Modeling method and system for historic building restoration

Abstract

The invention provides a modeling method and system for historic building restoration, and belongs to the field of building restoration modeling, and the method comprises the steps: S1, collecting three-dimensional point cloud data of a to-be-restored historic building, carrying out the preprocessing, and recording a damage state and deformation characteristics; s2, carrying out matching and attitude correction on the standardized data and a parameterized component library of the historic building; s3, dividing the initial repair model into a plurality of sub-regions, calculating a morphological difference metric value of each sub-region, and dividing the initial repair model; s4, performing registration on the completely reconstructed region and the partially repaired region by adopting an ancient building parameterized component library to obtain a conventional part model, and performing matching on the original reserved region by adopting a fitting algorithm to obtain a detail part model; and S5, fusing the conventional part model and the detail part model to obtain a three-dimensional model of the historic building. Accurate digital modeling of the ancient building is achieved, the overall structure standardization of the ancient building is guaranteed, and the characteristics and details of the original building can be reserved.

Description

Technical Field

[0001] The present invention relates to the technical field of building restoration modeling, and in particular to a modeling method and system for ancient building restoration. Background Art

[0002] Ancient architecture is a vital material vehicle for the development of human civilization. Its architectural systems are not only abundant in number but also boast unique intangible cultural heritage, including distinctive timber frame structures and mortise and tenon joint techniques. Traditional restoration of ancient buildings has long relied on experienced craftsmen, who determine restoration plans through visual inspection, manual surveying, and empirical judgment. While this approach has ensured smooth restoration work to a certain extent, it also faces numerous challenges. These include the limited accuracy of traditional surveying methods, which make it difficult to accurately capture subtle deformations and damage to ancient buildings; a lack of scientific basis for restoration plans; secondary damage to original components during the restoration process; and differences between restored buildings and their original appearance.

[0003] In recent years, the application of modeling technology in the restoration of ancient buildings has become a significant development trend. By creating digital models of ancient buildings, comprehensive analysis, research, and planning can be conducted in a virtual environment, providing a scientific basis and technical support for restoration work. However, existing modeling techniques struggle to balance overall structure with local details. They either oversimplify the overall structure and ignore local details, or overemphasize local precision, resulting in overly large and complex models. Furthermore, existing technologies struggle to effectively address the irregular deformations and defects common in ancient buildings. Consequently, the modeling process either involves forcibly mapping the actual structure onto an idealized model, resulting in distortion, or abandoning parametric structures, making the model difficult to modify and analyze.

[0004] Therefore, finding a method that can balance the overall structure and detailed expression of ancient buildings and improve data processing efficiency is a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present invention provides a modeling method and system for ancient building restoration, which is used to solve the defects of the existing technology in ancient building restoration, such as poor restoration effect and easy neglect of details, and realize accurate digital modeling of ancient buildings. While ensuring the overall structural standardization of ancient buildings, the characteristics and details of the original buildings can be preserved.

[0006] The present invention provides a modeling method for ancient building restoration, comprising the following steps: S1. Collect 3D point cloud data of the ancient building to be restored, pre-process the 3D point cloud data to obtain standardized data, and record the damage status and deformation characteristics of the ancient building to be restored; S2. Acquire historical data of ancient buildings to construct a parametric component library of ancient buildings, match the standardized data with the parametric component library of ancient buildings, and perform posture correction to obtain an initial restoration model of the ideal state of the ancient buildings before they were damaged; S3. Divide the initial restoration model into several sub-areas according to the restoration requirements, calculate the morphological difference metric value of each sub-area according to the damage state and deformation characteristics of the ancient building to be restored, and divide the initial restoration model into a complete reconstruction area, a partial restoration area, and an original state preservation area based on the morphological difference metric value; S4. The fully reconstructed area and the partially restored area are registered using the ancient building parametric component library to obtain a conventional partial model, and the original preserved area is matched using a fitting algorithm to obtain a detailed partial model; S5. The conventional part model and the detailed part model are integrated in a unified coordinate system to obtain a three-dimensional model of the ancient building, and a digital repair file is established based on the three-dimensional model of the ancient building and the damage state and deformation characteristics of the ancient building to be repaired.

[0007] According to a modeling method for ancient building restoration provided by the present invention, the ancient building parametric component library includes a basic construction model set, a mortise and tenon connection relationship library, a historical style database, a material and process information library and a dynamic update mechanism.

[0008] According to a modeling method for ancient building restoration provided by the present invention, the matching of standardized data with the ancient building parameterized component library and posture correction specifically include: Extracting key geometric features of the standardized data, aligning the key geometric features of the standardized data with geometric reference points of the ancient architectural parametric component library, and generating a preliminary matching result; wherein the key geometric feature points include structural feature points, curvature feature points, edge feature points, and plane feature points; Based on the preliminary matching results, the relative relationship constraints between components are established in combination with the constraints of ancient architectural structural principles, including flatness, verticality and symmetry. Based on the relative relationship constraints between components and the preliminary matching results, the iterative closest point algorithm is used to process the standardized data.

[0009] According to a modeling method for ancient building restoration provided by the present invention, the iterative closest point algorithm specifically includes: Assigning weights to feature points of different categories of key geometric features, and constructing a registration objective function fused with structural constraints based on the weights of the feature points of different categories. The registration objective function fused with structural constraints includes a point-to-point distance term, a constraint based on the structural principles of ancient architecture, and a regularization constraint. The point-to-point distance term is the sum of the distances between points in the standardized data and the parametric construction models in the parametric component library of ancient architecture. Roughly align the preliminary matching results with the main components of the ancient building parametric component library to determine the basic framework of standardized data; Based on the relative relationship constraints between components, medium-precision alignment and fine alignment are performed with the component categories and component structures of the ancient building parametric component library respectively.

[0010] According to a modeling method for ancient building restoration provided by the present invention, the calculation of the morphological difference metric value of each sub-region based on the damage state and deformation characteristics of the ancient building to be restored specifically includes: The damage state and deformation characteristics of the ancient buildings to be restored: establish a characteristic description model and assessment benchmark for different types of damage; the damage state includes surface weathering, component fracture and structural deformation, and the deformation characteristics include component bending, warping and historical repair traces; Adaptively downsample the normalized data of each sub-region to generate a low-resolution point cloud representation; Determine a neighborhood point set for each point in each sub-area, where the neighborhood size is dynamically adjusted according to the component type and local normalized data density; Calculate the position difference vector between each point in the subregion and all points in the neighborhood point set, and calculate the mean of all position difference vectors in the neighborhood; The morphological difference metric value of each point in the sub-region is calculated based on the position difference vector and the mean of the position difference vector. The calculation formula is: ; in, Indicates a point The morphological difference metric at , represents any point in the normalized data, Indicates a point The neighborhood point set of represents the L2 norm, Represents neighborhood Any point in represents the mean of the vectors between points in the neighborhood, Indicates a point The number of points in the neighborhood of represents the i-th point in the subregion, Indicates the points in the subregion except i According to a modeling method for ancient building restoration provided by the present invention, the sub-area is divided into a completely reconstructed area, a partially restored area, and an original state preservation area based on the morphological difference metric value, specifically comprising: Calculate the global distribution of the morphological difference metrics of all sub-regions and determine the division thresholds T1 and T2, where T1 is the standard for light damage to ancient buildings and T2 is the standard for severe damage to ancient buildings; The sub-areas with the average value of the morphological difference metric lower than T1 are divided into the original state preservation area, and only protection and reinforcement treatment is performed; The sub-areas with the average value of the morphological difference metric higher than T2 are divided into completely reconstructed areas, which need to be reconstructed in accordance with the traditional ancient architectural style and local architectural style; The sub-regions whose average morphological difference metric values ​​are between T1 and T2 are subjected to a spatial distribution characteristic analysis of the morphological difference metric values, wherein the spatial distribution characteristic analysis includes calculating the local clustering coefficient, geometric characteristic vector and position change rate of the sub-region morphological difference metric values.

[0011] According to a modeling method for ancient building restoration provided by the present invention, the conventional partial model is obtained by registering the fully reconstructed area and the partially restored area using the ancient building parametric component library, specifically comprising: Extract the principal axis directions and key geometric parameters of the standardized data of the fully reconstructed area and the partially restored area, match them to the corresponding models in the parametric component library of ancient buildings, and obtain the initial conventional model; Adjust the core parameters of the initial conventional model, and optimize the position and posture of the components in the initial conventional model by combining the spatial relationship and connection constraints between the components of the ancient building; the core parameters include the basic form, size and structural characteristics of the components; The registration results are iterated step by step until the position and posture of the components in the initial conventional model meet the preset standards, and the conventional partial model is obtained.

[0012] According to a modeling method for ancient building restoration provided by the present invention, the method of using a fitting algorithm to match the original preserved area to obtain a detailed model specifically includes: Extracting a basic template model of the same type as the components in the original preservation area from the parametric component library of the ancient buildings, and performing Gaussian regression on the standardized data of the original preservation area to generate surface weathering characteristics of components of different materials; Extracting the skeleton of the basic template model, identifying the mortise and tenon joints, the center lines of the load-bearing components, and the key points of mechanical transmission of the components, and establishing a deformation influence weight system from the structural skeleton to the component surface; Based on the deformation influence weight system and the surface weathering characteristics of components made of different materials, the optimal repair parameters of the traditional component skeleton are calculated. During the calculation of the repair parameters, the structural rules of the ancient building are applied to constrain the repair parameters to obtain a repair model. The repair parameters include the curvature of the beam frame, the inclination angle of the column body, and the stacking deformation of the brackets. Based on the deformed component skeleton and deformation influence weight system, control elements of key nodes are set, and radial basis functions that conform to the deformation characteristics of traditional materials are used for surface repair interpolation. The historical and cultural information of the ancient buildings is extracted from the standardized data of the original preserved area and mapped to the surface of the repair model through normal displacement and texture mapping.

[0013] According to a modeling method for ancient building restoration provided by the present invention, a conventional part model and a detail part model are integrated in a unified coordinate system, specifically comprising: Analyze the boundary characteristics of the general part model and the detailed part model, establish the spatial correspondence between different model areas, and set a transition zone in the boundary area between the general part model and the detailed part model; A distance-weighted fusion algorithm is used to perform smooth transition processing in the transition zone. By calculating the spatial distance between each point in the transition zone and the conventional part model and the detailed part model, a weight function that changes smoothly with the distance is generated. Based on the weight function, the geometric features and surface properties of the conventional part model and the detailed part model are weightedly fused.

[0014] The present invention also provides a modeling system for ancient building restoration, which implements the above-mentioned modeling method, including: The data acquisition and processing module is used to collect the 3D point cloud data of the ancient building to be restored, pre-process the 3D point cloud data to obtain standardized data, and record the damage status and deformation characteristics of the ancient building to be restored; The initial model construction module is used to obtain historical data of ancient buildings to build a parametric component library of ancient buildings, match the standardized data with the parametric component library of ancient buildings, and perform posture correction to obtain an initial restoration model of the ideal state of the ancient building before damage; A region division module is used to divide the initial restoration model into several sub-regions according to the restoration requirements, calculate the morphological difference measurement value of each sub-region according to the damage state and deformation characteristics of the ancient building to be restored, and divide the initial restoration model into a complete reconstruction area, a partial restoration area and an original state preservation area based on the morphological difference measurement value; The registration module is used to register the fully reconstructed area and the partially restored area using the ancient building parameter component library to obtain the conventional partial model, and to match the original preserved area using the fitting algorithm to obtain the detailed partial model; The model fusion module is used to fuse the conventional part model and the detailed part model in a unified coordinate system to obtain a three-dimensional model of the ancient building, and to establish a digital repair file based on the three-dimensional model of the ancient building and the damage status and deformation characteristics of the ancient building to be repaired.

[0015] The present invention provides a modeling method and system for ancient building restoration. By processing the three-dimensional point cloud data of the firmware to be repaired and performing regional division based on morphological differential measurement values, accurate digital modeling of the ancient building is achieved. While ensuring the overall structural standardization of the ancient building, the characteristics and details of the original building can be preserved, significantly improving the accuracy, scientific nature and efficiency of ancient building restoration. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is a flow chart of a modeling method for ancient building restoration provided by the present invention. DETAILED DESCRIPTION

[0018] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0019] like Figure 1 As shown, the present invention provides a modeling method for ancient building restoration, comprising the following steps: S1. Collect 3D point cloud data of the ancient building to be restored, pre-process the 3D point cloud data to obtain standardized data, and record the damage status and deformation characteristics of the ancient building to be restored; It is understandable that the three-dimensional point cloud data of ancient buildings can be detected using instruments such as three-dimensional laser scanners, handheld mobile laser scanner cameras, and thermal infrared imagers. In the actual acquisition process of ancient buildings, a variety of equipment can be combined to formulate a detailed acquisition plan based on the specific circumstances of the ancient buildings. For example, for large palace complexes, drones may be used for overall aerial photography and photogrammetry to obtain the overall layout and external form of the complex; ground laser scanners may be used to perform medium-precision scans of major buildings to obtain basic geometric forms; and high-precision structured light scanners may be used to perform local fine scanning of exquisite carvings, paintings, and other parts. The present invention does not impose specific restrictions on the method of collecting three-dimensional point cloud data.

[0020] In one embodiment of the present invention, preprocessing includes data cleaning and noise removal, multi-site data registration, coordinate normalization and direction correction.

[0021] Understandably, since ancient buildings often have fine carvings and traces of weathering on their surfaces, data cleaning and noise removal are required to distinguish between real surface details and noise, to avoid excessive smoothing and loss of important historical information. The collected 3D point cloud data should be aligned to a unified coordinate system, and then converted to a standard coordinate system to establish a local coordinate system with the main building as a reference. The main axis direction of the building should be corrected to align it with the coordinate axis, and the direction should be corrected according to the orientation rules of the ancient building (such as facing north and south).

[0022] S2. Obtain historical data of ancient buildings to construct a parametric component library of ancient buildings. Match and correct the standardized data with the parametric component library of ancient buildings to obtain an initial restoration model of the ideal state of the ancient buildings before damage. The parametric component library of ancient buildings includes a basic construction model set, a mortise and tenon connection relationship library, a historical style database, a material and process information library, and a dynamic update mechanism.

[0023] Understandably, the ancient architectural parametric component library should not only include the common component types in traditional ancient architecture, but also consider regional differences, dynastic changes, and the diversity of special crafts. Therefore, constructing an ancient architectural parametric component library based on historical data requires extracting the basic parameter relationships and geometric features of various types of architectural structures. It is necessary to collect component examples from different regions, different eras, and different types of buildings, and consider the relationships between ancient architectural components. For example, the sizes and forms of components such as capitals and brackets, beams and purlins are interrelated. At the same time, because Chinese ancient architecture shows rich changes in different regions and different historical periods, such as the differences between northern and southern architecture, and the changes in style between the Tang and Song dynasties and the Ming and Qing dynasties, the ancient architectural parametric component library constructed based on historical data should provide parametric models with regional and era labels.

[0024] In one embodiment of the present invention, the basic component model set includes multiple types of basic component parametric models, and the geometric shape of each basic component model is controlled by core parameters. The core parameters include the basic shape, size and structural characteristics of the component, such as length, width, height, cross-sectional shape, curvature, etc.

[0025] In one embodiment of the present invention, the mortise and tenon connection relationship library is used to record the connection methods and positional relationships between various components, including parameters such as mortise and tenon type, connection position, and connection angle.

[0026] In one embodiment of the present invention, the historical style database includes style characteristic parameters of ancient buildings from different dynasties and regions, such as roof curve equations, bracket projection ratios, decorative patterns, etc.

[0027] In one embodiment of the present invention, the material and process information library is used to record the material properties, manufacturing processes and common deformation characteristics of various components.

[0028] In one embodiment of the present invention, a dynamic update mechanism can automatically expand the ancient architectural parametric component library based on newly discovered component features, including processes such as feature extraction, parametric modeling, expert verification, and establishment of associations within the library. The dynamic update mechanism specifically includes: When the matching degree between the standardized data and the existing models in the ancient building parametric component library reaches a preset threshold (usually 70%), the new component identification process is triggered: Perform in-depth feature analysis on standardized data to extract geometric, topological, and semantic features. Geometric features include the main size ratios, contour shapes, and surface curvature distribution; topological features include the internal structural relationships of components and hole distribution; and semantic features are inferred based on the functional attributes and positional relationships of components. The distance similarity between geometric, topological, and semantic features and existing component types in the ancient architectural parametric component library is calculated. For example, a special type of bracket may be identified as a variant of the "bracket type" component, and the most similar basic bracket models in the ancient architectural parametric component library are found as references. Segment the standardized data to identify the main components and key feature lines of the new component, analyze the relationship between geometric elements, determine key parameters and the constraints between key parameters; for example, for bracket sets, identify key parameters such as the number of brackets, number of layers, jump distance, and tilt, as well as the proportional relationship between these parameters; The identified key parameters and the constraints between them are mapped to a predefined construction operation sequence, and a parameter-controlled three-dimensional model is automatically generated. The generated three-dimensional model is then added to the parametric component library of ancient buildings.

[0029] Furthermore, experts will supplement the generated three-dimensional model with cultural background information, manufacturing process characteristics or historical evolution laws of the corresponding components, and will also confirm the classification position of the new components to decide whether it is a variant of a certain type of component or to establish a completely new component category.

[0030] In one embodiment of the present invention, matching the standardized data with the ancient architectural parameterized component library and performing posture correction specifically includes: Extracting key geometric features of the standardized data, aligning the key geometric features of the standardized data with geometric reference points of the ancient architectural parametric component library, and generating a preliminary matching result; wherein the key geometric feature points include structural feature points, curvature feature points, edge feature points, and plane feature points; Based on the preliminary matching results, the relative relationship constraints between components are established in combination with the constraints of ancient architectural structural principles, including flatness, verticality and symmetry. Based on the relative relationship constraints between components and the preliminary matching results, the iterative closest point algorithm is used to process the standardized data.

[0031] Specifically, the iterative closest point algorithm specifically includes: Weights are assigned to different categories of feature points of key geometric features, and a registration objective function integrating structural constraints is constructed based on the weights of different categories of feature points. The registration objective function integrating structural constraints includes a point-to-point distance term, an ancient architectural structural principle constraint, and a regularization constraint. The point-to-point distance term is the sum of the distances between the points in the standardized data and the parametric construction model in the ancient architectural parametric component library. The formula of the registration objective function is: Etotal=Edistance+λ1×Estructure+λ2×Eregularity Among them, Etotal represents the overall registration objective function, Edistance represents the point pair term, λ1 represents the structural constraint weight coefficient, Estructure represents the ancient architectural structural principle constraint, λ2 represents the regularization weight coefficient, and Eregularity represents the regularization constraint term; Roughly align the preliminary matching results with the main components of the ancient building parametric component library to determine the basic framework of standardized data; Based on the relative relationship constraints between components, medium-precision alignment and fine alignment are performed with the component categories and component structures of the ancient building parametric component library respectively.

[0032] It can be understood that the constraints of the structural principles of ancient buildings include constraints on the position relationship of components (such as columns must fall on the base, beams must be placed on the capitals, and brackets must be located in a specific position between the capitals and the eaves, etc.), structural geometry constraints (including the verticality of columns, the horizontality of transverse components (such as beams and rafters), the specific form of roof curves, the rules of bracket stacking, etc.), proportion constraints (the sizes of various parts of ancient buildings usually follow specific proportional relationships, such as the modular system of "materials" and "fans", as well as different scale regulations for buildings of different levels) and component combination logic (such as the matching relationship between a specific type of bracket and a specific type of roof, the traditional combination method between different components, etc.). The constraints of the structural principles of ancient buildings are the key rules of ancient buildings, such as columns should be perpendicular to the ground, beams should be placed horizontally, and bracket layers should have a regular stacking relationship.

[0033] S3. Divide the initial restoration model into several sub-areas according to the restoration requirements, calculate the morphological difference measurement value of each sub-area according to the damage state and deformation characteristics of the ancient building to be restored, and divide the initial restoration model into a complete reconstruction area, a partial restoration area and an original state preservation area based on the morphological difference measurement value.

[0034] The present invention calculates morphological differential metrics and divides the area into completely rebuilt areas, partially repaired areas and original state preservation areas accordingly, so that the most suitable treatment strategy can be adopted for ancient building areas in different states. This not only ensures the structural correctness of severely damaged parts, but also maximizes the preservation of the original characteristics of higher-value parts, thereby improving the scientificity and rationality of the restoration plan.

[0035] Specifically, the calculation of the morphological difference metric value of each sub-region according to the damage state and deformation characteristics of the ancient building to be restored specifically includes: The damage state and deformation characteristics of the ancient buildings to be restored: establish a characteristic description model and assessment benchmark for different types of damage; the damage state includes surface weathering, component fracture and structural deformation, and the deformation characteristics include component bending, warping and historical repair traces; Adaptively downsample the normalized data of each sub-region to generate a low-resolution point cloud representation; Determine a neighborhood point set for each point in each sub-area, where the neighborhood size is dynamically adjusted according to the component type and local normalized data density; Calculate the position difference vector between each point in the subregion and all points in the neighborhood point set, and calculate the mean of all position difference vectors in the neighborhood; The morphological difference metric value of each point in the sub-region is calculated based on the position difference vector and the mean of the position difference vector. The calculation formula is: ; in, Indicates a point The morphological difference metric at , represents any point in the normalized data, Indicates a point The neighborhood point set of represents the L2 norm, Represents neighborhood Any point in represents the mean of the vectors between points in the neighborhood, Indicates a point The number of points in the neighborhood of represents the i-th point in the subregion, represents the points in the subregion except i.

[0036] It is understandable that the restoration of ancient buildings requires accurate identification of which areas are well preserved, which areas are partially damaged, and which areas are completely damaged. The traditional method of directly comparing the geometric distance between point clouds and parametric models is too simple and cannot reflect the structural change characteristics of the region. The damage of ancient buildings is often manifested as changes in local structural relationships, rather than just the position offset of points. For example, the warping and deformation of wooden components is characterized by changes in the overall shape but the destruction of local structural relationships. Therefore, the present invention calculates the morphological differential measurement values ​​of the sub-areas of the ancient buildings to be repaired and divides the regions accordingly. It can capture the changes in structural relationships and more accurately reflect the damage status and repair needs of the ancient buildings, thereby guiding more scientific and reasonable repair decisions.

[0037] Furthermore, the sub-regions are divided into a completely reconstructed region, a partially repaired region, and an original state preserved region based on the morphological difference metric value, specifically including: Calculate the global distribution of the morphological difference metrics of all sub-regions and determine the division thresholds T1 and T2, where T1 is the standard for light damage to ancient buildings and T2 is the standard for severe damage to ancient buildings; The sub-areas with the average value of the morphological difference metric lower than T1 are divided into the original state preservation area, and only protection and reinforcement treatment is performed; The sub-areas with the average value of the morphological difference metric higher than T2 are divided into completely reconstructed areas, which need to be reconstructed in accordance with the traditional ancient architectural style and local architectural style; The spatial distribution characteristics of the morphological difference metric values ​​of the sub-regions whose average values ​​are between T1 and T2 are analyzed. The spatial distribution characteristics analysis includes calculating the local clustering coefficient, geometric characteristic vector and position change rate of the morphological difference metric values ​​of the sub-regions. The local clustering coefficient reflects the spatial distribution characteristics of the damage or deformation of the ancient building to be repaired. The geometric characteristic vector can reflect whether the deformation of the ancient building to be repaired is caused by the design characteristics of the components themselves or by later damage. The position change rate reflects the gradual characteristics of the deformation or damage of the components in the ancient building to be repaired: If the local clustering coefficient of the sub-region morphological difference metric value is greater than the clustering threshold, the geometric feature vector of the sub-region morphological difference metric value is further calculated: If the geometric feature vector of the morphological difference metric value of the sub-region has a high matching degree with the components in the parametric component library of ancient buildings, the sub-region will be divided into the original state preservation area; If the geometric feature vector of the morphological difference metric value of the sub-region does not match the components in the parametric component library of ancient buildings, the sub-region is divided into a partial restoration area; If the local clustering coefficient of the sub-region morphological difference metric value is less than the clustering threshold, the rate of change of the morphological difference metric value with spatial position is calculated: If the rate of change of the morphological difference metric value with spatial position is greater than the change rate threshold, the subregion is a partially repaired region; If the rate of change of the morphological difference metric value with spatial position is less than the change rate threshold, the subregion is divided into a completely reconstructed region.

[0038] It can be understood that T1 is the average value of the global morphological difference measurement value plus one standard deviation, and T2 is the average value of the global morphological difference measurement value plus two standard deviations. The thresholds T1, T2, aggregation thresholds and change rate thresholds can all be set according to the actual situation and restoration requirements of the ancient buildings to be repaired. The present invention does not impose specific restrictions on this.

[0039] Specifically, the local clustering coefficient, geometric eigenvector and position change rate of the sub-region morphological difference metric are calculated, including: Determine the point set HR in the sub-region whose morphological difference metric value is higher than the local threshold, and calculate the spatial proximity between these points. Then the local clustering coefficient = the actual number of adjacent high-difference point pairs / the maximum possible number of adjacent high-difference point pairs, where the local threshold is the mean of the sub-region morphological difference metric value plus half the standard deviation; Perform principal component analysis on the point set HR, extract the first three principal direction vectors and their corresponding eigenvalue ratios, calculate the principal curvature and variation of the surface formed by the point set HR in different directions, calculate shape descriptors (including geometric indicators such as compactness, linearity, and flatness), extract the distribution of morphological features at different scales, and integrate the morphological features at different scales into a multidimensional vector, recorded as the geometric feature vector; Calculate the spatial gradient vector of the differential metric value of each point in the sub-region, and calculate the average value of the gradient vector modulus of all points in the sub-region, which is recorded as the position change rate.

[0040] S4. The fully reconstructed area and the partially restored area are registered using the ancient building parametric component library to obtain a conventional partial model, and the original preserved area is matched using a fitting algorithm to obtain a detailed partial model; Specifically, the registration of the completely reconstructed area and the partially restored area using the ancient building parametric component library to obtain a conventional partial model specifically includes: Extract the principal axis directions and key geometric parameters of the standardized data of the fully reconstructed area and the partially restored area, match them to the corresponding models in the parametric component library of ancient buildings, and obtain the initial conventional model; Adjust the core parameters of the initial conventional model, and optimize the position and posture of the components in the initial conventional model by combining the spatial relationship and connection constraints between the components of the ancient building; the core parameters include the basic form, size and structural characteristics of the components; The registration results are iterated step by step until the position and posture of the components in the initial conventional model meet the preset standards, and the conventional partial model is obtained.

[0041] Through continuous parametric alignment, we ensure that the components conform to the standard form and proportional relationship of the ancient buildings, reduce errors caused by incomplete point clouds or noise, and take into account the unique structural rules and connection constraints of ancient buildings. This makes the reconstruction model of the ancient building to be completed closer to the original building in form. This not only ensures the standardization of the components of the ancient buildings (in line with traditional construction rules and craftsmanship requirements), but also retains the individual characteristics of the specific buildings through parameter adjustment.

[0042] Specifically, a specific embodiment is used to illustrate: Core parameter adjustments for the initial conventional model included adjustments to corner bracket sets (such as adjusting the overall height, the proportions of bracket sets at each level, and the angle of the sway bar) and eaves beam parameters (such as adjusting the cross-section of the eaves beam model, setting the center sag, and adjusting the dimensions of the mortise and tenon joints at the beam ends and bracket sets). The spatial relationships and connection constraints between ancient architectural components were specifically determined by applying the connection rules outlined in the Qing Dynasty's "Engineering Practice Rules": for example, bracket sets and eaves beams were connected using the "bucket-mounted" method, and bracket sets at each level were connected using the "interlaced and overlapped" method for mortise and tenon joints. Constraint points included the beam end-to-bracket connection point (three points), the bracket set-to-wall / column connection point (four points), and key internal bracket set points (12 points).

[0043] Furthermore, the matching of the original retained area with a fitting algorithm to obtain a detailed part model specifically includes: Extracting a basic template model of the same type as the components in the original preservation area from the parametric component library of the ancient buildings, and performing Gaussian regression on the standardized data of the original preservation area to generate surface weathering characteristics of components of different materials; Performing skeleton extraction on the basic template model, identifying the mortise and tenon joints, the center lines of the load-bearing components, and the key points of mechanical transmission of the components, and establishing a deformation influence weight system from the structural skeleton to the component surface, wherein the weight value decreases smoothly as the distance from the surface point to the skeleton increases; Based on the deformation influence weight system and the surface weathering characteristics of components made of different materials, the optimal repair parameters of the traditional component skeleton are calculated. During the calculation of the repair parameters, the structural rules of the ancient building are applied to constrain the repair parameters to obtain a repair model. The repair parameters include the curvature of the beam frame, the inclination angle of the column body, and the stacking deformation of the brackets. Based on the deformed component skeleton and deformation influence weight system, control elements corresponding to the key nodes of traditional wood work are set, and radial basis functions that conform to the deformation characteristics of traditional materials are used for surface repair interpolation. The historical and cultural information of the ancient buildings is extracted from the standardized data of the original preserved area and mapped to the surface of the repair model through normal displacement and texture mapping.

[0044] Normal displacement and texture mapping are used to restore the model surface, extracting cultural information such as textures, inscriptions, and paintings from standardized data. This information is then applied to the reconstructed surface through normal displacement and texture mapping to create a detailed model. The weight in the deformation influence weight system represents the degree of influence of the skeleton deformation on a specific surface point: a weight of 1 indicates that the point completely follows the skeleton deformation, a weight of 0 indicates that the point is unaffected by the skeleton deformation, and a weight between 0 and 1 indicates that the point is partially affected by the skeleton deformation.

[0045] The present invention achieves more accurate reconstruction by adopting corresponding weathering models and deformation characteristics based on the characteristics of different materials (such as different woods, stones, bricks and tiles, etc.). It also preserves historical and cultural information that is difficult to directly express with geometric shapes (such as paintings, carvings, traces of use, etc.) through normal replacement and texture mapping, thereby retaining the cultural value of the ancient buildings while meeting the restoration parameters required for structural safety.

[0046] S5. The conventional part model and the detailed part model are integrated in a unified coordinate system to obtain a three-dimensional model of the ancient building, and a digital repair file is established based on the three-dimensional model of the ancient building and the damage state and deformation characteristics of the ancient building to be repaired, wherein the digital repair file includes component number, repair process, material selection and cultural value assessment.

[0047] Specifically, the conventional part model and the detail part model are integrated in a unified coordinate system, including: Analyze the boundary characteristics of the general part model and the detailed part model, establish the spatial correspondence between different model areas, and set a transition zone in the boundary area between the general part model and the detailed part model; the width of the transition zone is adaptively adjusted according to the scale characteristics and structural characteristics of the ancient building components; A distance-weighted fusion algorithm is used to perform smooth transition processing in the transition zone. By calculating the spatial distance between each point in the transition zone and the conventional part model and the detailed part model, a weight function that changes smoothly with the distance is generated. Based on the weight function, the geometric features and surface properties of the conventional part model and the detailed part model are weightedly fused.

[0048] By setting an adaptive transition zone in the boundary area between the conventional part model and the detailed part model, and using a distance weight-based fusion algorithm for smooth transition processing, the connection problem between different processing strategy areas is effectively solved, avoiding unnatural breaks or jumps at the boundary of the model, and improving the integrity and visual effect of the final three-dimensional model. At the same time, the digital restoration archives have accumulated valuable digital resources for subsequent research and protection work, and promoted the inheritance and development of ancient building protection technology.

[0049] The present invention processes the three-dimensional point cloud data of the firmware to be repaired and divides the area based on morphological differential measurement values, thereby achieving accurate digital modeling of ancient buildings. While ensuring the overall structural standardization of the ancient buildings, it can retain the characteristics and details of the original buildings, significantly improving the accuracy, scientific nature and efficiency of ancient building restoration.

[0050] The present invention also provides a modeling system for ancient building restoration, which implements the modeling method described above, including: The data acquisition and processing module is used to collect the 3D point cloud data of the ancient building to be restored, pre-process the 3D point cloud data to obtain standardized data, and record the damage status and deformation characteristics of the ancient building to be restored; The initial model construction module is used to obtain historical data of ancient buildings to build a parametric component library of ancient buildings, match the standardized data with the parametric component library of ancient buildings, and perform posture correction to obtain an initial restoration model of the ideal state of the ancient building before damage; A region division module is used to divide the initial restoration model into several sub-regions according to the restoration requirements, calculate the morphological difference measurement value of each sub-region according to the damage state and deformation characteristics of the ancient building to be restored, and divide the initial restoration model into a complete reconstruction area, a partial restoration area and an original state preservation area based on the morphological difference measurement value; The registration module is used to register the fully reconstructed area and the partially restored area using the ancient building parameter component library to obtain the conventional partial model, and to match the original preserved area using the fitting algorithm to obtain the detailed partial model; The model fusion module is used to fuse the conventional part model and the detailed part model in a unified coordinate system to obtain a three-dimensional model of the ancient building, and to establish a digital repair file based on the three-dimensional model of the ancient building and the damage status and deformation characteristics of the ancient building to be repaired.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A modeling method for ancient building restoration, characterized in that: The following steps are involved: S1. Collect 3D point cloud data of the ancient building to be restored, pre-process the 3D point cloud data to obtain standardized data, and record the damage status and deformation characteristics of the ancient building to be restored; S2. Acquire historical data of ancient buildings to construct a parametric component library of ancient buildings, match the standardized data with the parametric component library of ancient buildings, and perform posture correction to obtain an initial restoration model of the ideal state of the ancient buildings before they were damaged; S3. Divide the initial restoration model into several sub-areas according to the restoration requirements, calculate the morphological difference metric value of each sub-area according to the damage state and deformation characteristics of the ancient building to be restored, and divide the initial restoration model into a complete reconstruction area, a partial restoration area, and an original state preservation area based on the morphological difference metric value; S4. The fully reconstructed area and the partially restored area are registered using the ancient building parametric component library to obtain a conventional partial model, and the original preserved area is matched using a fitting algorithm to obtain a detailed partial model; S5. The conventional part model and the detailed part model are integrated in a unified coordinate system to obtain a three-dimensional model of the ancient building, and a digital repair file is established based on the three-dimensional model of the ancient building and the damage state and deformation characteristics of the ancient building to be repaired.

2. A modeling method for ancient building restoration according to claim 1, characterized in that: The ancient building parametric component library includes a basic construction model set, a mortise and tenon connection relationship library, a historical style database, a material and craft information library and a dynamic update mechanism.

3. A modeling method for ancient building restoration according to claim 1, characterized in that: The matching of the standardized data with the ancient architectural parametric component library and posture correction specifically includes: Extracting key geometric features of the standardized data, aligning the key geometric features of the standardized data with geometric reference points of the ancient architectural parametric component library, and generating a preliminary matching result; wherein the key geometric feature points include structural feature points, curvature feature points, edge feature points, and plane feature points; Based on the preliminary matching results, the relative relationship constraints between components are established in combination with the constraints of ancient architectural structural principles, including flatness, verticality and symmetry. Based on the relative relationship constraints between components and the preliminary matching results, the iterative closest point algorithm is used to process the standardized data.

4. A modeling method for ancient building restoration according to claim 3, characterized in that: The iterative closest point algorithm specifically includes: Assigning weights to different categories of feature points of key geometric features, and constructing a registration objective function fused with structural constraints based on the weights of the different categories of feature points. The registration objective function fused with structural constraints includes a point-to-point distance term, a constraint based on the structural principles of ancient architecture, and a regularization constraint. The point-to-point distance term is the sum of the distances between points in the standardized data and the parametric construction models in the parametric component library of ancient architecture. Roughly align the preliminary matching results with the main components of the ancient building parametric component library to determine the basic framework of standardized data; Based on the relative relationship constraints between components, medium-precision alignment and fine alignment are performed with the component categories and component structures of the ancient building parametric component library respectively.

5. The modeling method for ancient building restoration according to claim 1, characterized in that: The calculation of the morphological difference metric value of each sub-region according to the damage state and deformation characteristics of the ancient building to be restored specifically includes: The damage state and deformation characteristics of the ancient buildings to be restored: establish a characteristic description model and assessment benchmark for different types of damage; the damage state includes surface weathering, component fracture and structural deformation, and the deformation characteristics include component bending, warping and historical repair traces; Adaptively downsample the normalized data of each sub-region to generate a low-resolution point cloud representation; Determine a neighborhood point set for each point in each sub-area, where the neighborhood size is dynamically adjusted according to the component type and local normalized data density; Calculate the position difference vector between each point in the subregion and all points in the neighborhood point set, and calculate the mean of all position difference vectors in the neighborhood; The morphological difference metric value of each point in the sub-region is calculated based on the position difference vector and the mean of the position difference vector. The calculation formula is: ； in, Indicates a point The morphological difference metric at , represents any point in the normalized data, Indicates a point The neighborhood point set of represents the L2 norm, Represents neighborhood Any point in represents the mean of the vectors between points in the neighborhood, Indicates a point The number of points in the neighborhood of represents the i-th point in the subregion, represents the points in the subregion except i.

6. A modeling method for ancient building restoration according to claim 5, characterized in that: The sub-regions are divided into a completely reconstructed region, a partially repaired region, and an original state preserved region based on the morphological difference metric value, specifically including: Calculate the global distribution of the morphological difference metrics of all sub-regions and determine the division thresholds T1 and T2, where T1 is the standard for light damage to ancient buildings and T2 is the standard for severe damage to ancient buildings; The sub-areas with the average value of the morphological difference metric lower than T1 are divided into the original state preservation area, and only protection and reinforcement treatment is performed; The sub-areas with the average value of the morphological difference metric higher than T2 are divided into completely reconstructed areas, which need to be reconstructed in accordance with the traditional ancient architectural style and local architectural style; The sub-regions whose average morphological difference metric values ​​are between T1 and T2 are subjected to a spatial distribution characteristic analysis of the morphological difference metric values, wherein the spatial distribution characteristic analysis includes calculating the local clustering coefficient, geometric characteristic vector and position change rate of the sub-region morphological difference metric values.

7. The modeling method for ancient building restoration according to claim 1, characterized in that: The registration of the completely reconstructed area and the partially restored area using the ancient building parametric component library to obtain a conventional partial model specifically includes: Extract the principal axis directions and key geometric parameters of the standardized data of the fully reconstructed area and the partially restored area, match them to the corresponding models in the parametric component library of ancient buildings, and obtain the initial conventional model; Adjust the core parameters of the initial conventional model, and optimize the position and posture of the components in the initial conventional model by combining the spatial relationship and connection constraints between the components of the ancient building; the core parameters include the basic form, size and structural characteristics of the components; The registration results are iterated step by step until the position and posture of the components in the initial conventional model meet the preset standards, and the conventional partial model is obtained.

8. A modeling method for ancient building restoration according to claim 7, characterized in that: The matching of the original retained area with a fitting algorithm to obtain a detailed part model specifically includes: Extracting a basic template model of the same type as the components in the original preservation area from the parametric component library of the ancient building, and performing Gaussian regression on the standardized data of the original preservation area to generate surface weathering characteristics of components of different materials; Extracting the skeleton of the basic template model, identifying the mortise and tenon joints, the center lines of the load-bearing components, and the key points of mechanical transmission of the components, and establishing a deformation influence weight system from the structural skeleton to the component surface; Based on the deformation influence weight system and the surface weathering characteristics of components made of different materials, the optimal repair parameters of the traditional component skeleton are calculated. During the calculation of the repair parameters, the structural rules of the ancient building are applied to constrain the repair parameters to obtain a repair model. The repair parameters include the curvature of the beam frame, the inclination angle of the column body, and the stacking deformation of the brackets. Based on the deformed component skeleton and deformation influence weight system, control elements of key nodes are set, and radial basis functions that conform to the deformation characteristics of traditional materials are used for surface repair interpolation. The historical and cultural information of the ancient buildings is extracted from the standardized data of the original preserved area and mapped to the surface of the repair model through normal displacement and texture mapping.

9. The modeling method for ancient building restoration according to claim 1, characterized in that: The conventional part model and the detailed part model are integrated in a unified coordinate system, including: Analyze the boundary characteristics of the general part model and the detailed part model, establish the spatial correspondence between different model areas, and set a transition zone in the boundary area between the general part model and the detailed part model; A distance-weighted fusion algorithm is used to perform smooth transition processing in the transition zone. By calculating the spatial distance between each point in the transition zone and the conventional part model and the detailed part model, a weight function that changes smoothly with the distance is generated. Based on the weight function, the geometric features and surface properties of the conventional part model and the detailed part model are weightedly fused.

10. A modeling system for ancient building restoration, characterized in that: Implementing the modeling method according to any one of claims 1 to 9, comprising: The data acquisition and processing module is used to collect the 3D point cloud data of the ancient building to be restored, pre-process the 3D point cloud data to obtain standardized data, and record the damage status and deformation characteristics of the ancient building to be restored; The initial model construction module is used to obtain historical data of ancient buildings to build a parametric component library of ancient buildings, match the standardized data with the parametric component library of ancient buildings, and perform posture correction to obtain an initial restoration model of the ideal state of the ancient building before damage; A region division module is used to divide the initial restoration model into several sub-regions according to the restoration requirements, calculate the morphological difference measurement value of each sub-region according to the damage state and deformation characteristics of the ancient building to be restored, and divide the initial restoration model into a complete reconstruction area, a partial restoration area and an original state preservation area based on the morphological difference measurement value; The registration module is used to register the fully reconstructed area and the partially restored area using the ancient building parameter component library to obtain the conventional partial model, and to match the original preserved area using the fitting algorithm to obtain the detailed partial model; The model fusion module is used to fuse the conventional part model and the detailed part model in a unified coordinate system to obtain a three-dimensional model of the ancient building, and to establish a digital repair file based on the three-dimensional model of the ancient building and the damage status and deformation characteristics of the ancient building to be repaired.

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