Light cultural relic three-dimensional digital modeling method based on automatic rotary table rotation structure

Abstract

The invention discloses a light cultural relic three-dimensional digital modeling method based on an automatic rotary table rotation structure, and relates to the technical field of three-dimensional digital modeling, and the method comprises the steps: obtaining an optimal rotation path and an optimal angle increment of an automatic rotary table through a simulated annealing algorithm based on the contour information of a cultural relic; based on the optimal rotation path and the optimal angle increment of the automatic turntable, scanning the cultural relics through a structured light scanner to obtain initial three-dimensional point cloud data; based on the preliminary three-dimensional point cloud data, processing through a regional hierarchical optimization algorithm and a topological structure reconstruction method to obtain high-precision three-dimensional point cloud data; and based on the high-precision three-dimensional point cloud data, identifying the important structure of the cultural relic through a feature extraction algorithm, and generating a three-dimensional digital model of the cultural relic. According to the method, the rotation path and the angle increment of the automatic turntable are optimized by adopting the simulated annealing algorithm, and the three-dimensional point cloud data are processed in combination with the regional hierarchical optimization algorithm, so that the accuracy and the working efficiency of cultural relic three-dimensional modeling are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of three-dimensional digital modeling, in particular to a three-dimensional digital modeling method for cultural relics based on automatic turntable rotating structured light. Background Art

[0002] With the rapid development of digital technology, three-dimensional digital modeling technology has gradually been widely used in the field of cultural relics protection and restoration. Traditional three-dimensional modeling methods mostly rely on contact measurement or simple photogrammetry technology. However, these methods have certain limitations in the process of cultural relics scanning, especially when dealing with complex surfaces or details, it is difficult to accurately capture the high-precision three-dimensional information of cultural relics. In recent years, structured light three-dimensional scanning technology, as a non-contact high-precision measurement method, has gradually become an important tool for digital modeling of cultural relics. Structured light technology projects a known grating pattern onto the surface of the cultural relic, and uses the deformed grating pattern to restore the three-dimensional geometric shape of the object, thereby obtaining high-precision three-dimensional point cloud data. On this basis, automatic turntable technology is introduced to automatically control the rotation of the cultural relic, optimize the scanning angle, and achieve all-round, high-precision three-dimensional scanning. However, the existing technology still has certain deficiencies in the optimization of the rotation path and angle increment, resulting in low scanning efficiency. The acquired point cloud data may still contain noise, which affects the subsequent model reconstruction and accuracy improvement.

[0003] Although the application of structured light technology in the digital modeling of cultural relics has made certain progress, the existing technology still faces some challenges. First, there is still room for improvement in the optimization of the rotation path and angle increment of the automatic turntable. Most of the existing methods use fixed paths or simple angle increment settings, which makes it difficult to adaptively adjust the scanning path according to the surface characteristics of the cultural relics. This not only affects the scanning efficiency, but also may lead to the omission of certain important details on the surface of the cultural relics. Secondly, the existing three-dimensional point cloud data processing methods are often difficult to perform large-scale data processing quickly and efficiently while maintaining high precision. The three-dimensional data of cultural relics usually contains a lot of noise and redundant information, which needs to be processed and optimized through complex algorithms. The existing technology is often more complicated in point cloud optimization and topological structure reconstruction, and the processing speed is slow, and it is impossible to generate high-precision digital models in real time. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a three-dimensional digital modeling method for cultural relics based on automatic turntable rotating structured light, which solves the problems of insufficient optimization of automatic turntable path and angle increment and low efficiency of high-precision large-scale point cloud data processing.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for three-dimensional digital modeling of cultural relics based on automatic turntable rotating structured light, which includes collecting material characteristic data of cultural relics and preprocessing the material characteristic data of cultural relics; based on the preprocessed material characteristic data of cultural relics, obtaining the shape contour information of the cultural relics through a stereoscopic vision imaging method and a shape recognition algorithm; based on the shape contour information of the cultural relics, obtaining the optimal rotation path and optimal angle increment of the automatic turntable through a simulated annealing algorithm; based on the optimal rotation path and optimal angle increment of the automatic turntable, scanning the cultural relics through a structured light scanner to obtain preliminary three-dimensional point cloud data; based on the preliminary three-dimensional point cloud data, processing through a regional hierarchical optimization algorithm and a topological structure reconstruction method to obtain high-precision three-dimensional point cloud data; based on the high-precision three-dimensional point cloud data, identifying important structures of the cultural relics through a feature extraction algorithm to generate a three-dimensional digital model of the cultural relics.

[0008] As a preferred solution of the method for three-dimensional digital modeling of cultural relics based on automatic turntable rotating structured light of the present invention, wherein: the material characteristic data of the cultural relics include texture characteristics, gloss characteristics and spectral characteristics;

[0009] The preprocessing of the cultural relic material characteristic data includes data denoising, data normalization and data alignment.

[0010] As a preferred solution of the method for three-dimensional digital modeling of cultural relics based on automatic turntable rotating structured light of the present invention, wherein: based on the pre-processed material characteristic data of the cultural relics, the contour information of the cultural relics is obtained through a stereoscopic vision imaging method and a shape recognition algorithm, and the specific steps are as follows:

[0011] Based on the pre-processed material characteristic data of cultural relics, multi-view collection of cultural relics is performed through stereoscopic visual imaging method to obtain multi-view cultural relic images;

[0012] Through the feature matching algorithm, the collected multi-view cultural relic images are aligned and registered;

[0013] Based on the aligned and registered multi-view cultural relic images, feature points of the multi-view cultural relic images are extracted through the corner detection algorithm;

[0014] Based on the positions and relative relations of the feature points of the multi-view cultural relic images in three-dimensional space, the spatial relations of the feature points of the multi-view cultural relic images are obtained;

[0015] The contour recognition algorithm is used to identify the shape of the cultural relic based on the spatial relationship of the feature points of the multi-view cultural relic images, and finally the shape contour information of the cultural relic is obtained.

[0016] As a preferred solution of the method for three-dimensional digital modeling of cultural relics based on automatic turntable rotating structured light of the present invention, wherein: based on the contour information of the cultural relics, through the simulated annealing algorithm, the specific steps are as follows:

[0017] Based on the adaptability requirements of the cultural relic outline information, the current rotation path and angle increment of the automatic turntable are generated through a random generation method;

[0018] Based on the contour information of the cultural relic, the projection matrix is ​​used to map it to the two-dimensional image plane to generate the corresponding pixel points of the cultural relic contour;

[0019] Combining the illumination direction, viewing angle distribution and material characteristics of the cultural relic, the color grayscale value of each pixel point of the cultural relic outline is calculated to obtain the image intensity value of the pixel point of the cultural relic outline;

[0020] Based on the current rotation path and angle increment of the automatic turntable, combined with the image intensity value of the pixel points of the cultural relic outline, the error value between the current rotation path and angle increment and the cultural relic outline is calculated, and the expression is:

[0021]

[0022] Among them, F(θ,φ) represents the error value between the current rotation path θ and the angle increment φ and the shape of the cultural relic, I i (θ, φ) represents the image intensity value of the pixel point of the i-th artifact outline under the current rotation path θ and angle increment φ. It represents the ideal target image intensity value of the i-th pixel point of the cultural relic outline under the current rotation path θ and angle increment φ, dθ represents the small angle increment of the current rotation path θ, i represents the index variable of the pixel point of the cultural relic outline, and N represents the number of pixel points of the cultural relic outline.

[0023] As a preferred solution of the method for three-dimensional digital modeling of cultural relics based on automatic turntable rotating structured light of the present invention, wherein: the optimal rotation path and optimal angle increment of the automatic turntable are obtained, and the specific steps are as follows:

[0024] By analyzing the appearance characteristics of historical relics and the distribution of historical image intensity values, the error threshold R is defined;

[0025] Based on the error value F(θ,φ) between the current rotation path θ and the angle increment φ and the shape of the cultural relic and the error threshold R, the optimal rotation path and the optimal angle increment are evaluated;

[0026] When F(θ,φ)>R, it means that the current rotation path and angle increment do not meet the standard and need to be further adjusted and optimized;

[0027] When F(θ,φ)≤R, it means that the current rotation path and angle increment are the optimal rotation path and the optimal angle increment.

[0028] As a preferred solution of the method for three-dimensional digital modeling of cultural relics based on automatic turntable rotating structured light of the present invention, wherein: the optimal rotation path and optimal angle increment based on the automatic turntable are used to scan the cultural relics through a structured light scanner to obtain preliminary three-dimensional point cloud data, and the specific steps are as follows:

[0029] Based on the optimal rotation path of the automatic turntable, the sub-path is divided by the discretized path method;

[0030] The structured light scanner scans the surface of the cultural relic in steps by projecting a regular grating grid based on the divided sub-paths and combined with the optimal angle increment to obtain a grating pattern;

[0031] Based on the grating pattern and combined with the calibration parameters of the structured light scanner, the three-dimensional coordinates of the surface of the cultural relic are analyzed through triangulation to obtain local three-dimensional point cloud data;

[0032] The local 3D point cloud data are spliced ​​using the iterative closest point algorithm, and the surface normal distribution and point cloud density are detected to obtain preliminary 3D point cloud data.

[0033] As a preferred solution of the method for three-dimensional digital modeling of cultural relics based on automatic turntable rotating structured light of the present invention, wherein: based on the preliminary three-dimensional point cloud data, the regional hierarchical optimization algorithm and the topological structure reconstruction method are used for processing to obtain high-precision three-dimensional point cloud data, the specific steps are as follows:

[0034] Based on the preliminary 3D point cloud data, regional stratification is performed using a 3D density clustering algorithm;

[0035] Based on each stratified region, stratified optimization is performed through the regional stratified optimization algorithm to calculate the optimized scalar value of the stratified region. The expression is:

[0036]

[0037] Among them, E(R k ) is the optimized scalar value of the kth stratified region, R k is the kth stratified region, n u is the normal direction of the u-th 3D point cloud data point in the hierarchical region, λ is the weight parameter of the smoothing term, Q(u) is the neighborhood point set of the u-th 3D point cloud data point in the hierarchical region, and w uj is the similarity value between the u-th 3D point cloud data point in the hierarchical region and the j-th neighborhood 3D point cloud data point in the hierarchical region, p u is the u-th 3D point cloud data point in the layered area, h jis the jth neighborhood 3D point cloud data point in the stratified region, k is the index variable of the stratified region, u is the index variable of the stratified region 3D point cloud data point, and j is the index variable of the stratified region neighborhood 3D point cloud data point;

[0038] Based on the optimized scalar value of the stratified area, a numerical optimization algorithm is used to perform minimization processing to obtain the stratified area after stratification optimization;

[0039] Based on the layered optimized layered regions, they are integrated into the global coordinate system through stitching and alignment algorithms, and the boundary points of each region are identified;

[0040] By using the constrained Laplace smoothing method, the connection relationship of the regional boundary points is adjusted to obtain the global topological structure;

[0041] Based on the global topological structure, the hierarchical area after hierarchical optimization is mapped to the global topological structure through a rigid transformation matrix to obtain high-precision three-dimensional point cloud data.

[0042] As a preferred solution of the method for three-dimensional digital modeling of cultural relics based on automatic turntable rotating structured light of the present invention, wherein: based on high-precision three-dimensional point cloud data, the important structure of the cultural relics is identified by feature extraction algorithm to generate a three-dimensional digital model of the cultural relics, and the specific steps are as follows:

[0043] Based on high-precision 3D point cloud data, the structure of cultural relics is analyzed through feature extraction algorithms to obtain the geometric features of cultural relics;

[0044] Based on the geometric features of the cultural relics, the high-precision 3D point cloud data is segmented through a clustering algorithm, and the surface structure areas of the cultural relics are identified;

[0045] Through the Poisson reconstruction method, triangulated meshing is performed based on the identified surface structure area of ​​the cultural relic to obtain a three-dimensional digital model of the cultural relic.

[0046] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for three-dimensional digital modeling of cultural relics based on automatic turntable rotation structured light as described in the first aspect of the present invention is implemented.

[0047] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for three-dimensional digital modeling of cultural relics based on automatic turntable rotation of structured light as described in the first aspect of the present invention is implemented.

[0048] The beneficial effects of the present invention are as follows: by using the simulated annealing algorithm to optimize the rotation path and angle increment of the automatic turntable, adaptive adjustment of the cultural relic scanning process is achieved, ensuring the comprehensive capture of the surface details of the cultural relic; at the same time, the three-dimensional point cloud data is processed in combination with the regional layered optimization algorithm, the accuracy and processing efficiency of the point cloud data are improved, and finally a high-precision three-dimensional digital model is generated. This method effectively solves the problems of incomplete scanning and inefficient data processing in the prior art, significantly improves the accuracy and work efficiency of three-dimensional modeling of cultural relics, and is particularly suitable for the fine reconstruction of complex morphological cultural relics. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0050] Figure 1 This is a flow chart of the method for three-dimensional digital modeling of cultural relics based on automatic turntable rotation structured light in Example 1.

[0051] Figure 2 This is a flowchart for obtaining preliminary three-dimensional point cloud data in Example 1. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0055] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a three-dimensional digital modeling method for cultural relics based on automatic turntable rotation structured light, including the following steps:

[0056] S1. Cultural relic material property data include texture properties, gloss properties and spectral properties.

[0057] S1.1. Preprocessing of cultural relics material characteristic data includes data denoising, data normalization and data alignment.

[0058] Specifically, data denoising: first, identify and classify noise types, such as Gaussian noise, impulse noise, etc., through statistical analysis or spectrum analysis, and then select appropriate filtering methods according to the characteristics of the noise. For example, a low-pass filter is used to smooth randomly distributed Gaussian noise; for impulse-type outliers, a median filter is used to suppress the influence of these outliers. In addition, multi-resolution analysis based on wavelet transform can be applied to separate signals and noise at different scales, and only the frequency components representing the true characteristics of the cultural relics are retained, thereby effectively reducing noise interference and improving data quality;

[0059] Data normalization: First, calculate the maximum and minimum values ​​(or mean and standard deviation) of each characteristic (such as texture characteristics, gloss characteristics, and spectral characteristics), and then apply linear transformation or Z-score normalization method to scale all data to a common range (such as [0,1] interval or with zero mean and unit variance);

[0060] Data alignment: First, use feature matching algorithms (such as SIFT or SURF) to automatically detect and extract stable feature points in the image. Then, based on the correspondence between these stable feature points, use the iterative closest point (ICP) algorithm or other similarity transformation models to estimate the optimal rotation and translation parameters to achieve accurate alignment between data sets.

[0061] S2. Based on the pre-processed material characteristic data of the cultural relics, the outline information of the cultural relics is obtained through a stereoscopic vision imaging method and a shape recognition algorithm.

[0062] S2.1. Based on the pre-processed material characteristic data of cultural relics, multi-perspective collection of cultural relics is performed through stereoscopic vision imaging method to obtain multi-perspective cultural relic images.

[0063] Specifically, first, based on the pre-processed material property data of the cultural relics, the optimal lighting conditions and camera parameter settings suitable for the surface characteristics of the cultural relics are determined to ensure that the most realistic texture and details are captured. Then, a stereo camera system equipped with a high-resolution sensor is used to surround the cultural relics and shoot from multiple predefined angles. These angles cover a full range of views of the cultural relics, including different positions of the top, bottom, and sides to ensure coverage without blind spots. At each shooting position, a synchronous trigger mechanism is used to ensure that the left and right cameras are exposed at the same time, thereby obtaining each pair of stereo images.

[0064] S2.2. Align and register the collected multi-view cultural relic images through feature matching algorithm.

[0065] Specifically, firstly, SIFT (Scale Invariant Feature Transform) or SURF (Speeded Up Robust Features) and other algorithms are used to detect and extract image feature points in each image. These image feature points include key points and their descriptors, which can uniquely identify the details of the surface of cultural relics. Then, the nearest neighbor or bidirectional nearest neighbor matching strategy is used to find similar image feature point pairs between images of different perspectives, and the RANSAC (Random Sample Consensus) algorithm is used to screen out the correctly matched point pairs to eliminate incorrect matches. Then, based on the successfully matched image feature point pairs, the relative position and attitude transformation matrix (including rotation and translation) between the cameras is calculated. This step usually involves solving the homography matrix or the basic matrix. Finally, these transformation matrices are applied to perform geometric correction on the images to ensure that all images can be accurately aligned in a unified spatial coordinate system, thereby achieving accurate registration of multi-perspective cultural relic images.

[0066] S2.3. Based on the aligned and registered multi-view cultural relic images, feature points of the multi-view cultural relic images are extracted using a corner detection algorithm.

[0067] Specifically, first, apply a corner detection algorithm, such as Harris corner detection or FAST (Features from Accelerated Segment Test), to each aligned image to identify pixels with high gradient changes and local uniqueness as potential corners. Then, use non-maximum suppression technology to remove redundant points and retain only the local strongest response points to ensure the stability and distinctiveness of the selected corners. Then, calculate the descriptor around each corner point to capture the texture information of its neighborhood, which helps with subsequent feature matching. Finally, combine the consistency constraints between multi-view images, screen and integrate corners from different viewpoints, and finally obtain the feature points of multi-view cultural relics images.

[0068] S2.4. Based on the positions and relative relationships of the feature points of the multi-view cultural relic images in three-dimensional space, the spatial relationship of the feature points of the multi-view cultural relic images is obtained.

[0069] Specifically, first, using the principle of triangulation, based on the known camera intrinsic and extrinsic parameters (obtained through previous alignment and registration), combined with the successfully matched feature point pairs with the same name, the precise coordinates of each multi-view cultural relic image feature point in three-dimensional space are calculated, and then a spatial adjacency graph of the multi-view cultural relic image feature points is constructed or a clustering algorithm is used to analyze the distances, angles and topological connections between the multi-view cultural relic image feature points to determine their spatial distribution patterns and local structures. Then, through geometric consistency checks, such as evaluating whether the multi-view cultural relic image feature points are collinear, coplanar or form a specific shape, the spatial relationship of the multi-view cultural relic image feature points is further verified and optimized.

[0070] S2.5. The contour recognition algorithm is used to identify the contour of the cultural relic based on the spatial relationship of the feature points of the multi-view cultural relic image, and finally the contour information of the cultural relic is obtained.

[0071] Specifically, first, the feature points of multi-view cultural relic images are converted into a dense point cloud model using 3D reconstruction technology to ensure the accurate position of these multi-view cultural relic image feature points in space. Then, clustering or segmentation algorithms are applied to process the point cloud to separate different areas representing the surface of cultural relics. Then, edge detection or curvature analysis and other methods are used to extract potential contour lines from the point cloud. These contour lines mark the boundaries between different surfaces. Subsequently, by connecting adjacent contour points and applying smoothing algorithms to eliminate noise and discontinuities, a continuous and smooth contour curve is constructed. Finally, information from all perspectives is integrated, and the contour is checked and corrected using geometric consistency constraints to ensure its integrity and accuracy, thereby ultimately obtaining contour information that accurately describes the appearance of the cultural relic.

[0072] S3, based on the contour information of the cultural relics, through the simulated annealing algorithm.

[0073] S3.1. Based on the adaptability requirements of the contour information of the cultural relics, the current rotation path and angle increment of the automatic turntable are generated through a random generation method.

[0074] It should be noted that adaptability refers to the customization of the rotation path and angle increment of the automatic turntable according to the specific shape and contour information of the cultural relic to ensure comprehensive coverage and detail capture during the scanning process;

[0075] The need for adaptability of the contour information of cultural relics comes from the process of high-precision 3D scanning and digital reconstruction of cultural relics. In order to ensure that the geometric features and surface details of cultural relics can be fully and accurately captured, the rotation path and angle increment of the automatic turntable need to be customized according to the specific contour information of cultural relics, as each cultural relic has different shapes, sizes and complexities, so as to optimize the scanning path and avoid missing or repeated scanning, thereby ensuring the integrity and accuracy of data collection;

[0076] It should also be noted that the key areas and complexity of the scan are determined based on the three-dimensional contour data of the cultural relics. On the premise of meeting the requirements of comprehensive coverage and detail capture, a randomization algorithm is used to generate a series of possible rotation paths and angle increment combinations based on a predefined parameter range (such as maximum rotation angle, minimum step angle).

[0077] S3.2. Based on the contour information of the cultural relic, the contour information is mapped to the two-dimensional image plane through the projection matrix to generate corresponding pixel points of the contour of the cultural relic.

[0078] Specifically, first, based on the known camera intrinsic parameters (such as focal length, principal point coordinates) and extrinsic parameters (such as rotation and translation vectors), an accurate projection matrix is ​​constructed. Then, the projection matrix is ​​used to map the contour points of the cultural relics in the three-dimensional space to the two-dimensional image plane one by one, and the corresponding pixel coordinates of each three-dimensional point in the image are calculated. The mapped pixel coordinates are then quantized to ensure that they fall on integer pixel positions, and non-integer coordinates are processed using interpolation methods to maintain the continuity of the contour.

[0079] S3.3. Calculate the color grayscale value of each pixel point of the cultural relic outline by combining the illumination direction, viewing angle distribution and material characteristics of the cultural relic, and obtain the image intensity value of the pixel point of the cultural relic outline.

[0080] Specifically, first, according to the known illumination model (such as the Phong reflection model) and the position of the light source, the direction and intensity of the incident light at each contour pixel are determined. Then, considering the normal direction of the cultural relic surface at different viewing angles, the angle between the light and the surface is calculated to evaluate the impact of direct illumination on each pixel. Then, combined with the material properties of the cultural relic (such as diffuse reflection coefficient, specular reflection coefficient and glossiness), BRDF (bidirectional reflection distribution function) or similar models are applied to simulate the illumination interaction effect, and the color and brightness contribution of each pixel is accurately calculated. In addition, considering the influence of shadow occlusion and ambient light, the calculation results are adjusted to more realistically reflect the illumination conditions. Finally, all these factors are combined, and a color grayscale value, i.e., image intensity value, is assigned to each pixel of the cultural relic outline through a rendering algorithm.

[0081] It should be noted that the lighting direction refers to the position and angle information of the light source relative to the cultural relic, and these data usually come from a pre-set lighting system configuration or are generated through computer simulation;

[0082] View distribution refers to the viewpoint information when the camera shoots cultural relics at different positions and angles. These data usually come from the predefined shooting path of the multi-view imaging system or the rotation parameters of the automatic turntable.

[0083] S3.4. Based on the current rotation path and angle increment of the automatic turntable, combined with the image intensity value of the pixel points of the cultural relic outline, calculate the error value between the current rotation path and angle increment and the cultural relic outline. The expression is:

[0084]

[0085] Among them, F(θ,φ) represents the error value between the current rotation path θ and the angle increment φ and the shape of the cultural relic, I i (θ, φ) represents the image intensity value of the pixel point of the i-th artifact outline under the current rotation path θ and angle increment φ. It represents the ideal target image intensity value of the i-th pixel point of the cultural relic outline under the current rotation path θ and angle increment φ, dθ represents the small angle increment of the current rotation path θ, i represents the index variable of the pixel point of the cultural relic outline, and N represents the number of pixel points of the cultural relic outline.

[0086] It should be noted that the ideal target image intensity value is the image intensity that the pixel points of the cultural relic outline should theoretically have under a given rotation path and angle increment, taking into account the position of the light source, the material properties of the cultural relic (such as reflectivity and color), and the observation angle. This process involves the use of an illumination model (such as the Phong model or a more complex global illumination model) to accurately simulate the interaction between light and the surface of the cultural relic, including direct illumination, shadows, reflections, and refractions. By comprehensively considering these factors, an ideal grayscale or color value, that is, its image intensity value, can be calculated for each pixel, which is used to represent the brightness level that the pixel should present under specific conditions.

[0087] S4. Obtain the optimal rotation path and optimal angle increment of the automatic turntable.

[0088] S4.1. Define the error threshold R by analyzing the appearance characteristics of historical relics and the distribution of historical image intensity values.

[0089] It should be noted that, first, a large number of representative three-dimensional models of historical cultural relics and their corresponding multi-view image data sets are collected and organized. The multi-view image data sets should cover different types of cultural relics and diverse lighting conditions. Then, the shape contour features of these cultural relics are extracted, and the distribution of their image intensity values ​​(grayscale or color) is statistically analyzed to identify typical patterns and variation ranges. Then, statistical analysis methods (such as mean, standard deviation calculation or histogram analysis) are used to evaluate the central trend and dispersion of the historical image intensity values, and a reasonable error range is determined. On this basis, considering the accuracy requirements and allowable deviations in practical applications, an error threshold is set that can accommodate normal changes and effectively distinguish abnormal situations. Finally, the effectiveness of the threshold is verified through cross-validation or a test set to ensure that it can accurately judge the rationality of the scanning path optimization results in the three-dimensional reconstruction process of new cultural relics.

[0090] S4.2, based on the error value F(θ,φ) between the current rotation path θ and the angle increment φ and the shape of the cultural relic and the error threshold R, evaluate the optimal rotation path and the optimal angle increment;

[0091] When F(θ,φ)>R, it means that the current rotation path and angle increment do not meet the standard and need to be further adjusted and optimized;

[0092] When F(θ,φ)≤R, it means that the current rotation path and angle increment are the optimal rotation path and the optimal angle increment.

[0093] S5. Based on the optimal rotation path and optimal angle increment of the automatic turntable, the cultural relics are scanned by a structured light scanner to obtain preliminary three-dimensional point cloud data.

[0094] S5.1. Based on the optimal rotation path of the automatic turntable, sub-path division is performed through the discretization path method.

[0095] It should be noted that, firstly, according to the calculated optimal rotation path and optimal angle increment, the continuous rotation motion is decomposed into a series of discrete rotation steps, each step corresponding to a specific angle change, and then according to the geometric complexity of the cultural relics and the scanning accuracy requirements, the appropriate step length is determined to ensure that each sub-path can cover sufficient surface area without missing any details, and then a mathematical algorithm (such as equal division or adaptive segmentation) is applied to evenly or unevenly divide the entire rotation path to generate a number of sub-path segments, each of which represents the rotation movement of the turntable within a certain interval.

[0096] S5.2. The structured light scanner is based on the divided sub-paths and combined with the optimal angle increment to scan the surface of the cultural relic in steps by projecting a regular grating grid to obtain a grating pattern.

[0097] It should be noted that, first, according to the divided sub-paths and the optimal angle increment setting, the structured light scanner accurately adjusts its projection angle at each specified position. Then, at each sub-path position, the scanner projects a series of regular grating patterns (such as stripes or dots) of known geometric shapes onto the surface of the cultural relic. These gratings deform along with the surface contour, and then the synchronous camera captures the deformed grating image modulated by the surface of the cultural relic, recording the interaction between the grating pattern and the surface morphology.

[0098] S5.3. Based on the grating pattern and combined with the calibration parameters of the structured light scanner, the three-dimensional coordinates of the surface of the cultural relic are analyzed through triangulation to obtain local three-dimensional point cloud data.

[0099] It should be noted that, based on the grating pattern and combined with the calibration parameters of the structured light scanner, an accurate camera model is first established, using known internal parameters (such as focal length, principal point position) and external parameters (such as rotation and translation matrices); then, when the scanner projects a regular grating pattern onto the surface of the cultural relic, the camera captures the deformed grating image, matches the original and deformed gratings through a pattern recognition algorithm, and determines the offset of the grating line; then, through the principle of triangulation, the offset of the grating line is used to determine the position of the cultural relic surface point relative to the camera, and combined with the known baseline distance and relative angle between the camera and the projector, the depth information of the point is calculated using geometric relationships, and then the depth information is converted into three-dimensional coordinates according to the camera's internal parameter matrix; finally, all the calculated three-dimensional coordinate points are integrated to form a three-dimensional point cloud data set that describes the local surface morphology of the cultural relic;

[0100] It should also be noted that the depth information refers to the vertical distance from the camera to each point on the surface of the scanned object, that is, the distance value of each point along the optical axis of the camera.

[0101] S5.4. Use the iterative closest point algorithm to stitch the local three-dimensional point cloud data, and detect the surface normal distribution and point cloud density to obtain preliminary three-dimensional point cloud data.

[0102] It should be noted that the iterative closest point (ICP) algorithm is used to gradually align and stitch each local 3D point cloud data, and the relative positions of each part are adjusted by minimizing the distance between corresponding points to ensure that they are accurately matched in a unified spatial coordinate system; then, the geometric continuity is checked by analyzing the surface normal distribution of the stitched point cloud, and the point cloud density is evaluated to identify possible sparse or overlapping areas, thereby detecting and correcting stitching errors, and finally obtaining complete and uniform preliminary 3D point cloud data.

[0103] S6. Based on the preliminary three-dimensional point cloud data, it is processed through the regional hierarchical optimization algorithm and the topological structure reconstruction method to obtain high-precision three-dimensional point cloud data.

[0104] S6.1. Based on the preliminary 3D point cloud data, regional stratification is performed using a 3D density clustering algorithm.

[0105] It should be noted that, first, a suitable density clustering algorithm (such as DBSCAN or OPTICS) is selected, and the distance threshold and minimum number of points are set according to the spatial distribution of the point cloud data to define the "core point" and its neighborhood. Then, the density clustering algorithm (such as DBSCAN or OPTICS) traverses all points, identifies the core points and expands them to form high-density areas, and marks the boundary points and noise points at the same time. Then, according to the connectivity of different density areas, the point cloud is automatically segmented into multiple hierarchical clusters, each cluster represents a specific area or feature on the surface of the cultural relic. Then, the geometric characteristics of each cluster, such as size, shape and normal direction, are analyzed to further refine the stratification results to ensure that each stratified area has similar density and surface properties. Finally, a unique identifier is assigned to each stratified area, and its spatial position and topological relationship are recorded to form an orderly regional hierarchical structure.

[0106] It should also be noted that connectivity refers to the spatial connection relationship between different points or clusters in the point cloud data, that is, whether the points can be connected to each other through a series of neighboring points within a certain distance threshold (such as Epsilon, ε in the DBSCAN algorithm) and thus belong to the same density area or cluster.

[0107] S6.2, based on each stratified region, perform stratified optimization through the regional stratified optimization algorithm, and calculate the optimized scalar value of the stratified region, the expression is:

[0108]

[0109] Among them, E(R k ) is the optimized scalar value of the kth stratified region, R k is the kth stratified region, n u is the normal direction of the u-th 3D point cloud data point in the hierarchical region, λ is the weight parameter of the smoothing term, Q(u) is the neighborhood point set of the u-th 3D point cloud data point in the hierarchical region, and w uj is the similarity value between the u-th 3D point cloud data point in the hierarchical region and the j-th neighborhood 3D point cloud data point in the hierarchical region, p u is the u-th 3D point cloud data point in the layered area, h j is the jth neighborhood 3D point cloud data point in the hierarchical region, k is the index variable of the hierarchical region, u is the index variable of the 3D point cloud data point in the hierarchical region, and j is the index variable of the neighborhood 3D point cloud data point in the hierarchical region.

[0110] It should be noted that w uj The similarity is quantified by evaluating the Euclidean distance, normal direction consistency, and similarity of surface attributes such as color or reflectivity between 3D point cloud data points in the layered area. In some cases, more complex feature descriptors (such as FPFH-FastPointFeature Histograms) are used to capture local shape features, and the similarity values ​​are determined by comparing these descriptors.

[0111] S6.3. Based on the optimized scalar value of the stratified area, a numerical optimization algorithm is used to perform minimization processing to obtain the stratified area after stratification optimization.

[0112] It should be noted that, first, an objective function is defined, which combines the surface normal consistency and smoothness term of each layered region to quantify the quality of the layered region. Then, a numerical optimization algorithm (such as gradient descent or conjugate gradient method) is applied to iteratively adjust the position of the midpoints in the layered region to minimize the objective function value, that is, to optimize the scalar value E(R k ), in each iteration, the algorithm calculates the gradient according to the current point cloud data and its neighborhood relationship, and updates the position of the point in the direction of reducing the objective function. At the same time, a smooth constraint is introduced to maintain the overall continuity and smoothness of the stratified area to avoid overfitting. Finally, when the objective function converges to the minimum value or reaches the preset number of iterations, the optimization is stopped to obtain the optimized stratified area.

[0113] S6.4, based on the layered optimization, the layered regions are integrated into the global coordinate system through stitching and alignment algorithms, and the boundary points of each region are identified.

[0114] It should be noted that the point cloud data of each hierarchical area is accurately aligned to a unified global coordinate system using a global registration algorithm (such as ICP or feature-based registration), ensuring the geometric consistency between different areas, analyzing the spatial relationship of each hierarchical area, identifying and marking the junctions between adjacent areas, and determining the location of boundary points.

[0115] S6.5. By using the constrained Laplace smoothing method, the connection relationship between the regional boundary points is adjusted to obtain the global topological structure.

[0116] It should be noted that, first, the Laplace operator is defined to quantify the average position difference between each boundary point and its neighboring points, and constraints are introduced to maintain the original geometric features and boundary continuity. Then, the optimization algorithm is applied to iteratively update the position of the boundary points, ensuring that the constraints are met while minimizing the Laplace energy, so as to smooth the boundary without distorting the overall shape. Then, possible non-ideal connections (such as intersections or overlaps) are checked and corrected to ensure the topological correctness between regions. Finally, all optimized boundary points are integrated to construct a coherent and stable global topological structure.

[0117] S6.6. Based on the global topological structure, the hierarchical area after hierarchical optimization is mapped to the global topological structure through a rigid transformation matrix to obtain high-precision three-dimensional point cloud data.

[0118] It should be noted that, first, according to the spatial relationship and boundary point positions of each hierarchical area defined by the global topological structure, the rigid transformation matrix (including rotation and translation) of each hierarchical area relative to the global coordinate system is calculated, and then these rigid transformation matrices are applied to accurately reposition the point cloud data after hierarchical optimization to ensure the correct placement of each hierarchical area in the global coordinate system. Then, through consistency checks and fine-tuning, possible alignment errors are eliminated to ensure seamless connection between all hierarchical areas. Finally, all converted hierarchical area point cloud data are integrated to form a unified, coherent and high-precision three-dimensional point cloud data.

[0119] S7. Based on high-precision 3D point cloud data, feature extraction algorithms are used to identify important structures of cultural relics and generate 3D digital models of cultural relics.

[0120] S7.1. Based on high-precision three-dimensional point cloud data, the structure of cultural relics is analyzed through feature extraction algorithms to obtain the geometric features of cultural relics.

[0121] It should be noted that, firstly, geometric analysis algorithms (such as curvature estimation and normal calculation) are applied to analyze whether the geometric properties are significantly different from the surrounding areas under specific thresholds or conditions. For example, curvature estimation can find the edges of mutations, and normal calculation can help identify the boundaries between smooth areas and non-smooth areas, thereby distinguishing the parts that are crucial to understanding the structure of cultural relics, and identifying the key geometric properties in the point cloud to determine the smooth areas and characteristic edges of the surface. Then, segmentation algorithms are used to divide the point cloud into different structural parts, such as basic geometric elements such as planes, cylinders or free-form surfaces. Then, topological analysis is used to detect and extract important structural features, such as holes, grooves, protrusions, etc., while identifying symmetry and repeated patterns.

[0122] It should also be noted that key geometric attributes refer to the attributes in the point cloud data that reflect the surface characteristics of cultural relics, such as curvature, normal direction and smoothness. These attributes can help identify the structural characteristics and irregularities of the surface.

[0123] S7.2. Based on the geometric features of the cultural relics, the high-precision three-dimensional point cloud data is segmented through a clustering algorithm, and the surface structure areas of the cultural relics are identified.

[0124] It should be noted that the point cloud data is annotated according to the extracted geometric features (such as curvature and normal direction), and a clustering algorithm (such as K-means, DBSCAN or hierarchical clustering) is applied to segment the point cloud into multiple clusters with similar attributes based on these geometric features. Each cluster represents a specific structural area on the surface of the cultural relic.

[0125] S7.3. Using the Poisson reconstruction method, triangulated meshing is performed based on the identified surface structure area of ​​the cultural relic to obtain a three-dimensional digital model of the cultural relic.

[0126] It should be noted that, first, based on the identified surface structure area, an indicator function is constructed to describe the density distribution and boundary of the point cloud data. Then, the Poisson equation solver is applied to interpolate these indicator functions in three-dimensional space to generate a smooth scalar field whose gradient is approximately the normal direction of the original point cloud. Then, the isosurface extraction algorithm (such as MarchingCubes) is used to extract the zero isosurface from the scalar field to form a closed and continuous triangular mesh, ensuring that the mesh faithfully reflects the geometric features and details of the cultural relics. Finally, the generated triangular mesh is optimized, including removing noise, filling holes and smoothing the surface, so as to obtain a high-quality three-dimensional digital model of the cultural relic.

[0127] This embodiment also provides a computer device, which is suitable for the case of a three-dimensional digital modeling method for cultural relics based on the automatic turntable rotating structured light, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the three-dimensional digital modeling method for cultural relics based on the automatic turntable rotating structured light proposed in the above embodiment.

[0128] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0129] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for three-dimensional digital modeling of cultural relics based on automatic turntable rotating structured light proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0130] In summary, the present invention achieves adaptive adjustment of the cultural relic scanning process by: optimizing the rotation path and angle increment of the automatic turntable using a simulated annealing algorithm, ensuring comprehensive capture of the surface details of the cultural relic; at the same time, processing the three-dimensional point cloud data in combination with a regional layered optimization algorithm improves the accuracy and processing efficiency of the point cloud data, and ultimately generates a high-precision three-dimensional digital model. This method effectively solves the problems of incomplete scanning and inefficient data processing in the prior art, significantly improves the accuracy and work efficiency of three-dimensional modeling of cultural relics, and is particularly suitable for the fine reconstruction of complex-shaped cultural relics.

[0131] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of a three-dimensional digital modeling method of cultural relics based on an automatic turntable rotating structured light are provided.

[0132] In order to verify the effectiveness of the three-dimensional digital modeling method of cultural relics based on automatic turntable rotating structured light, an ancient bronze ware with complex geometric shape and fine texture features was selected as the experimental object.

[0133] First, in order to verify the effectiveness of the three-dimensional digital modeling method of cultural relics based on automatic turntable rotating structured light, an ancient bronze with complex geometric shapes and fine texture features was selected as the experimental object. Comprehensive material property data of this bronze was collected, including its texture characteristics, gloss characteristics and spectral characteristics, and these data were denoised, normalized and aligned through advanced data processing technology to ensure the data quality of subsequent steps. At the same time, the bronze was captured from multiple perspectives using a stereoscopic vision imaging system, and these multi-perspective images were accurately aligned and registered through feature matching algorithms.

[0134] Secondly, the corner detection algorithm was used to extract the feature points in each image, and the shape and contour information of the bronze artifacts was identified based on the spatial relationship of these feature points, providing a solid foundation for the subsequent scanning path planning. Then, the simulated annealing algorithm was used to calculate the optimal rotation path and angle increment of the automatic turntable, taking into account factors such as the shape and contour information of the cultural relics, the direction of illumination, the distribution of viewing angles, and the characteristics of the material. After multiple iterations of optimization, a rotation path with the minimum error value was finally determined, ensuring that each scanning position can obtain the ideal image intensity value, improving the scanning efficiency and accuracy.

[0135] Then, according to the calculated optimal rotation path and angle increment, a high-precision structured light scanner was used to perform a detailed step-by-step scan of the bronze artifact. The entire scanning process is divided into 36 sub-paths, each of which corresponds to a specific angle increment. The structured light scanner projects a regular grating grid onto the surface of the bronze artifact, and records the three-dimensional coordinates of each grating pattern after deformation through triangulation. Subsequently, the iterative closest point (ICP) algorithm was used to seamlessly splice all local three-dimensional point cloud data to form a preliminary three-dimensional point cloud model, and the preliminary point cloud data was regionally optimized and topologically reconstructed to improve the quality of the model.

[0136] Finally, the point cloud was segmented into several hierarchical regions by a three-dimensional density clustering algorithm, and a special optimization algorithm was applied to each region to adjust the position of the boundary points to ensure the continuity and consistency of the global topological structure. Based on the optimized high-precision three-dimensional point cloud data, the important structure of the bronze ware was identified by a feature extraction algorithm, and the three-dimensional digital model of the cultural relic was constructed using the Poisson reconstruction method. Through a detailed analysis of the generated model, it was proved that the method of the present invention is significantly superior to the traditional method in terms of scanning speed, point cloud density, surface detail retention and error rate, demonstrating the innovation and superiority of this method in the field of three-dimensional digital modeling of cultural relics.

[0137] Traditional method 1: refers to the method of using standard scanning equipment and conventional parameter settings to perform 3D modeling of cultural relics.

[0138] Traditional method 2: refers to the method of using different equipment or optimized traditional parameter settings to perform 3D modeling of cultural relics, which may be suitable for more complex or specific types of cultural relics.

[0139] The details are shown in Table 1 below:

[0140] Table 1 Comparison of performance of 3D modeling of cultural relics

[0141]

[0142] By analyzing the data in the above table, it can be clearly seen that the method of the present invention has significant advantages over the traditional method in multiple key performance indicators: the average scanning time is shortened by about 20%-22%, the point cloud density is increased by about 15%-20%, the surface detail retention is increased by about 5%-6%, and the error rate is reduced by more than 50%. These improvements not only greatly improve the efficiency and accuracy of 3D modeling, but also provide higher quality technical support for the protection, display and research of cultural relics.

[0143] The present invention significantly improves the efficiency and accuracy of three-dimensional digital modeling of cultural relics. By optimizing the scanning path and increasing the point cloud density, the average scanning time is shortened by about 20%-22%, the point cloud density is increased by about 15%-20%, the surface detail retention is improved by about 5%-6%, and the error rate is reduced by more than 50%, thereby generating a more detailed, accurate and reliable three-dimensional model, providing high-quality technical support for the protection, display and research of cultural relics.

[0144] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A three-dimensional digital modeling method for cultural relics based on automatic turntable rotating structured light, characterized by: include, Collecting and preprocessing the material characteristic data of cultural relics; Based on the pre-processed material characteristic data of cultural relics, the outline information of cultural relics is obtained through stereoscopic vision imaging method and shape recognition algorithm; Based on the contour information of the cultural relics, the optimal rotation path and optimal angle increment of the automatic turntable are obtained through the simulated annealing algorithm; Based on the optimal rotation path and optimal angle increment of the automatic turntable, the cultural relics are scanned by a structured light scanner to obtain preliminary 3D point cloud data; Based on the preliminary 3D point cloud data, the high-precision 3D point cloud data is obtained by processing it through the regional hierarchical optimization algorithm and the topological structure reconstruction method; Based on high-precision three-dimensional point cloud data, feature extraction algorithms are used to identify important structures of cultural relics and generate three-dimensional digital models of cultural relics.

2. The method for three-dimensional digital modeling of cultural relics based on automatic turntable rotation structured light according to claim 1, characterized in that: The material characteristic data of the cultural relics include texture characteristics, gloss characteristics and spectral characteristics; The preprocessing of the cultural relic material characteristic data includes data denoising, data normalization and data alignment.

3. The method for three-dimensional digital modeling of cultural relics based on automatic turntable rotating structured light according to claim 2, characterized in that: The method of obtaining the contour information of the cultural relics through a stereoscopic imaging method and a shape recognition algorithm based on the pre-processed cultural relics material characteristic data is as follows: Based on the pre-processed material characteristic data of cultural relics, multi-view collection of cultural relics is performed through stereoscopic visual imaging method to obtain multi-view cultural relic images; Through the feature matching algorithm, the collected multi-view cultural relic images are aligned and registered; Based on the aligned and registered multi-view cultural relic images, feature points of the multi-view cultural relic images are extracted through the corner detection algorithm; Based on the positions and relative relations of the feature points of the multi-view cultural relic images in three-dimensional space, the spatial relations of the feature points of the multi-view cultural relic images are obtained; The contour recognition algorithm is used to identify the shape of the cultural relic based on the spatial relationship of the feature points of the multi-view cultural relic images, and finally the shape contour information of the cultural relic is obtained.

4. The method for three-dimensional digital modeling of cultural relics based on automatic turntable rotation structured light according to claim 3, characterized in that: Based on the contour information of the cultural relics, the specific steps are as follows through the simulated annealing algorithm: Based on the adaptability requirements of the cultural relic outline information, the current rotation path and angle increment of the automatic turntable are generated through a random generation method; Based on the contour information of the cultural relic, the projection matrix is ​​used to map it to the two-dimensional image plane to generate the corresponding pixel points of the cultural relic contour; Combining the illumination direction, viewing angle distribution and material characteristics of the cultural relic, the color grayscale value of each pixel point of the cultural relic outline is calculated to obtain the image intensity value of the pixel point of the cultural relic outline; Based on the current rotation path and angle increment of the automatic turntable, combined with the image intensity value of the pixel points of the cultural relic outline, the error value between the current rotation path and angle increment and the cultural relic outline is calculated, and the expression is: Where F(θ, φ) represents the error between the current rotation path θ and the angle increment φ and the shape of the cultural relic, I i (θ, φ) represents the image intensity value of the pixel point of the i-th artifact outline under the current rotation path θ and angle increment φ. It represents the ideal target image intensity value of the i-th pixel point of the cultural relic outline under the current rotation path θ and angle increment φ, dθ represents the small angle increment of the current rotation path θ, i represents the index variable of the pixel point of the cultural relic outline, and N represents the number of pixel points of the cultural relic outline.

5. The method for three-dimensional digital modeling of cultural relics based on automatic turntable rotation structured light according to claim 4, characterized in that: The specific steps of obtaining the optimal rotation path and optimal angle increment of the automatic turntable are as follows: By analyzing the appearance characteristics of historical relics and the distribution of historical image intensity values, the error threshold R is defined; Based on the error value F(θ, φ) between the current rotation path θ and the angle increment φ and the shape of the cultural relic and the error threshold R, the optimal rotation path and the optimal angle increment are evaluated; When F(θ, φ)>R, it means that the current rotation path and angle increment do not meet the standard and need to be further adjusted and optimized; When F(θ, φ)≤R, it indicates that the current rotation path and angle increment are the optimal rotation path and the optimal angle increment.

6. The method for three-dimensional digital modeling of cultural relics based on automatic turntable rotation structured light according to claim 5, characterized in that: The optimal rotation path and optimal angle increment based on the automatic turntable are used to scan the cultural relics through a structured light scanner to obtain preliminary three-dimensional point cloud data. The specific steps are as follows: Based on the optimal rotation path of the automatic turntable, the sub-path is divided by the discretized path method; The structured light scanner scans the surface of the cultural relic in steps by projecting a regular grating grid based on the divided sub-paths and combined with the optimal angle increment to obtain a grating pattern; Based on the grating pattern and combined with the calibration parameters of the structured light scanner, the three-dimensional coordinates of the surface of the cultural relic are analyzed through triangulation to obtain local three-dimensional point cloud data; The local 3D point cloud data are spliced ​​using the iterative closest point algorithm, and the surface normal distribution and point cloud density are detected to obtain preliminary 3D point cloud data.

7. The method for three-dimensional digital modeling of cultural relics based on automatic turntable rotation structured light according to claim 6, characterized in that: The preliminary three-dimensional point cloud data is processed by a regional hierarchical optimization algorithm and a topological structure reconstruction method to obtain high-precision three-dimensional point cloud data. The specific steps are as follows: Based on the preliminary 3D point cloud data, regional stratification is performed using a 3D density clustering algorithm; Based on each stratified region, stratified optimization is performed through the regional stratified optimization algorithm to calculate the optimized scalar value of the stratified region. The expression is: Where E(Rk) is the optimized scalar value of the kth stratified region, Rk is the kth stratified region, and n u is the normal direction of the u-th 3D point cloud data point in the hierarchical region, λ is the weight parameter of the smoothing term, Q(u) is the neighborhood point set of the u-th 3D point cloud data point in the hierarchical region, and w uj is the similarity value between the u-th 3D point cloud data point in the hierarchical region and the j-th neighborhood 3D point cloud data point in the hierarchical region, p u is the u-th 3D point cloud data point in the layered area, h j is the jth neighborhood 3D point cloud data point in the stratified region, k is the index variable of the stratified region, u is the index variable of the stratified region 3D point cloud data point, and j is the index variable of the stratified region neighborhood 3D point cloud data point; Based on the optimized scalar value of the stratified area, a numerical optimization algorithm is used to perform minimization processing to obtain the stratified area after stratification optimization; The layered regions after layered optimization are integrated into the global coordinate system through stitching and alignment algorithms, and the boundary points of each region are identified; By using the constrained Laplace smoothing method, the connection relationship of the regional boundary points is adjusted to obtain the global topological structure; Based on the global topological structure, the hierarchical area after hierarchical optimization is mapped to the global topological structure through a rigid transformation matrix to obtain high-precision three-dimensional point cloud data.

8. The method for three-dimensional digital modeling of cultural relics based on automatic turntable rotation structured light according to claim 7, characterized in that: Based on high-precision three-dimensional point cloud data, the important structures of cultural relics are identified through feature extraction algorithms to generate three-dimensional digital models of cultural relics. The specific steps are as follows: Based on high-precision 3D point cloud data, the structure of cultural relics is analyzed through feature extraction algorithms to obtain the geometric features of cultural relics; Based on the geometric features of the cultural relics, the high-precision 3D point cloud data is segmented through a clustering algorithm, and the surface structure areas of the cultural relics are identified; Through the Poisson reconstruction method, triangulated meshing is performed based on the identified surface structure area of ​​the cultural relic to obtain a three-dimensional digital model of the cultural relic.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for three-dimensional digital modeling of cultural relics based on automatic turntable rotation structured light are implemented as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for three-dimensional digital modeling of cultural relics based on automatic turntable rotation structured light are implemented as described in any one of claims 1 to 8.

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