A digital model storage method for multi-material 3D printed dentures

Abstract

The present application relates to the technical field of digital model storage, and specifically to a digital model storage method for multi-material 3D printed dentures, the method comprising: determining a local complexity index based on the smoothness and complexity of each point cloud data and all point cloud data in its neighborhood at corresponding positions on a fitting surface, and determining neighborhood complexity in combination with the distance from each point cloud data and all point cloud data in its neighborhood to the fitting surface, and the distance from each point cloud data to all point cloud data in its neighborhood; determining a comprehensive complexity index of each point cloud data based on the angle between the normal vectors of each point cloud data and all point cloud data in its neighborhood on the fitting surface, and in combination with the neighborhood complexity, and storing the digital model of the denture. The present application aims to reduce the waste of resources in the digital model storage process of 3D printed dentures by analyzing the complexity of the digital model of the denture.

Description

Technical Field

[0001] The present application relates to the technical field of digital model storage, and in particular to a digital model storage method for multi-material 3D printed dentures. Background Art

[0002] As a major technological innovation in the field of stomatology, 3D printed dentures are gradually changing the traditional denture manufacturing process. This technology generates high-precision 3D models by scanning the patient's oral data, and uses 3D printing technology to directly manufacture dentures that perfectly match the patient's oral structure, realizing a fully digital production process from model scanning to finished product printing. Among them, in the production process of 3D printed dentures, the storage of digital models plays a vital role. The storage of digital models not only ensures the consistency and integrity of the data, but also provides important reference and adjustment basis for doctors in the subsequent treatment process.

[0003] With the increasing complexity of multi-material denture design and the increase in patient customization needs, the demand for digital model storage is also growing exponentially. Due to the complex internal structure of the oral cavity, the structure of the three-dimensional digital model generated by CAD software is also relatively complex, and the shape distribution of the body, surface, edge, and point in the digital model is neither regular nor balanced, making it difficult for the traditional ocTree compression algorithm to adapt to the characteristics of the 3D denture digital model and achieve efficient compression effects. Setting the point threshold too large or too small will lead to a decrease in data compression efficiency, which will cause a waste of resources in the storage process of the 3D printed denture digital model. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a digital model storage method for multi-material 3D printed dentures to solve the existing problems.

[0005] A digital model storage method for multi-material 3D printed dentures in this application adopts the following technical solution:

[0006] An embodiment of the present application provides a method for storing a digital model of a multi-material 3D printed denture, the method comprising the following steps:

[0007] Obtain 3D point cloud data of the patient's oral tissue and use it as a digital model of the denture;

[0008] Fitting each point cloud data and all point cloud data in its neighborhood in the digital denture model to obtain a fitting surface, and determining the local complexity of each point cloud data based on the smoothness and complexity of each point cloud data and all point cloud data in its neighborhood at the corresponding position on the fitting surface;

[0009] Based on the curvature of each point cloud data and all point cloud data in its neighborhood at corresponding positions on the fitting surface, determine the curvature probability distribution curve of each point cloud data, and determine the local sharpness of each point cloud data by analyzing the local sharpness of the curvature probability distribution curve, and combine the local complexity to determine the local complexity index of each point cloud data;

[0010] Based on the distance from each point cloud data and all point cloud data in its neighborhood to the fitting surface, and the distance from each point cloud data to all point cloud data in its neighborhood, determine the spatial complexity of each point cloud data, and determine the neighborhood complexity of each point cloud data in combination with the local complexity index;

[0011] Clustering each point cloud data and all point cloud data in its neighborhood, determining the structural complexity of each point cloud data based on the number of clusters obtained and the angle between the normal vectors of each point cloud data and all point cloud data in its neighborhood on the fitting surface, and determining the comprehensive complexity index of each point cloud data based on the neighborhood complexity;

[0012] Based on the comprehensive complexity index and in combination with a point cloud compression algorithm, the digital denture model is stored.

[0013] Preferably, the method for determining the local complexity of each point cloud data is:

[0014] The range and information entropy of the curvature of each point cloud data and all point cloud data in its neighborhood on the corresponding fitting surface are calculated respectively, and the product of the range and the information entropy is taken as the local complexity of each point cloud data.

[0015] Preferably, the method for determining the curvature probability distribution curve of each point cloud data is:

[0016] The curvature of each point cloud data and all point cloud data in its neighborhood on the fitting surface is calculated, and the histogram of the curvature of each point cloud data and all point cloud data in its neighborhood is statistically analyzed. The curve obtained after smoothing the histogram is used as the curvature probability distribution curve of each point cloud data.

[0017] Preferably, the method for determining the local sharpness of each point cloud data is:

[0018] Extract all peaks on the curvature probability distribution curve of each point cloud data, calculate the kurtosis of each peak on the curvature probability distribution curve and all elements in its neighborhood, and record it as the kurtosis of each peak on the curvature probability distribution curve of each point cloud data;

[0019] The average value of the kurtosis of all peaks on the curvature probability distribution curve of each point cloud data is taken as the local sharpness of each point cloud data.

[0020] Preferably, the expression of the local complexity index of each point cloud data is: ; In the formula, Represents the local complexity index of the i-th point cloud data; Represents the local complexity of the i-th point cloud data; Represents the local sharpness of the i-th point cloud data; Represents the number of all peaks on the curvature probability distribution curve of the i-th point cloud data.

[0021] Preferably, the method for determining the spatial complexity of each point cloud data is:

[0022] Calculate the mean value of the distance from each point cloud data and all point cloud data in its neighborhood to the fitting surface, and record it as the first distance mean value of each point cloud data;

[0023] Calculate the mean of the distances from each point cloud data to all point cloud data in its neighborhood, and record it as the second distance mean of each point cloud data;

[0024] The spatial complexity of each point cloud data is the ratio of the first distance mean to the second distance mean of each point cloud data.

[0025] Preferably, the neighborhood complexity of each point cloud data is the product of the local complexity index of each point cloud data and the spatial complexity.

[0026] Preferably, the expression of the structural complexity of each point cloud data is: ; In the formula, Represents the structural complexity of the i-th point cloud data; Represents the total number of clusters obtained after clustering the i-th point cloud data and all point cloud data in its neighborhood; Represents the angle between the normal vector of the i-th point cloud data and the j-th point cloud data in its neighborhood on the i-th point cloud data fitting surface; represents the number of all point cloud data in the neighborhood of the i-th point cloud data; norm() represents the normalization function.

[0027] Preferably, the expression of the comprehensive complexity index of each point cloud data is: ; In the formula, Represents the comprehensive complexity index of the i-th point cloud data; Represents the neighborhood complexity of the i-th point cloud data; represents the structural complexity of the i-th point cloud data; norm() represents the normalization function.

[0028] Preferably, storing the digital denture model includes:

[0029] The denture digital model is used as the input of the point cloud compression algorithm, and the normalized value of the mean comprehensive complexity index of all point cloud data in the denture digital model is calculated. If the normalized value is less than the preset termination recursive threshold, the compression of the denture digital model is stopped. Otherwise, the denture digital model is divided into multiple sub-cubes using the point cloud compression algorithm, and the normalized value of the mean comprehensive complexity index of all point cloud data in each sub-cube is further calculated. When the normalized value of the mean comprehensive complexity index is less than the preset termination recursive threshold, the sub-cubes are no longer divided to obtain the compressed denture digital model, which is stored locally.

[0030] This application has at least the following beneficial effects:

[0031] This application constructs local complexity by analyzing the smoothness and complexity of the point cloud data and all the point cloud data in its neighborhood at the corresponding positions on the fitting surface. The local complexity of the point cloud data can be evaluated, which helps to maintain the details of the complex area during the compression process and improve the compression efficiency. Furthermore, by analyzing the local sharpness of the point cloud data distribution and combining the local complexity, a local complexity index is constructed, which reflects the degree of spatial difference in the local area where the point cloud data is located, which helps to more accurately adjust the compression accuracy, thereby improving the compression efficiency and reducing the waste of resources in the storage process. Furthermore, by analyzing the distance between the point cloud data and the distance from the point cloud data to the fitting surface, a spatial complexity index is constructed. degree, which helps to more comprehensively evaluate the distribution of point cloud data, so as to determine the compression accuracy; further, in combination with the local complexity index, the neighborhood complexity is constructed, which helps to more accurately reflect the distribution of point clouds in the neighborhood of point cloud data, so as to accurately compress the accuracy; this application constructs structural complexity by analyzing the directional differences between point cloud data, which helps to evaluate the complexity of the area where the point cloud data is located; finally, based on the structural complexity and neighborhood complexity, the denture data smoothing coefficient is determined, which helps to more accurately reflect the local complexity of point cloud data, and the denture smoothing coefficient and point cloud compression algorithm are combined to store the denture digital model, which improves the compression efficiency and reduces the waste of resources in the storage process. This application improves the data compression efficiency and reduces the waste of resources in the storage process of the digital model of 3D printed dentures by analyzing the complexity of the denture digital model. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 A flowchart of a method for storing a digital model of a multi-material 3D printed denture provided in one embodiment of the present application;

[0034] Figure 2 A schematic diagram of a comprehensive complex index extraction process provided by an embodiment of the present application;

[0035] Figure 3 A schematic diagram of the denture digital model compression process provided in one embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the digital model storage method of a multi-material 3D printed denture proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0037] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0038] The specific scheme of the digital model storage method for multi-material 3D printed dentures provided by the present application is described in detail below with reference to the accompanying drawings.

[0039] An embodiment of the present application provides a method for storing a digital model of a multi-material 3D printed denture. Specifically, the following method for storing a digital model of a multi-material 3D printed denture is provided. Figure 1 , the method comprises the following steps:

[0040] Step S1: Acquire the three-dimensional point cloud data of the patient's oral tissue and use it as a digital model of the denture.

[0041] First, a 3D scanner is used to obtain 3D point cloud data of the patient's oral tissue. Due to the influence of environmental factors and the equipment itself, there is a large amount of irrelevant environmental noise and random noise in the obtained 3D data. Therefore, this embodiment uses a radius outlier denoising algorithm to denoise the obtained 3D point cloud data. Secondly, the denoised 3D point cloud data is used as a digital model of the denture and imported into the CAD software to design the shape and structure of the 3D printed denture.

[0042] Among them, the radius outlier denoising algorithm and the use of CAD software to design the shape structure of 3D printed dentures are both well-known technologies, and their specific principles and processes will not be repeated here.

[0043] Step S2: Fit each point cloud data and all point cloud data in its neighborhood in the digital denture model to obtain a fitting surface, and determine the local complexity of each point cloud data based on the smoothness and complexity of each point cloud data and all point cloud data in its neighborhood at the corresponding position on the fitting surface.

[0044] The digital model of 3D printed dentures refers to a virtual three-dimensional model of 3D dentures obtained through digital technology, which is essentially composed of a series of three-dimensional point cloud data. Since the geometric complexity of different areas in the digital model of 3D printed dentures is different, it is necessary to adjust the size of the neighborhood window according to the actual situation of the data points during the compression process. Specifically, for areas with more complex geometric shapes, a smaller neighborhood window is used to control the compression accuracy to ensure that key details are not lost during the compression process. Therefore, this embodiment further determines the compression accuracy by analyzing the geometric complexity of the local area of ​​each point in the digital model.

[0045] In the digital denture model, the more complex areas usually have obvious fluctuations, and the fluctuations of data points in the point cloud data usually correspond to obvious curvature changes. Therefore, by analyzing the smoothness and complexity of the surface surrounded by the point cloud data in the digital denture model and the point cloud data nearby, the complexity of the point cloud in different areas of the digital denture model is evaluated, specifically:

[0046] (1) The neighborhood is divided with each point cloud data in the digital denture model as the center. The specific process of dividing the neighborhood in this embodiment is as follows: taking the i-th point cloud data as an example, with the i-th point cloud data as the center, the k-nearest neighbor algorithm is used to obtain the point cloud data closest to the point cloud data i, and these point cloud data are used as the point cloud data in the neighborhood of the point cloud data i, wherein the number of nearest neighbor points in the k-nearest neighbor algorithm is set to 20. The setting of the number of nearest neighbor points is set by humans. The value in this embodiment is 20, and the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0047] Among them, the k-nearest neighbor algorithm is a well-known technology, and its specific principle will not be repeated here.

[0048] (2) Furthermore, each point cloud data in the denture digital model and all point cloud data in its neighborhood are fitted to obtain a fitting surface.

[0049] It should be noted that there are many commonly used surface fitting algorithms. In this embodiment, a three-dimensional spline surface fitting algorithm is used to fit each point cloud data and all point cloud data in its neighborhood. The implementer may also use other surface fitting methods such as polynomial surface fitting. Regarding the selection of surface fitting methods, this embodiment does not impose any special restrictions.

[0050] Among them, the three-dimensional spline surface fitting algorithm is a well-known technology in the field of surface fitting, and the specific process of fitting the three-dimensional data is not described in detail.

[0051] (3) Furthermore, the range and information entropy of the curvature of each point cloud data and all point cloud data in its neighborhood on the corresponding fitting surface are calculated respectively, and the product of the range and the information entropy is taken as the local complexity of each point cloud data.

[0052] Among them, the calculation steps of curvature, range and information entropy are all well-known technologies, and the specific calculation process will not be repeated here.

[0053] Furthermore, according to the local complexity of each point cloud data, it can be understood that when the point cloud data in the digital denture model is in a complex area, the greater the range and information entropy of the curvature of the point cloud data and all the point cloud data in its neighborhood on the corresponding fitting surface, the greater the local complexity obtained; conversely, if the point cloud data in the digital denture model is in a relatively simple area, the smaller the range and information entropy of the curvature of the point cloud data and all the point cloud data in its neighborhood on the corresponding fitting surface, the smaller the local complexity obtained.

[0054] Step S3: Based on the degree of curvature of each point cloud data and all point cloud data in its neighborhood at corresponding positions on the fitting surface, determine the curvature probability distribution curve of each point cloud data, and determine the local sharpness of each point cloud data by analyzing the local sharpness of the curvature probability distribution curve, and combine the local complexity to determine the local complexity index of each point cloud data.

[0055] In order to ensure a good fit between the denture and the oral tissue, the base of the denture is usually relatively flat, which makes the distribution of the point cloud data of the base relatively smooth, with small differences between each other. At the same time, in order to ensure a close fit between the denture and the adjacent teeth and a good fit between the occlusal surface of the upper and lower teeth, when designing and constructing the denture, the structure of the occlusal surface of the denture and the surface of the denture with the adjacent teeth is relatively complex, and the corresponding point cloud data shows a large difference in the curvature between the point cloud data on the corresponding fitting surface. Therefore, in order to further determine the compression accuracy, the local complexity index of each point cloud data is determined by analyzing the sharpness of the surrounding area where the point cloud data is located and combining the local complexity, which is as follows:

[0056] (1) Calculate the curvature of each point cloud data and all point cloud data in its neighborhood on the fitting surface, and statistically calculate the histogram of the curvature of each point cloud data and all point cloud data in its neighborhood. The curve obtained after smoothing the histogram is used as the curvature probability distribution curve of each point cloud data.

[0057] The processes of obtaining the histogram and smoothing are well-known techniques, and the specific principles and processes are not described in detail here.

[0058] (2) Further, all peaks on the curvature probability distribution curve of each point cloud data are extracted, and the kurtosis of each peak on the curvature probability distribution curve and all elements in its neighborhood are calculated, which is recorded as the kurtosis of each peak on the curvature probability distribution curve of each point cloud data;

[0059] It should be noted that there are many commonly used peak extraction algorithms. In this embodiment, an automatic multi-scale peak search algorithm is used to obtain all peaks on the curvature probability distribution curve. The implementer may also use other peak extraction algorithms such as the sliding window method. This embodiment does not impose any special restrictions on the selection of the peak extraction algorithm.

[0060] Among them, the automatic multi-scale peak search algorithm is a well-known technology, and the specific process of obtaining the peak value on the curve is not repeated here.

[0061] In addition, it should be noted that the size of the neighborhood of each peak is artificially set. In this embodiment, the size of the neighborhood of the peak is set to 100, that is, the sharpness of the peak is measured by analyzing the kurtosis of the peak and the 100 elements in its neighborhood. The implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0062] (3) Furthermore, the average kurtosis of all peaks on the curvature probability distribution curve of each point cloud data is taken as the local sharpness of each point cloud data.

[0063] (4) Furthermore, by analyzing the local sharpness of the curvature probability distribution curve, the local sharpness of each point cloud data is determined, and combined with the local complexity, the local complexity index of each point cloud data is determined, which is specifically:

[0064] The local complexity index of the i-th point cloud data The expression is: ; In the formula, Represents the local complexity of the i-th point cloud data; Represents the local sharpness of the i-th point cloud data; Represents the number of all peaks on the curvature probability distribution curve of the i-th point cloud data.

[0065] According to the local complexity index of each point cloud data, it can be understood that if the number of peaks on the curvature probability distribution curve of the point cloud data is more, the local complexity is greater, and the local sharpness is greater, then the calculated local complexity index is larger, indicating that the point cloud distribution in the neighborhood where the point cloud data is located is more complex; conversely, if the number of peaks on the curvature probability distribution curve of the point cloud data is smaller, the local complexity is smaller, and the local sharpness is smaller, then the calculated local complexity index is smaller, indicating that the point cloud distribution in the neighborhood where the point cloud data is located is smoother and simpler.

[0066] Step S4: Based on the distance from each point cloud data and all point cloud data in its neighborhood to the fitting surface, as well as the distance from each point cloud data to all point cloud data in its neighborhood, determine the spatial complexity of each point cloud data, and determine the neighborhood complexity of each point cloud data in combination with the local complexity index.

[0067] In order to ensure the accuracy of the denture digital model, complex or irregular areas usually require more point cloud data to represent the local details of the denture. The density of point cloud data in complex or irregular areas is relatively high, while the density of point cloud data in simple or flat areas is relatively low. Therefore, in order to measure the complexity of the point cloud data distribution in the denture digital model, the compression accuracy is further determined as follows:

[0068] (1) Calculate the mean distance between each point cloud data and all point cloud data in its neighborhood and the fitting surface, and record it as the first distance mean of each point cloud data;

[0069] (2) Further, the mean value of the distance from each point cloud data to all point cloud data in its neighborhood is calculated, which is recorded as the second distance mean value of each point cloud data;

[0070] (3) Furthermore, the ratio of the first distance mean to the second distance mean of each point cloud data is taken as the spatial complexity of each point cloud data.

[0071] It should be understood that the calculation process of the distance from a point to a surface is a well-known technology, and its specific calculation steps are not repeated here.

[0072] In addition, it should be noted that there are many methods for measuring the distance between two points. In this embodiment, the distance between different point cloud data is measured by calculating the Euclidean distance from each point cloud data to all point cloud data in its neighborhood. The implementer may also use other methods for measuring the distance between two points, such as DTW distance. Regarding the selection of the method for measuring the distance between two points, this embodiment does not impose any special restrictions.

[0073] The calculation process of the Euclidean distance is a well-known technique, and its specific calculation principle will not be described in detail.

[0074] According to the spatial complexity of each point cloud data, it can be understood that the greater the distance from the point cloud data and all the point cloud data in its neighborhood to the fitting surface, the more complex the point cloud distribution in the neighborhood of the point cloud data is, and the greater the difference between the point cloud data and the corresponding fitting value on the fitting surface, that is, the greater the first distance mean; and the smaller the distance from the point cloud data to all the point cloud data in its neighborhood, that is, the smaller the second distance mean, the denser the point cloud distribution in the neighborhood of the point cloud data is, and the more complex the point cloud distribution in the neighborhood of the point cloud data is; therefore, the greater the first distance mean, the smaller the second distance mean, the higher the spatial complexity obtained, indicating that the point cloud distribution in the point cloud data and its neighborhood is more complex;

[0075] On the contrary, the smaller the distance from the point cloud data and all the point cloud data in its neighborhood to the fitting surface, the simpler the point cloud distribution in the neighborhood of the point cloud data, and the smaller the difference between the point cloud data and the corresponding fitting values ​​on the fitting surface, that is, the smaller the first distance mean; and the larger the distance from the point cloud data to all the point cloud data in its neighborhood, that is, the larger the second distance mean, the sparser the point cloud distribution in the neighborhood of the point cloud data, and the simpler the point cloud distribution in the neighborhood of the point cloud data; therefore, the smaller the first distance mean and the larger the second distance mean, the lower the spatial complexity obtained, indicating that the point cloud distribution in the point cloud data and its neighborhood is simpler.

[0076] (4) Based on the local complexity index and the spatial complexity, determine the neighborhood complexity of each point cloud data. Specifically, the neighborhood complexity of each point cloud data is the product of the local complexity index of each point cloud data and the spatial complexity.

[0077] According to the neighborhood complexity of each point cloud data, it can be understood that if the local complexity index of the point cloud data in the digital denture model is larger, it means that the point cloud distribution in the local area where the point cloud data is located is more complex, and the spatial complexity of the point cloud data is larger, it means that the point cloud distribution in the local area where the point cloud data is located is more complex, and the final neighborhood complexity is larger; conversely, if the local complexity index of the point cloud data in the digital denture model is smaller, it means that the point cloud distribution in the local area where the point cloud data is located is simpler, and the spatial complexity of the point cloud data is smaller, it means that the point cloud distribution in the local area where the point cloud data is located is simpler, and the final neighborhood complexity is smaller.

[0078] Step S5: Cluster each point cloud data and all point cloud data in its neighborhood, determine the structural complexity of each point cloud data based on the number of clusters obtained and the angle between the normal vectors of each point cloud data and all point cloud data in its neighborhood on the fitting surface, and determine the comprehensive complexity index of each point cloud data in combination with the neighborhood complexity.

[0079] If the point cloud data is located in a complex area in the digital model of the denture, such as a tooth cavity or the vertex area on the denture, the neighboring points of the point cloud data will be distributed on multiple planes, and the difference between the normal vectors of the point cloud data and the point cloud data in its neighborhood on the fitting surface will also be quite different. Therefore, the structural complexity is determined by analyzing the difference between the normal vectors of the point cloud data, and the comprehensive complexity index of each point cloud data is determined in combination with the neighborhood complexity. Specifically:

[0080] (1) Cluster each point cloud data and all point cloud data in its neighborhood to obtain multiple clusters, that is, each point cloud data corresponds to multiple clusters.

[0081] It should be noted that there are many commonly used clustering algorithms. In this embodiment, the AP clustering algorithm is used to cluster the point cloud data. The implementer may also use other clustering methods such as the k-means clustering algorithm or the DPC density mean clustering. Regarding the calculation and use of the clustering algorithm, this embodiment does not make any special restrictions.

[0082] Among them, the AP clustering algorithm is a well-known technology, and its specific clustering principle and process are not described in detail.

[0083] (2) Furthermore, based on the angle between the normal vectors of each point cloud data and all point cloud data in its neighborhood on the fitting surface, and combined with the number of clusters of each point cloud data, the structural complexity of each point cloud data is determined, specifically:

[0084] The structural complexity of the i-th point cloud data The expression is: ; In the formula, Represents the total number of clusters obtained after clustering the i-th point cloud data and all point cloud data in its neighborhood; Represents the angle between the normal vector of the i-th point cloud data and the j-th point cloud data in its neighborhood on the i-th point cloud data fitting surface; represents the number of all point cloud data in the neighborhood of the i-th point cloud data; norm() represents the normalization function.

[0085] According to the structural complexity of each point cloud data, it can be understood that when the point cloud data is located in a complex area of ​​the 3D denture digital model, the greater the difference between the normal vectors of the point cloud data and the point cloud data in its neighborhood on the fitting surface, that is, the larger the angle of the normal vectors between the point cloud data, and thus the greater the calculated structural complexity, and the more complex the local area is, the more clusters are obtained by clustering all the point cloud data in the neighborhood of the point cloud data, and therefore, the greater the calculated structural complexity is in the end; conversely, when the point cloud data is located in a relatively simple area of ​​the 3D denture digital model, the smaller the difference between the normal vectors of the point cloud data and the point cloud data in its neighborhood on the fitting surface, that is, the smaller the angle of the normal vectors between the point cloud data, and thus the smaller the calculated structural complexity is, and the simpler the local area is, the fewer the number of clusters are obtained by clustering all the point cloud data in the neighborhood of the point cloud data, and therefore, the smaller the calculated structural complexity is in the end.

[0086] (3) Based on the structural complexity and the neighborhood complexity, a comprehensive complexity index is determined, specifically:

[0087] The comprehensive complex index of the i-th point cloud data The expression is: ; In the formula, Represents the neighborhood complexity of the i-th point cloud data; represents the structural complexity of the i-th point cloud data; norm() represents the normalization function.

[0088] According to the comprehensive complexity index of each point cloud data, it can be understood that the greater the structural complexity, the greater the neighborhood complexity, and the larger the local smoothness coefficient of the denture obtained, indicating that the point cloud distribution in the area where the point cloud data is located is more complex, and the point cloud data is in a relatively complex structural area in the denture digital model; conversely, the smaller the structural complexity, the smaller the neighborhood complexity, and the smaller the local smoothness coefficient of the denture obtained, indicating that the point cloud distribution in the area where the point cloud data is located is simpler, and the point cloud data is in a relatively simple structural area in the denture digital model.

[0089] Preferably, the schematic diagram of the comprehensive complex index extraction process provided in this embodiment is as follows: Figure 2 shown.

[0090] Step S6: Based on the comprehensive complexity index and in combination with the point cloud compression algorithm, the denture digital model is stored.

[0091] Based on the above analysis and calculation, the comprehensive complexity index of each point cloud data in the denture digital model is obtained. , comprehensive complex index Considering the curvature distribution characteristics and spatial distribution characteristics of the local area where each point cloud data in the digital denture model is located, the comprehensive complexity index It can better reflect the local complexity of the denture digital model.

[0092] Furthermore, by combining the ocTree point cloud compression algorithm and the comprehensive complex index Compress the 3D denture digital model, specifically:

[0093] The denture digital model is used as the input of the ocTree octree algorithm, and the normalized value of the mean comprehensive complexity index of all point cloud data in the denture digital model is calculated. If the normalized value is less than the preset termination recursion threshold, the compression of the denture digital model is stopped. Otherwise, the denture digital model is divided into 8 sub-cubes using the point cloud compression algorithm, and the normalized value of the mean comprehensive complexity index of all point cloud data in each sub-cube is further calculated. If the normalized value of the mean comprehensive complexity index of the sub-cube is less than the preset termination recursion threshold, the division of the sub-cube is stopped and it is stored as compressed data. On the contrary, if the normalized value of the mean comprehensive complexity index of the sub-cube is greater than or equal to the preset termination recursion threshold, the sub-cube is further divided into 8 smaller sub-cubes until the normalized value of the mean comprehensive complexity index of all point cloud data in the sub-cube is less than the preset termination recursion threshold, so as to obtain the final compressed denture digital model and store it locally.

[0094] It should be noted that the value of the preset recursion termination threshold is manually set. In this embodiment, the value of the preset recursion termination threshold is 0.1. The implementer can also set it by himself according to the specific situation. This embodiment does not impose any special restrictions.

[0095] Among them, the principle of the ocTree octree algorithm is a well-known technology, and its specific principle process will not be repeated here.

[0096] Preferably, the schematic diagram of the denture digital model compression process provided in this embodiment is as follows: Figure 3 shown.

[0097] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0098] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0099] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A digital model storage method for multi-material 3D printed dentures, characterized in that: The method comprises the following steps: Obtain 3D point cloud data of the patient's oral tissue and use it as a digital model of the denture; Fitting each point cloud data and all point cloud data in its neighborhood in the digital denture model to obtain a fitting surface, and determining the local complexity of each point cloud data based on the smoothness and complexity of each point cloud data and all point cloud data in its neighborhood at the corresponding position on the fitting surface; Based on the curvature of each point cloud data and all point cloud data in its neighborhood at corresponding positions on the fitting surface, determine the curvature probability distribution curve of each point cloud data, and determine the local sharpness of each point cloud data by analyzing the local sharpness of the curvature probability distribution curve, and combine the local complexity to determine the local complexity index of each point cloud data; Based on the distance from each point cloud data and all point cloud data in its neighborhood to the fitting surface, and the distance from each point cloud data to all point cloud data in its neighborhood, determine the spatial complexity of each point cloud data, and determine the neighborhood complexity of each point cloud data in combination with the local complexity index; Clustering each point cloud data and all point cloud data in its neighborhood, determining the structural complexity of each point cloud data based on the number of clusters obtained and the angle between the normal vectors of each point cloud data and all point cloud data in its neighborhood on the fitting surface, and determining the comprehensive complexity index of each point cloud data based on the neighborhood complexity; Based on the comprehensive complexity index and in combination with a point cloud compression algorithm, the digital model of the denture is stored; The expression of the local complex index of each point cloud data is: ; In the formula, Represents the local complexity index of the i-th point cloud data; Represents the local complexity of the i-th point cloud data; Represents the local sharpness of the i-th point cloud data; Represents the number of all peaks on the curvature probability distribution curve of the i-th point cloud data; The expression of the structural complexity of each point cloud data is: ; In the formula, Represents the structural complexity of the i-th point cloud data; Represents the total number of clusters obtained after clustering the i-th point cloud data and all point cloud data in its neighborhood; Represents the angle between the normal vector of the i-th point cloud data and the j-th point cloud data in its neighborhood on the i-th point cloud data fitting surface; represents the number of all point cloud data in the neighborhood of the i-th point cloud data; norm() represents the normalization function.

2. A digital model storage method for multi-material 3D printed dentures according to claim 1, characterized in that: The method for determining the local complexity of each point cloud data is: The range and information entropy of the curvature of each point cloud data and all point cloud data in its neighborhood on the corresponding fitting surface are calculated respectively, and the product of the range and the information entropy is taken as the local complexity of each point cloud data.

3. The method for storing a digital model of a multi-material 3D printed denture according to claim 1, characterized in that: Method for determining the curvature probability distribution curve of each point cloud data: The curvature of each point cloud data and all point cloud data in its neighborhood on the fitting surface is calculated, and the histogram of the curvature of each point cloud data and all point cloud data in its neighborhood is statistically analyzed. The curve obtained after smoothing the histogram is used as the curvature probability distribution curve of each point cloud data.

4. The method for storing a digital model of a multi-material 3D printed denture according to claim 1, characterized in that: The method for determining the local sharpness of each point cloud data is: Extract all peaks on the curvature probability distribution curve of each point cloud data, calculate the kurtosis of each peak on the curvature probability distribution curve and all elements in its neighborhood, and record it as the kurtosis of each peak on the curvature probability distribution curve of each point cloud data; The average value of the kurtosis of all peaks on the curvature probability distribution curve of each point cloud data is taken as the local sharpness of each point cloud data.

5. The method for storing a digital model of a multi-material 3D printed denture according to claim 1, characterized in that: The method for determining the spatial complexity of each point cloud data is as follows: Calculate the mean value of the distance from each point cloud data and all point cloud data in its neighborhood to the fitting surface, and record it as the first distance mean value of each point cloud data; Calculate the mean of the distances from each point cloud data to all point cloud data in its neighborhood, and record it as the second distance mean of each point cloud data; The spatial complexity of each point cloud data is the ratio of the first distance mean to the second distance mean of each point cloud data.

6. The method for storing a digital model of a multi-material 3D printed denture according to claim 1, characterized in that: The neighborhood complexity of each point cloud data is the product of the local complexity index of each point cloud data and the spatial complexity.

7. The method for storing a digital model of a multi-material 3D printed denture according to claim 1, characterized in that: The expression of the comprehensive complex index of each point cloud data is: ; In the formula, Represents the comprehensive complexity index of the i-th point cloud data; Represents the neighborhood complexity of the i-th point cloud data; represents the structural complexity of the i-th point cloud data; norm() represents the normalization function.

8. The method for storing a digital model of a multi-material 3D printed denture according to claim 1, characterized in that: The storing of the digital denture model comprises: The denture digital model is used as the input of the point cloud compression algorithm, and the normalized value of the mean comprehensive complexity index of all point cloud data in the denture digital model is calculated. If the normalized value is less than the preset termination recursive threshold, the compression of the denture digital model is stopped. Otherwise, the denture digital model is divided into multiple sub-cubes using the point cloud compression algorithm, and the normalized value of the mean comprehensive complexity index of all point cloud data in each sub-cube is further calculated. When the normalized value of the mean comprehensive complexity index is less than the preset termination recursive threshold, the sub-cubes are no longer divided to obtain the compressed denture digital model, which is stored locally.

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