Historic building three-dimensional model reconstruction method based on global oblique photography technology and application thereof

Abstract

The invention discloses a historic building three-dimensional model reconstruction method based on a global oblique photography technology and application thereof. The method comprises the following steps: S1, delineating the envelope range of global non-contact oblique photography according to the range and shape of an ancient building body and the focal length of camera equipment; s2, acquiring an image of the outer surface of the ancient building along the determined shooting site by using unmanned aerial vehicle camera equipment; s3, performing point cloud conversion processing and point cloud fusion on the batch model images by using a digital image processing technology, and performing point cloud denoising by combining an automatic denoising technology; s4, in combination with the processed point cloud, establishing a three-dimensional model of the surface of the historic building, and realizing entity mesh generation; and S5, carrying out data format conversion on the model units subjected to grid division, importing engineering processing software, and reading model file information. According to the invention, the rapid generation method of the'image-point cloud-grid 'historic building ontology model is realized, and the purpose of rapidly and finely processing and calculating the model in batches can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of three-dimensional modeling and analysis of the surface of ancient buildings through tilt photography technology and image processing technology, and particularly relates to the global tilt photography technology of ancient buildings and its three-dimensional model reconstruction method, specifically a method for reconstructing a three-dimensional model of ancient buildings based on the global tilt photography technology. Background Art

[0002] Ancient buildings such as city walls, ancient towers, and pavilions not only have a long history and cultural value, but also possess extremely high artistic attainments and scientific research value. They play an important role in the continuation and development of traditional Chinese culture and also occupy an important position in the history of world architecture. Therefore, it is of great significance to use scientific and technological means for protection. Most ancient buildings are cultural relics, and their surveying and mapping methods require no damage to the cultural relics themselves. Therefore, it is particularly important to develop a fast, convenient, refined, non-contact method for extracting information from ancient buildings and a three-dimensional modeling method that can be used for calculation and analysis.

[0003] At present, relatively mature non-contact measurement technologies include three-dimensional laser scanning and tilt photography technology. The three-dimensional laser scanning technology has the advantages of non-contact, high efficiency, and high precision in obtaining three-dimensional point cloud data of the object surface, and has been widely used in the extraction of building information such as digital cultural heritage protection and restoration, and smart cities. However, it requires equipment calibration and a long time to capture point clouds locally, and may also face the difficulty of redundant data caused by multiple point clouds. The unmanned aerial vehicle (UAV) tilt photography measurement technology has been widely used and developed in the fields of geological engineering, hydraulic engineering, and municipal engineering. Different from the three-dimensional laser scanning technology, this technology can quickly capture blind spots of the building body and perform omnidirectional three-dimensional modeling. Ancient buildings have exquisite and unique shapes. In order to obtain their refined surface models, tilt photography technology can be considered, combined with devices such as UAVs and mobile phones to achieve lightweight and portable image information collection. However, the application of this technology in the field of ancient buildings is not yet mature, and there are still technical defects that need to be improved.

[0004] With the development of computer hardware devices and numerical simulation technology, more and more simulation studies on the structural stability of ancient buildings have been carried out. The generation of model establishment, refined characterization, and the quality of its mesh division have an important impact on the calculation results. Quickly obtaining model information from the original building and converting it into readable data information requires engineers to quickly and accurately complete the information extraction work. At the same time, a method for quickly establishing a three-dimensional complex geometric model with high reduction degree, refined characterization, and ability to reflect the three-dimensional complex geometry is particularly important for numerical simulation and result analysis work. The displacement nephogram, stress distribution nephogram, plastic zone expansion nephogram, etc. obtained by simulation solving provide specific references and guidance for scholars to describe and analyze the apparent phenomena and essential mechanisms.

[0005] The global oblique photography technology for ancient buildings and its three-dimensional model reconstruction method have two major difficult problems: on the one hand, there is no full-process method for image acquisition-point cloud extraction-mesh generation-simulation analysis for the building body. The smooth connection of each step of the process, as well as whether it can achieve the rapid generation of "image-point cloud-mesh" and realize efficient batch processing, still requires overall planning and research on details; on the other hand, the oblique photography image acquisition tools are still mostly limited to a single device of an unmanned aerial vehicle. In some cases, the acquisition work is restricted by flight restrictions, and the acquisition of corners cannot be completed, and the refinement degree of the model cannot be guaranteed. Summary of the Invention

[0006] The present invention aims to solve the problem that there is no rapid generation technology of "image-point cloud-mesh" in the prior art means in the generation of the preprocessing model for the simulation analysis of ancient buildings, and complete the full-process operation method of image acquisition-point cloud extraction-mesh generation-simulation analysis, and proposes a global oblique photography technology for ancient buildings and its three-dimensional model reconstruction method. The purpose of the present invention is achieved through the following technical solutions:

[0007] A three-dimensional model reconstruction method for ancient buildings based on the global oblique photography technology, comprising the following steps:

[0008] S1, according to the scope, shape and focal length of the camera equipment of the ancient building body, delineate the envelope range of the global non-contact oblique photography; then determine the shooting positions in combination with the shooting wide angle;

[0009] S2, use the unmanned aerial vehicle camera equipment to collect the external surface images of the ancient building along the determined shooting positions, and add supplementary shooting images at local corner parts;

[0010] S3, use digital image processing technology to perform point cloud conversion processing and point cloud fusion on a batch of model images, and perform point cloud denoising in combination with automatic denoising technology; perform point cloud fusion based on the feature point registration algorithm;

[0011] S4, combine the processed point cloud to establish a three-dimensional model of the ancient building surface, and realize entity mesh dissection. By controlling the mesh accuracy, mesh division of different unit sizes is realized, and a numerical calculation model of the corresponding refined mesh is generated;

[0012] S5, convert the data format of the model units after mesh division, read the model file information after importing it into the engineering processing software, and perform calculation simulation analysis and result visualization display.

[0013] Further, the step S1 includes the following specific operations:

[0014] Step S11: Determine the starting points of its length, width, and height according to the shape and size of the ancient building body to obtain the basic range of the body. Then, combined with the focal length of the camera device, add one times the focal length to the basic range to obtain the outer envelope range.

[0015] Step S12: The shooting wide-angle range of the camera device should overlap by 1 / 3 to 1 / 2 at adjacent shooting points. Delimit each shooting area along the obtained outer envelope range to determine the specific shooting points.

[0016] Further optimization, step S2 specifically includes the following operations:

[0017] Step S21: According to the shooting points determined in S12, use the UAV camera device to perform sequential shooting, and the acquisition sequence is carried out respectively in the x, y, and z directions; during the process of collecting the images of the outer surface of the building body, keep the focal length and aperture stable, and keep the exposure rate and resolution unchanged.

[0018] Step S22: After completing step S21, depending on the specific situation, use the camera to perform supplementary shooting at the corners and local refined parts of the building, and keep the exposure rate and resolution of the camera device consistent with those in step S21 during the supplementary shooting.

[0019] Furthermore, step S3 includes the following steps:

[0020] Step S31: Use existing commercial software to batch import the captured images and perform automatic point cloud conversion operations; the commercial software includes: Metashape.

[0021] Step S32: Use point cloud fusion technology to splice multiple groups of point cloud data; perform parametric denoising using existing software, or perform denoising optimization processing in combination with self-written programs; the existing software includes 3DResharper or GeomagicStudio.

[0022] Furthermore, the point cloud fusion based on the feature point registration algorithm in step S3 includes the following operations:

[0023] Perform data splicing and fusion on two groups of point cloud data: Manually select feature points, take one group as the target feature points and the other group as the to-be-transformed feature points, and calculate their rotation matrix and translation matrix; assume that the corresponding coordinates of point cloud data 1 are P1(x1, y1, z1), and the corresponding coordinates of point cloud data 2 are P2(x2, y2, z2).

[0024] P1 = M R × P2 + M T

[0025] Among them, M R and M TThey are the rotation matrix and the translation matrix respectively;

[0026] Furthermore, the following can be obtained:

[0027]

[0028] where α, β, and γ are the rotation angles in three directions between point P2 and point P1, and t x , t y , t z correspond to the translation distances in three directions between the two coordinate points respectively; according to the selected three groups of feature points, six parameter values [α, β, γ, t x , t y , t z for data stitching and fusion are obtained by the least squares method. Substituting them into the above formula, the rotation matrix M R and the translation matrix M T ;

[0029] By traversing all the point cloud data 2, for all (x 2,i , y 2,i , z 2,i ), rotation and translation operations are performed to obtain a new data set (x 2,i ’ , y 2,i ’ , z 2,i ’ ) that matches the point cloud data 1 set, that is, the fused point cloud data set is obtained and exported for subsequent point cloud denoising processing.

[0030] Furthermore, step S4 of the method further includes the following steps:

[0031] Step S41: From the point cloud denoised in step S32, it is exported and saved as a data file containing (x, y, z) information. The Delaunay triangulation algorithm is used to convert the point cloud data into a mesh model in mesh format, and the point cloud data is imported to implement the surface mesh generation operation, where the surface mesh is divided by triangular faces;

[0032] Step S42: Through the mesh quality controller, its mesh quality is optimized and adjusted; the mesh size can be changed according to requirements to achieve its refined or rough characterization.

[0033] Application of the three-dimensional model reconstruction method for ancient buildings based on the global oblique photography technology

[0034] The generation model generated by the present invention can be used in subsequent continuous finite element and discrete body finite element analyses of ancient buildings considering solid or fluid-solid coupling.

[0035] Advantages of the present invention:

[0036] 1) A method for reconstructing a three-dimensional model of an ancient building based on the global oblique photography technology according to the present invention, based on the non-contact image acquisition technology, uses portable and lightweight devices such as mobile phones to realize a fast generation method for the ontology model of the ancient building of "image - point cloud - mesh", which can be applied to the finite element simulation analysis under various different working conditions, and can achieve the purpose of quickly, precisely and batch-processing calculation models, greatly improving the pre-processing modeling efficiency of complex models.

[0037] 2) The fast generation technology of "image - point cloud - mesh" adopted by the present invention greatly increases the pre-processing efficiency of numerical simulation.

[0038] 3) The multiple multi-dimensional acquisition method and its point cloud stitching and fusion technology in the present invention greatly improve the refinement degree of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a schematic flow chart of the global oblique photography technology of the ancient building and its three-dimensional model reconstruction method according to the embodiment of the present invention;

[0040] Figure 2 is a schematic diagram of the global contour three-dimensional oblique photography of the ancient building according to the embodiment of the present invention;

[0041] Figure 3 is a schematic diagram of the global three-dimensional oblique photography points and their acquisition sequence according to the embodiment of the present invention;

[0042] Figure 4 is a schematic diagram of the "image - point cloud" conversion and point cloud data fusion according to the embodiment of the present invention;

[0043] Figure 5 is a schematic diagram showing different mesh sizes of the model division according to the embodiment of the present invention;

[0044] Figure 6 is a schematic diagram of the output model file and the calculation simulation result according to the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present invention, and should not be construed as limiting the present invention.

[0046] Embodiment 1

[0047] Figure 1It is a schematic diagram of the entire operation process of a three-dimensional model reconstruction method for ancient buildings based on global oblique photography technology in an embodiment of the present invention. First, determine the scope of the building body and delineate the image acquisition envelope range. Then, use a non-contact lightweight camera to take pictures along the acquisition points, and use a mobile phone to supplement the shooting of local corner points. Further, batch convert the collected images into point clouds and perform denoising optimization. Finally, generate a surface mesh from the point clouds, and take the Abaqus modeling rules as an example to divide the solid elements and save them in the data format of an inp calculation file, realizing the pre-processing modeling process of the entire process of "image acquisition - point cloud processing - mesh generation". The specific steps are as follows:

[0048] S1. According to the scope, shape and camera equipment focal length of the ancient building body, delineate the envelope range of global non-contact oblique photography; then determine the shooting positions in combination with the shooting wide angle.

[0049] S2. Use the UAV camera equipment 7 to collect the images of the outer surface of the ancient building along the determined shooting positions, and add supplementary shooting images at local corner parts.

[0050] S3. Use digital image processing technology to perform point cloud conversion processing and point cloud fusion on batch model images, and perform point cloud denoising in combination with automatic denoising technology; perform point cloud fusion based on the feature point registration algorithm.

[0051] S4. Combine the processed point clouds to establish a three-dimensional model of the ancient building surface and realize solid mesh dissection. Different unit size mesh divisions can be achieved by controlling the mesh accuracy.

[0052] S5. Convert the data format of the model units after mesh division, read the model file information after importing it into the engineering processing software, and perform subsequent calculation simulation analysis and result visualization display.

[0053] Among them, the detailed operation process of step S1 is as follows:

[0054] Step S11. According to the size of the ancient building body, determine the starting points of its length, width and height to obtain the basic range of the body. Then, in combination with the focal length of the camera equipment, add and subtract 1 times the focal length on the basis of the basic range to obtain the outer envelope range.

[0055] Specifically, as Figure 2 shown, combine the ancient building body 1 to determine the boundary line of its outer contour, and thus delineate its outer contour 2 of the body; the outer envelope range 3 is expanded a certain distance outward from the outer contour 2 of the body, and this distance is the focal length value of the UAV camera equipment 7.

[0056] Step S12. The shooting wide angle range of the camera equipment should overlap by 1 / 3 to 1 / 2 at adjacent shooting points. Delineate each shooting area along the obtained outer envelope range to determine the specific shooting positions.

[0057] Specifically, the shooting points 5 are distributed on the outer envelope range 3, and their setting rule is to be evenly arranged on the outer envelope range 3 according to the shooting wide-angle range 4 of the UAV camera device 7. It should be noted that the shooting coverage areas (shooting wide-angle ranges) between adjacent shooting points should overlap at least 1 / 3 of the area to ensure the continuity of the collected images.

[0058] Among them, the detailed operation process of step S2 is as follows:

[0059] Step S21, according to the shooting points determined in S12, use the UAV camera device 7 to perform sequential shooting, and the acquisition sequence is carried out respectively according to the three directions of x, y, and z; during the process of collecting images of the outer surface of the building body, the focal length and aperture should be kept stable, and the exposure rate, resolution, etc. should remain unchanged.

[0060] Specifically, according to the shooting points determined above, use the UAV camera device 7 to perform fixed-point sequential acquisition, and the shooting sequence is from left to right in the x direction, from front to back in the y direction, and from bottom to top in the z direction; it should be noted that during the shooting process, the shooting angle of the UAV device should always be perpendicular to the vertical surface of the outer envelope range, and fixed-focus shooting should be carried out while keeping the aperture, exposure rate, and resolution consistent.

[0061] Step S22, after completing step S21, depending on the specific situation, use a mobile phone camera to perform supplementary shooting at the corners and local refined parts of the building. The supplementary shooting should still keep the exposure rate and resolution of the camera device as consistent as possible with the above.

[0062] Specifically, according to the quality of the collected images and the situation of their local hidden positions, determine the local shooting points 6, and use a portable device (mobile phone shooting device 8) to perform supplementary image acquisition. During the shooting process, the device should try to ensure that the exposure rate and resolution are consistent with those of the UAV device; thus, multiple sets of image sets can be obtained for subsequent image analysis.

[0063] Among them, the detailed operation process of step S3 is as follows:

[0064] Step S31, use existing commercial software such as Metashape to batch import the shooting images and perform point cloud automatic conversion operations on them.

[0065] Specifically, taking Metashape as an example, Chunk1 and Chunk2 are established respectively, corresponding to the image sets collected by the drone device and the mobile device. Import the image sets and perform the processes of image arrangement and automatic calibration, set its accuracy and the number of point clouds that can be generated, and thus obtain point cloud data 1; process the image set collected by the mobile device according to the above steps to obtain point cloud data 2; finally, export multiple groups of point cloud data respectively for subsequent stitching and point cloud denoising.

[0066] In step S32, use point cloud fusion technology to stitch multiple groups of point cloud data; point cloud denoising mainly relies on existing software for parametric denoising, or combines self-written programs for denoising optimization processing.

[0067] Specifically, point cloud fusion technology is based on the feature point registration algorithm to perform data stitching and fusion on two groups of point cloud data. First, manually select feature points, and then take one group as the target feature points and the other group as the feature points to be transformed, and calculate its rotation matrix and translation matrix. Suppose the corresponding coordinates of point cloud data 1 are P1(x1, y1, z1), and the corresponding coordinates of point cloud data 2 are P2(x2, y2, z2), and thus the formula can be obtained:

[0068] P1 = M R ×P2 + M T

[0069] where M R and M T are the rotation matrix and translation matrix respectively, and then it can be obtained:

[0070]

[0071] where α, β, γ are the rotation angles in three directions between point P2 and point P1, and t x , t y , t z correspond to the translation distances in three directions between the two coordinate points respectively. Thus, according to the selected three groups of feature points, the six parameter values [α, β, γ, t x , t y , t z of data stitching and fusion can be solved by the least squares method, and substituting them into the above formula to obtain the rotation matrix M R and the translation matrix M T .

[0072] Furthermore, by traversing all point cloud data 2, for all (x 2,i , y 2,i , z 2,i ) perform rotation and translation operations to obtain a new data set (x 2,i’ , y 2,i ’ , z 2,i ’ ); Finally, the fused point cloud dataset is obtained and exported for subsequent point cloud denoising processing.

[0073]

[0074] Furthermore, the point cloud denoising technology can be combined with 3DResharper or Geomagic Studio to denoise the fused point cloud data. Generally, the surrounding environmental point cloud should be manually deleted (cropped) first, and then its point cloud is smoothed through the noise reduction function, and the smoothness and interpolation coefficient are set to control the denoising process. Another way is to write a self-program, and the points with fewer neighbor points than the set threshold within a certain range are excluded as isolated points or invalid points.

[0075] Among them, the detailed operation process of step S4 is as follows:

[0076] Step S41: The point cloud denoised by step S32 is exported and saved as a data file containing (x, y, z) information, and the modeling algorithm for generating the ancient building surface mesh from the point cloud is correspondingly imported to realize the surface mesh generation operation, where the surface mesh is divided by triangular faces. The core of the modeling algorithm for generating the ancient building surface mesh from the point cloud is to use the Delaunay triangulation algorithm to convert the point cloud data into a mesh model in mesh format.

[0077] Specifically, after denoising the point cloud data, its coordinate information is exported and saved as a.xyz file, and a self-program is called to realize the point cloud data reading and point cloud modeling process. The core of its algorithm lies in using the Delaunay triangulation algorithm in the CGAL library, which can convert the point cloud data into a mesh model in mesh format. This algorithm can ensure that the generated mesh model has a good topological relationship, and for ancient buildings with possible local refined geometric configurations, the authenticity of the external contour representation of the ancient building can be realized. The specific operation process includes, after reading the point cloud data, using the creatSurface() algorithm to create a surface according to the point cloud, and then combining meshing.generate(2) to mesh the surface to generate three-dimensional surface elements, and its division method is composed of triangular faces.

[0078] Step S42. Due to differences in image acquisition operations, the point cloud data may be dense in some positions and sparse in others. Therefore, the mesh quality controller is used to optimize and adjust its mesh quality. Additionally, the mesh size can be changed according to requirements to achieve its fine or rough representation. Moreover, the mesh generation method in the CGAL library supports inserting feature cracks or damaged three-dimensional surfaces and preserves the geometric topological node relationships of its feature surfaces, which can well realize the representation of existing damages inside ancient buildings.

[0079] Specifically, due to the operational differences in the acquisition step S2 and the point cloud processing step S3 in image processing, the point cloud data in some positions of the model may be less or more, which may lead to sparse or dense surface meshes generated. Therefore, by adjusting the mesh quality parameters, the Size command can uniformly set the element size, and the Mesh.Adapt command can start the adaptive mesh adjustment, thereby realizing the rough or fine representation of the model.

[0080] Among them, the detailed operation process of step S5 is as follows:

[0081] Specifically, in this embodiment, taking the Abaqus computational simulation software as an example, the mesh information after the model division in step S4 is subjected to format conversion corresponding to the.inp file format, and the data information is written out separately according to several modules of nodes Node, elements Element, assembly Assembly, and materials Material, and output to the model file Model.inp. Then, the Abaqus software imports and reads the model file information for subsequent computational simulation analysis and result visualization display.

[0082] Furthermore, it should be particularly noted that in this embodiment, discretization is achieved based on the finite element continuum model after mesh division. The method to realize this required function is to use the intersection algorithm to traverse whether all the generated tetrahedral meshes are in the large block to be discretized. If they are inside, they are retained, and the rest are deleted. Finally, all the remaining tetrahedrons are the entity meshes used to represent the discretized block, forming Part1; and so on, forming Part2, Part3... as needed. The contact type and contact parameters are set in the Abaqus computational simulation software to realize the contact action and friction slip behavior between blocks, etc., which conform to the discrete body characteristics shown by the mechanical property degradation or even disappearance of the connection interface of ancient buildings.

[0083] The global oblique photography technology and three-dimensional model reconstruction method for ancient buildings according to the embodiments of the present invention can quickly and conveniently obtain the image information on the surface of the target model. The feature point matching method can be used to realize the matching combination of multiple groups of point cloud data sets. The point cloud denoising and the process of converting the point cloud into surface units can be realized, and the entity grid units of different unit scales can be divided accordingly. Thus, the model file can be calculated and analyzed by finite element software, greatly improving the modeling speed. The full-process rapid processing technology of "image-point cloud-model" reduces the restrictions and usage thresholds between multiple software, and greatly improves the adaptability.

[0084] All kinds of functions adopted in this embodiment are only detailed descriptions of the specific implementation manners of the present invention. Other functions can be used to implement related functions; and the subsequent model analysis software is not limited to Abaqus software. Only its file type is described in detail. Other software such as Ansys can be used and corresponding to its model file format.

[0085] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0086] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention, and all the changes, modifications, substitutions, and variations made should be included within the protection scope of the present invention.

Claims

1. A method for reconstructing a three-dimensional model of an ancient building based on global oblique photography technology, characterized in that, Including: S1. According to the scope, shape and focal length of the camera equipment of the ancient building body, delimit the envelope range of the global non-contact oblique photography; then determine the shooting positions in combination with the shooting wide angle. S2. Use the UAV camera equipment to collect the external surface images of the ancient building along the determined shooting positions, and add supplementary shooting images at the local corner parts. S3. Use digital image processing technology to perform point cloud conversion processing and point cloud fusion on the batch model images, and perform point cloud denoising in combination with the automatic denoising technology; perform point cloud fusion based on the feature point registration algorithm. S4. Combine the processed point cloud to establish a three-dimensional model of the ancient building surface, and realize the entity mesh dissection. By controlling the mesh accuracy, the mesh division of different unit sizes is realized, and the numerical calculation model of the corresponding refined mesh is generated. S5. Convert the data format of the model units after mesh division, read the model file information after importing it into the engineering processing software, and perform calculation simulation analysis and result visualization display.

2. The method for reconstructing a three-dimensional model of an ancient building based on the global tilt photography technology according to claim 1, wherein The step S1 includes the following specific operations: Step S11. According to the shape and size of the ancient building body, determine the starting points of its length, width and height, obtain the basic range of the body, and then combine the focal length of the camera equipment. Add 1 times the focal length to the basic range to obtain the outer envelope range. Step S12. The shooting wide angle range of the camera equipment should overlap 1 / 3 - 1 / 2 at adjacent shooting points. Delimit each shooting area along the obtained outer envelope range to determine the specific shooting positions.

3. The three-dimensional model reconstruction method of ancient buildings based on the global tilt photography technology according to claim 1, characterized in that The step S2 specifically includes the following operations: Step S21. According to the shooting positions determined in S12, use the UAV camera equipment to perform sequential shooting, and the acquisition sequence is carried out in the x, y, and z directions respectively; during the process of collecting the images of the external surface of the building body, keep the focal length and aperture stable, and the exposure rate and resolution remain unchanged. Step S22. After completing step S21, according to the specific situation, use the camera to perform supplementary shooting at the corners and local refined parts of the building, and keep the exposure rate and resolution of the camera equipment consistent with those in step S21 during the supplementary shooting.

4. The method for reconstructing a three-dimensional model of an ancient building based on the global oblique photography technology according to claim 1, wherein The step S3 includes the following steps: Step S31. Use existing commercial software to batch import the shooting images and perform automatic point cloud conversion operations on them; the commercial software includes: Metashape. Step S32. Use point cloud fusion technology to splice multiple groups of point cloud data; use existing software for parametric denoising, or perform denoising optimization processing in combination with self-written programs; the existing software includes 3DResharper or GeomagicStudio.

5. The method for reconstructing a three-dimensional model of an ancient building based on the global oblique photography technology according to claim 1, wherein: The point cloud fusion based on the feature point registration algorithm in step S3 includes the following operations: Perform data splicing and fusion on two groups of point cloud data: manually select feature points, take one group as the target feature points and the other group as the to-be-transformed feature points, and calculate their rotation matrix and translation matrix; assume that the corresponding coordinates of point cloud data 1 are P1(x1, y1, z1), and the corresponding coordinates of point cloud data 2 are P2(x2, y2, z2). P1 = M R × P2 + M T where M R and M T are the rotation matrix and the translation matrix, respectively; Furthermore, it can be obtained: where α, β, and γ are the rotation angles in three directions between point P2 and point P1, and t x , t y , t z correspond to the translation distances in three directions between the two coordinate points respectively; according to the selected three groups of feature points, six parameter values [α, β, γ, t x , t y , t z for data stitching and fusion are obtained by the least squares method. Substituting these values into the above formula gives the rotation matrix M R and the translation matrix M T ; By traversing all the point cloud data 2, for all (x 2,i , y 2,i , z 2,i ), perform rotation and translation operations to obtain a new data set (x 2,i ’ , y 2,i ’ , z 2,i ’ ) that matches the set of point cloud data 1, that is, obtain the fused point cloud data set, and export it for subsequent point cloud denoising processing.

6. The method for reconstructing a three-dimensional model of an ancient building based on the global tilt photography technology according to claim 1, wherein The step S4 further includes the following steps: Step S41: The point cloud denoised in step S32 is exported and saved as a data file containing (x, y, z) information. The Delaunay triangulation algorithm is used to convert the point cloud data into a mesh format grid model, and the point cloud data is imported to implement the surface mesh generation operation, where the surface mesh is divided by triangular faces. Step S42: The mesh quality is optimized and adjusted through a mesh quality controller; the mesh size can be changed according to requirements to achieve its refined or rough characterization.

7. Application of the method for reconstructing the three-dimensional model of ancient buildings based on the global oblique photography technology according to any one of claims 1 to 6, characterized in that The model is used in subsequent continuous finite element and discrete body finite element analyses of ancient buildings that can consider solid or fluid-structure interaction analyses.

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