poles. Therefore, the point cloud data of each house and building in the ultra-high-density point cloud data is extracted using a building facade point cloud extraction method based on multi-level semantic features. Among them, the building facade point cloud extraction method based on multi-level semantic features is a well-known technology and will not be described in detail in this application.

[0053] Based on the point cloud data of each house and building, the three-dimensional digital model of each house and building is generated by using Pix4d software, and the triangular mesh model of each house and building is recorded as each local triangular mesh model.

[0054] A triangular mesh model contains triangular meshes, edges, and vertices. If there is an edge in the triangular mesh model that belongs to only one triangular mesh, this edge is called a boundary edge. If there are no holes in the three-dimensional digital model, the three-dimensional digital model is completely formed by connecting irregular triangular meshes. Except for the edges on the model boundary of the three-dimensional digital model, all other edges belong to two triangular meshes at the same time. In a three-dimensional digital model without holes, there is generally only one closed-loop polygon formed by the model boundary; if there are holes in the three-dimensional digital model, each edge on the polygonal mesh where the hole is located usually belongs to only one triangular mesh, so that there is usually a closed-loop polygon formed by the model boundary and a closed-loop polygon formed by the boundary of the hole in the three-dimensional digital model.

[0055] The schematic diagram of the triangular mesh model with holes is as follows Figure 2 As shown in the figure, a, b, c, and d are respectively the triangle mesh, edge, vertex, and boundary edge in the triangle mesh model. This triangle mesh model contains two closed-loop polygons, namely the closed-loop polygon formed by the outermost model boundary and the closed-loop polygon formed by the boundary of the internal hole.

[0056] Based on the above analysis, taking any triangular mesh model as an example, all boundary edges in the triangular mesh model are extracted. The boundary edges are taken as edges in an undirected graph, and the vertices on the boundary edges are taken as nodes in the undirected graph. All rings in the undirected graph are extracted, and each ring corresponds to a polygonal mesh in the triangular mesh model. The purpose is to obtain the closed-loop polygons formed by the model boundary and the closed-loop polygons formed by the boundary of the hole in the triangular mesh model. Among them, the extraction of rings in the undirected graph is a well-known technology and will not be repeated in this application.

[0057] Step 3: Based on the area of ​​the closed-loop polygon, the closed-loop polygons corresponding to the holes in each triangular mesh model are obtained and recorded as hole polygons.

[0058] Still taking any triangular mesh model as an example, since the closed-loop polygon formed by the model boundary of the triangular mesh model has a larger area than the closed-loop polygon formed by the boundary of the hole, the segmentation method is used to calculate the area of ​​each closed-loop polygon in the triangular mesh model, and each closed-loop polygon except the closed-loop polygon with the largest area is used as the closed-loop polygon corresponding to each hole in the triangular mesh model, and is recorded as each hole polygon. Among them, the segmentation method is a well-known technology and will not be described in detail in this application.

[0059] Step 4: All the hole polygons in all local triangular mesh models are grouped into a hole polygon set. Based on the difference in area between any hole polygon in the hole polygon set and the other hole polygons, and the similarity between the shape of any hole polygon and a rectangle, the confidence level that any hole polygon in the hole polygon set is a non-defect hole is determined.

[0060] All the hole polygons in all local triangular mesh models are grouped into a hole polygon set.

[0061] Since the structures of the buildings themselves, such as windows and balconies in the urban renewal areas, usually have regular shapes, generally rectangular shapes, the closed-loop polygons corresponding to the non-defect holes formed by the structures of the buildings themselves, such as windows and balconies, in the local triangular mesh model usually also have an approximately rectangular shape, while the defect holes in the triangular mesh model usually have more complex morphological characteristics, which makes the closed-loop polygons corresponding to the defect holes in the triangular mesh model have more complex shapes.

[0062] Based on the above analysis, the ratio of the area of ​​the maximum inscribed rectangle to the area of ​​the minimum circumscribed rectangle of each hole polygon is used as the shape approximation of each hole polygon.

[0063] It should be noted that the larger the shape approximation value is, the closer the shape of the void polygon is to a rectangle, and the more likely the void polygon is formed by the structure of the building itself.

[0064] Furthermore, although the styles of all houses and buildings in the urban renewal area are not exactly the same, the specifications of windows and balconies in the same house and building are usually the same, and within a certain range in the urban renewal area, multiple houses and buildings usually have similar structures. Therefore, the specifications of windows and balconies of some houses and buildings in the urban renewal area are consistent, so the area difference between the non-defective holes formed by these windows and balconies is small; and the way in which defective holes are generated is usually random, so the size of the hole polygon that is a defective hole is also random, so the area difference between the hole polygon that is a defective hole and the other hole polygons in the hole polygon set is large.

[0065] Based on the above analysis, the segmentation method is used to calculate the area of ​​each hole polygon in the hole polygon set, and the average of the area differences between each hole polygon in the hole polygon set and all other hole polygons is taken as the area difference degree of each hole polygon in the hole polygon set.

[0066] In this embodiment, the area difference is the absolute value of the difference between the areas. As other implementation methods, based on the measurable differences between the areas, the implementer may use other calculation methods to measure the differences between the areas, such as ratios, squares of differences, etc. This application does not impose any special restrictions.

[0067] It should be noted that: the closer the area of ​​a certain hole polygon in the hole polygon set is to that of the other hole polygons, the smaller the mean is, and the smaller the area difference is, which means that the possibility that the certain hole polygon is a non-defect hole is greater.

[0068] Furthermore, the confidence level that each hole polygon in the hole polygon set is a non-defect hole is determined by comprehensively considering the area difference degree and shape approximation of each hole polygon in the hole polygon set. The expression is:

[0069] ; In the formula, Indicates the confidence level that the kth hole polygon in the hole polygon set is a non-defect hole; Represents the shape approximation of the kth hole polygon in the set of hole polygons; It represents the area difference degree of the kth hole polygon in the hole polygon set; β is a preset value greater than 0, the purpose of which is to prevent the denominator from being 0. The value of β is preset manually and can be set by the implementer. In this embodiment, the value of β is 1; norm() represents the normalization function.

[0070] It should be noted that: by comprehensively analyzing the degree to which the shape of the k-th hole polygon is close to a rectangle, and the degree to which the area of ​​the k-th hole polygon is close to that of all other hole polygons, it is possible to judge that the k-th hole polygon is a non-defective hole formed by the building structure itself, such as a window or balcony. The larger the area, the greater the difference The smaller it is, the confidence level that the kth hole polygon in the hole polygon set is a non-defect hole The larger the confidence level, the greater the confidence level. Figure 3 shown.

[0071] Step 5, based on the confidence level and the hole polygons in the overall triangular mesh model, obtain each hole polygon that is a defect hole in the overall triangular mesh model, and use a hole repair method to repair each hole polygon that is a defect hole to construct a three-dimensional digital model.

[0072] A threshold segmentation method is used to obtain a segmentation threshold of the confidence level that all the hole polygons in the hole polygon set are non-defect holes, and each hole polygon in the hole polygon set that is greater than the segmentation threshold is regarded as a non-defect hole in the hole polygon set, that is, the hole polygons formed by the structure of the house building itself, such as windows and balconies.

[0073] Each hole polygon in the hole polygon set has one and only one corresponding hole polygon in the overall triangular mesh model.

[0074] The hole polygons corresponding to the hole polygons of the non-defective holes are removed from the hole polygons of the overall triangular mesh model to obtain the hole polygons that are defective holes in the overall triangular mesh model.

[0075] The three-dimensional digital model of the urban renewal area is used as input, and the defective holes in the three-dimensional digital model are repaired using a triangle mesh hole repair algorithm based on implicit surfaces, wherein the boundaries corresponding to all hole polygons of the defective holes in the overall triangle mesh model are used as all hole boundaries in the triangle mesh hole repair algorithm based on implicit surfaces, and the repaired three-dimensional digital model of the urban renewal area is output, thereby improving the integrity of the three-dimensional digital model of the urban renewal area. Among them, the triangle mesh hole repair algorithm based on implicit surfaces is a well-known technology and will not be described in detail in this application. Urban renewal based on the repaired three-dimensional digital model of the urban renewal area can improve the planning accuracy and rationality of resource allocation in the urban renewal process.

[0076] In summary, the present application analyzes the distinguishing features between defective cavities in the three-dimensional digital model of buildings in the urban renewal area and non-defective cavities formed by the structure of the buildings themselves, comprehensively considers the shape of each cavity polygon and the area difference between each cavity polygon and all other cavity polygons, obtains the confidence level that each cavity polygon is a non-defective cavity, reflects the possibility that each cavity polygon is a non-defective cavity, and improves the accuracy of the possibility that each cavity polygon is a non-defective cavity;

[0077] Furthermore, the hole polygons that are non-defective holes are obtained according to the confidence level that each hole polygon is a non-defective hole, and then the hole polygons that are defective holes in the three-dimensional digital model of the urban renewal area can be obtained, thereby improving the accuracy of identifying defective holes; all hole polygons that are defective holes are repaired using a hole repair algorithm to obtain a complete three-dimensional digital model. While improving the integrity of the three-dimensional digital model of the urban renewal area, it avoids repairing non-defective holes formed by the structure of the building itself in the three-dimensional digital model, thereby ensuring the authenticity of the three-dimensional digital model, and thereby improving the planning accuracy and rationality of resource allocation in the subsequent urban renewal process.

[0078] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0079] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic features of the present application. Therefore, no matter from which point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive.

Claims

1. A method for constructing a three-dimensional model of urban renewal based on UAV oblique photogrammetry, characterized in that: The method comprises the following steps: Collect image data of the urban renewal area, convert it into point cloud data, and generate a triangular mesh model, which is recorded as the overall triangular mesh model; Extracting point cloud data belonging to the building from the point cloud data, and generating triangular mesh models, which are recorded as local triangular mesh models; obtaining closed-loop polygons in each triangular mesh model based on the connection relationship between the edges belonging to only one triangular mesh in each triangular mesh model; Based on the area of ​​the closed-loop polygon, a closed-loop polygon corresponding to each hole in each triangular mesh model is obtained, which is recorded as each hole polygon; All the hole polygons in all the local triangular mesh models are grouped into a hole polygon set, and based on the difference in area between any hole polygon in the hole polygon set and the remaining hole polygons, and the similarity between the shape of any hole polygon and a rectangle, the confidence level that any hole polygon in the hole polygon set is a non-defect hole is determined; Based on the confidence level and the hole polygons in the overall triangular mesh model, each hole polygon that is a defect hole in the overall triangular mesh model is obtained, and each hole polygon that is a defect hole is repaired using a hole repair method to construct a three-dimensional digital model.

2. The method for constructing a three-dimensional model of urban renewal based on drone oblique photogrammetry according to claim 1, characterized in that: The process of obtaining the closed-loop polygon is as follows: Using the edges belonging to only one triangular mesh as edges in each undirected graph; Using the vertices of the edges belonging to only one triangular mesh as nodes in each undirected graph; Each ring in each undirected graph is used as each closed polygon in each triangular mesh model.

3. The method for constructing a three-dimensional model of urban renewal based on drone oblique photogrammetry according to claim 1, characterized in that: The closed-loop polygons corresponding to the holes are the closed-loop polygons except the closed-loop polygon with the largest area in each triangular mesh model.

4. The method for constructing a three-dimensional model of urban renewal based on drone oblique photogrammetry according to claim 1, characterized in that: The process of determining the confidence level is: Determine the shape approximation of each hollow polygon based on the similarity between the shape of each hollow polygon and the rectangle; Determine the degree of area difference of any hole polygon in the hole polygon set based on the difference in area between any hole polygon in the hole polygon set and the remaining hole polygons; The confidence level is determined by the area difference level and shape approximation value of any hole polygon in the hole polygon set.

5. The method for constructing a three-dimensional model of urban renewal based on drone oblique photogrammetry according to claim 4, characterized in that: The shape approximation is the ratio of the area of ​​the largest inscribed rectangle to the area of ​​the smallest circumscribed rectangle of each hole polygon.

6. The method for constructing a three-dimensional model of urban renewal based on drone oblique photogrammetry according to claim 4, characterized in that: The area difference degree is the average value of the area difference between any hole polygon and all other hole polygons in the hole polygon set.

7. The method for constructing a three-dimensional model of urban renewal based on drone oblique photogrammetry according to claim 4, characterized in that: The expression of the confidence level is: ; In the formula, Indicates the confidence level that the kth hole polygon in the hole polygon set is a non-defect hole; Represents the shape approximation of the kth hole polygon in the set of hole polygons; It represents the area difference of the kth hole polygon in the hole polygon set; β is a preset value greater than 0; norm() represents the normalization function.

8. The method for constructing a three-dimensional model of urban renewal based on drone oblique photogrammetry according to claim 1, characterized in that: The process of determining each void polygon that is a defect void is as follows: Based on all the confidence levels, obtaining each hole polygon in the hole polygon set that is a non-defect hole; The hole polygons corresponding to the hole polygons of the non-defective holes are removed from the hole polygons of the overall triangular mesh model to obtain the hole polygons of the defective holes.

9. The method for constructing a three-dimensional model of urban renewal based on drone oblique photogrammetry according to claim 8, characterized in that: The process of obtaining each hole polygon of the non-defect hole is as follows: Using a threshold segmentation algorithm to obtain segmentation thresholds of all the confidence levels; The hole polygons of the non-defect holes are the hole polygons in the hole polygon set that are larger than the segmentation threshold.

10. The method for constructing a three-dimensional model of urban renewal based on drone oblique photogrammetry according to claim 1, characterized in that: The boundaries corresponding to all the hole polygons of defect holes in the overall triangular mesh model are used as all the hole boundaries in the hole repair algorithm.

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