A method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction.

Abstract

This invention relates to the field of 3D modeling technology, specifically to a method for 3D scanning and data reconstruction of the oral cavity based on laser diffraction. The method includes: performing a nearest neighbor search on an initial oral cavity point cloud model to obtain curvature percentage change curves for each search radius; determining a target curvature threshold based on the fluctuation of each curvature percentage change curve; performing hierarchical processing using the target curvature threshold to obtain various curvature levels; acquiring the updated point cloud region for each first curvature level, the point cloud regions corresponding to each matched curvature level, and the point cloud regions for each remaining curvature level; analyzing the spatial size and level of the point cloud regions to determine the size of the thinned voxels; and performing thinning processing on the point cloud regions corresponding to the curvature levels to obtain a thinned oral cavity point cloud model. This invention effectively preserves key oral tissue features while ensuring the thinning effect of the oral cavity point cloud model, facilitating data reconstruction.

Description

Technical Field

[0001] This invention relates to the field of 3D modeling technology, specifically to a method for 3D scanning and data reconstruction of the oral cavity based on laser diffraction. Background Technology

[0002] In the process of using laser diffraction to perform 3D scanning of the oral cavity to reconstruct data, in order to reduce data redundancy, improve the efficiency of 3D reconstruction and visualization, reduce the computational burden, and facilitate storage and transmission, it is necessary to perform thinning processing on the point cloud data of the oral cavity.

[0003] Traditional thinning methods determine curvature thresholds by the proportion of discrete points under different curvatures, and then perform graded thinning. However, they do not take into account the feature distribution on actual oral tissues, which means that the point cloud model after thinning cannot well represent the features of oral tissues. This may result in the loss of key features, thereby affecting the accuracy of data reconstruction and the effectiveness of clinical applications. Summary of the Invention

[0004] To address the low accuracy of oral cavity data reconstruction after existing thinning processes, the present invention aims to provide a method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction. The specific technical solution adopted is as follows:

[0005] One embodiment of the present invention provides a method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction, the method comprising the following steps:

[0006] An initial oral cavity point cloud model is obtained by laser scanning of the oral cavity; nearest neighbor search is performed on the initial oral cavity point cloud model with several search radii to obtain the curvature ratio change curve of each search radius;

[0007] The target curvature threshold is determined based on the fluctuation of each curvature percentage change curve; the target curvature threshold is used to classify all curvature values ​​corresponding to the target search radius to obtain each curvature level;

[0008] Consistency analysis is performed on the regional features of the point cloud regions before and after curvature expansion corresponding to several selected first curvature levels to update the point cloud regions of the first curvature levels, thereby obtaining the updated point cloud regions for each first curvature level; wherein, the first curvature level is a curvature level whose curvature range contains curvature values ​​greater than the curvature threshold.

[0009] A first merging bias value is determined based on the spatial values ​​and distribution range proportions of the selected point cloud regions of several second curvature levels, in order to determine whether the second curvature level is updated to a matching curvature level. If updated, the point cloud regions corresponding to the matching curvature level are obtained. Wherein, the second curvature level is a curvature level with a level lower than a preset level.

[0010] The size of the thinned voxels is determined based on the spatial size and level of the updated point cloud region for each first curvature level, the point cloud regions corresponding to each matched curvature level, and the point cloud regions of the remaining curvature levels other than the first curvature level and the matched curvature level. This is used to perform thinning processing on the point cloud regions of the corresponding curvature levels, resulting in a thinned oral cavity point cloud model.

[0011] Further, the step of performing nearest neighbor search on the initial oral cavity point cloud model with several search radii to obtain the curvature percentage change curve for each search radius includes:

[0012] For any search radius, a nearest neighbor search is performed on the initial oral cavity point cloud model using the search radius to obtain the set of nearest neighbors for each discrete point;

[0013] Based on the nearest neighbor set, a covariance matrix is ​​constructed using the covariance calculation method; the eigenvalues ​​of the covariance matrix are obtained, and the curvature value of each discrete point under the search radius is obtained based on the eigenvalues;

[0014] Determine the first proportion of the number of discrete points corresponding to each curvature value in the total number of discrete points. Based on each curvature value and the first proportion corresponding to each curvature value, construct the curvature proportion change curve of the search radius.

[0015] Further, determining the target curvature threshold based on the fluctuation of each of the curvature percentage change curves includes:

[0016] Determine the minimum and maximum points in each of the curvature percentage change curves, and segment the curvature percentage change curves using the minimum value as the segmentation point to obtain each initial curvature segment.

[0017] For any initial curvature segment and any adjacent initial curvature segment, the second merging bias value is determined based on the curvature difference between the maxima in the two adjacent initial curvature segments.

[0018] The initial curvature segments are merged and analyzed using the second merging bias value to obtain the merged curvature segments.

[0019] The curvature values ​​corresponding to the two endpoints of each merged curvature segment are determined as suspected curvature thresholds, thus obtaining each suspected curvature threshold corresponding to each search radius;

[0020] The selection conformity is determined based on the second proportion of the occurrence frequency of each suspected curvature threshold in the total number of curvature proportion change curves and the first proportion of the number of discrete points corresponding to different search radii in the total number of discrete points.

[0021] By selecting the conformity, all the suspected curvature thresholds are filtered to obtain several target curvature thresholds.

[0022] Further, determining the second merging bias value based on the curvature difference between the maxima in two adjacent initial curvature segments includes:

[0023] The absolute value of the difference in curvature between the maxima in two adjacent initial curvature segments is determined to obtain the first merging bias factor;

[0024] Obtain the absolute value of the difference between the first proportion values ​​corresponding to the curvature of the maximum points in two adjacent initial curvature segments, and use the ratio of the absolute value of the difference between the first proportion values ​​to the absolute value of the maximum proportion difference as the second merging bias factor; wherein, the absolute value of the maximum proportion difference is the maximum value among all the absolute values ​​of the difference between the first proportion values.

[0025] The second merging bias value is determined by combining the first merging bias factor and the second merging bias factor.

[0026] Furthermore, the step of classifying all curvature values ​​corresponding to the target search radius using the target curvature threshold to obtain various curvature levels includes:

[0027] Obtain the number of identical values ​​between the suspected curvature threshold and the target curvature threshold corresponding to each search radius, and take the search radius corresponding to the largest number of identical values ​​as the target search radius.

[0028] The target curvature thresholds are arranged in order, and the curvature values ​​corresponding to the target search radius are graded using the arranged target curvature thresholds to obtain each curvature level.

[0029] Further, the consistency analysis of the regional features of the point cloud regions before and after curvature expansion corresponding to the selected first curvature levels is performed to update the point cloud regions of the first curvature levels, resulting in the updated point cloud regions for each first curvature level, includes:

[0030] For any first curvature level, obtain any point cloud region of the first curvature level as the point cloud region before expansion; update any curvature value smaller than the minimum curvature value in the curvature range of the first curvature level in the initial oral cavity point cloud model to the curvature range of the first curvature level, and obtain the point cloud region of the updated curvature range as the expanded point cloud region.

[0031] Determine the corresponding point of each boundary point of the point cloud region before expansion in the expanded point cloud region; determine the consistency index of the point cloud feature range before and after expansion based on the distance between each boundary point of the point cloud region before expansion and its corresponding point.

[0032] The consistency index is obtained for several curvature expansions. The optimal curvature expansion is selected based on the difference between the consistency indexes between two adjacent expansions. The point cloud region corresponding to the optimal curvature expansion is used as the updated point cloud region of the first curvature level. The two adjacent expansions indicate that the next expansion is based on the previous expansion.

[0033] Further, determining the corresponding point of each boundary point of the unexpanded point cloud region in the expanded point cloud region includes:

[0034] Determine the center point of the point cloud region before expansion, and then obtain the line connecting each boundary point of the point cloud region before and after expansion to the center point;

[0035] When the line connecting any boundary point of the point cloud region before expansion coincides with the line connecting a boundary point of the point cloud region after expansion, the boundary point of the point cloud region after expansion is taken as the corresponding point of the any boundary point.

[0036] Further, determining the consistency index of the point cloud feature range before and after expansion based on the distance between each boundary point of the point cloud region before expansion and its corresponding point includes:

[0037] A distance fluctuation curve is constructed using the boundary point number as the x-axis and the distance of each boundary point as the y-axis. The y-axis data of the distance fluctuation curve is then subjected to dimensionality reduction segmentation to obtain each dimensionality reduction segment.

[0038] Based on the ratio of the maximum number of curve data points in all dimensionality reduction segments to the total number of boundary points in the expanded point cloud region, and the distance between the center points of two adjacent dimensionality reduction segments, the consistency index of the point cloud feature range before and after expansion is determined.

[0039] Further, determining the first merging bias value based on the spatial values ​​and distribution range proportions of the selected point cloud regions of several second curvature levels includes:

[0040] For any second curvature level, the point cloud regions of other curvature levels that are adjacent to the point cloud region of the second curvature level and have a higher level than the second curvature level are used as the comparison point cloud regions.

[0041] Determine the spatial value of the point cloud region at the second curvature level, and then determine the ratio of the number of discrete points in the point cloud region at the second curvature level to the number of discrete points in each comparative point cloud region.

[0042] Based on the spatial values ​​and the respective ratios, a first merging bias value is determined between the second curvature level and each of the other curvature levels; wherein the spatial values ​​are negatively correlated with the first merging bias value, and the ratios are positively correlated with the first merging bias value.

[0043] Further, determining the thinned voxel size based on the spatial size and level of the updated point cloud region for each first curvature level, the point cloud regions corresponding to each matched curvature level, and the point cloud regions for the remaining curvature levels other than the first and matched curvature levels includes:

[0044] For any undetermined curvature level, determine the ratio of the level of the undetermined curvature level to the maximum level; wherein, the undetermined curvature level is the first curvature level, the matched curvature level, or the remaining curvature levels other than the first curvature level and the matched curvature level;

[0045] Calculate the product of the spatial value and the level ratio of the undetermined curvature level, and perform negative correlation normalization on the product of the spatial value and the level ratio to obtain the size correction coefficient of the undetermined curvature level;

[0046] The initial sludge volumetric size is corrected using the size correction factor to determine the sludge volumetric size of the undetermined curvature level.

[0047] The present invention has the following beneficial effects:

[0048] To overcome the shortcomings of existing thinning processes for oral point cloud models, this invention provides a method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction. This method determines the curvature threshold by obtaining the curvature ratio change curve of each search radius. It can fully consider the curvature characteristics of the oral point cloud model under different search methods, which improves the numerical accuracy of the curvature threshold and the grading effect when performing curvature grading to a certain extent. This facilitates subsequent thinning processing of the point cloud regions corresponding to the curvature grading results, which is the premise for subsequent graded thinning processing.

[0049] Next, in order to preserve the significant regional features of oral tissues as much as possible, the point cloud regions with higher curvature levels are expanded to obtain updated point cloud regions, and the lower curvature levels are merged into the surrounding higher curvature levels to obtain the point cloud regions corresponding to each matching curvature level. This is equivalent to adjusting the distribution of the point cloud regions of each curvature level obtained initially by combining the features of different curvature levels and point cloud regions of curvature levels, so as to achieve a more obvious emphasis on some oral tissue features.

[0050] Finally, the size of the thinned voxels is adaptively determined based on the point cloud region of each curvature level after adjustment. The thinned voxel size is then used to thin the point cloud region of the corresponding curvature level, resulting in a thinned oral point cloud model. Because the curvature grading of this invention considers the feature distribution on actual oral tissues, the thinned oral point cloud model can effectively represent the features of oral tissues, avoiding the loss of key features and improving the accuracy of data reconstruction and clinical application effectiveness. Attached Figure Description

[0051] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating the steps of a method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction, provided as an embodiment of the present invention;

[0053] Figure 2 This is a flowchart of step S21 in an embodiment of the present invention;

[0054] Figure 3 This is a flowchart of step S3 in an embodiment of the present invention;

[0055] Figure 4 This is a flowchart illustrating the steps for determining the first merging bias value in an embodiment of the present invention. Detailed Implementation

[0056] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the technical solution proposed according to the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0057] Unless otherwise defined, 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 invention pertains.

[0058] The application scenarios targeted by this invention can be:

[0059] Point cloud thinning of oral point cloud models can reduce data redundancy and improve the efficiency of 3D reconstruction and visualization, making it suitable for clinical applications such as orthodontic planning and implant surgery simulation. When performing point cloud thinning, it is crucial to maintain the accuracy of key anatomical structures, such as crown margins and occlusal surfaces, to avoid oversimplification that could negatively impact subsequent processing based on the oral 3D model. Simultaneously, it is essential to ensure that the thinned oral point cloud model still meets the accuracy requirements of subsequent processing and to avoid introducing jagged edges or distortion.

[0060] To overcome the shortcomings of existing thinned oral point cloud models, one embodiment of the present invention provides a method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction, such as... Figure 1 As shown, it includes the following steps:

[0061] S1. Perform laser scanning on the oral cavity to obtain an initial oral cavity point cloud model; perform nearest neighbor search on the initial oral cavity point cloud model with several search radii to obtain the curvature ratio change curve of each search radius.

[0062] The curvature distribution of the initial oral point cloud model for nearest neighbor search varies depending on the search radius. To facilitate the analysis of each curvature distribution and determine the target curvature threshold, it is necessary to obtain the curvature percentage change curve for each search radius. The curvature percentage change curve is the data support for determining the target curvature threshold. Determining the target curvature threshold by using the curvature percentage change curve for different search radii helps overcome the low accuracy of traditional methods that determine the curvature threshold solely based on the proportion of discrete points under different curvatures at a single search radius, thereby improving the accuracy of curvature classification.

[0063] Step S1 above can be achieved through steps S11 to S12 (not shown in the figure):

[0064] S11, Laser scanning is performed on the oral cavity to obtain an initial oral cavity point cloud model.

[0065] Before laser scanning, the oral cavity is pre-treated. Specifically, gingival retraction cord is used to expose the edges of the prepared material, and the teeth are cleaned and dried to prevent interference from liquids on the laser reflection.

[0066] Specifically, during the scanning process, a zigzag scanning path is used for the posterior teeth region, sequentially covering the occlusal surface, labial-buccal surface, and lingual-palatal surface. Simultaneously, maxillary and mandibular displacement data are acquired. A point cloud stitching algorithm is used to register partially overlapping scan data, and translation vectors and rotation matrices are used to achieve spatial alignment of multi-angle data, resulting in a coarse point cloud model of the oral cavity, denoted as the initial oral cavity point cloud model.

[0067] It should be noted that the scanning head needs to be preheated to prevent fogging in the warm and humid oral environment from affecting the imaging; the standard procedure should be followed first for the working dentition, then for the opposing dentition, and finally for the occlusal relationship.

[0068] S12, perform nearest neighbor search on the initial oral cavity point cloud model with several search radii respectively, and obtain the curvature ratio change curve of each search radius.

[0069] For any search radius, the steps to obtain the curvature percentage change curve of the search radius include:

[0070] S121, use the search radius to perform a nearest neighbor search on the initial oral cavity point cloud model to obtain the set of nearest neighbors for each discrete point.

[0071] In point cloud processing, the radius *r* of the K-nearest neighbor search is used to define a spherical range to find the nearest neighbor point within a certain radius of a given point. The detailed implementation process of the nearest neighbor search is prior art and is not within the scope of this invention; therefore, it will not be elaborated upon here.

[0072] S122: Based on the nearest neighbor set, construct the covariance matrix using the covariance calculation method; obtain the eigenvalues ​​of the covariance matrix, and obtain the curvature value of each discrete point under the search radius based on the eigenvalues.

[0073] Each discrete point has its corresponding covariance matrix. By extracting the eigenvalues ​​of the covariance matrix, the curvature value of each discrete point can be determined. Different discrete points may have the same curvature value. The calculation process for the curvature value is existing technology and is not within the scope of this invention; therefore, it will not be described in detail here.

[0074] S123, determine the first proportion of the number of discrete points corresponding to each curvature value in the total number of discrete points, and construct the curvature proportion change curve of the search radius based on each curvature value and the first proportion corresponding to each curvature value.

[0075] Here, the curvature percentage change curve can show how the number of discrete points corresponding to the curvature value changes as the curvature value increases. When determining the curvature threshold of the oral cavity point cloud model, using the curvature percentage change curve of the search radius as the basic data analysis can intuitively reflect the correlation between the statistical law of curvature distribution and anatomical features, thereby guiding the selection of the curvature threshold.

[0076] In this embodiment, curvature values ​​of varying magnitudes are used as the abscissa, and the proportion of discrete points corresponding to each curvature value is used as the ordinate, i.e., the first proportion value. Curve fitting is performed using the least squares method to obtain the curvature proportion change curve. By analyzing the curvature proportion change curve, the changes in different curvature distributions in the oral cavity point cloud model can be analyzed. The curvature distribution changes can be used to analyze curvature distribution characteristics to determine the most suitable curvature threshold.

[0077] It should be noted that the curvatures on the horizontal axis of the curvature percentage change curve are arranged in a certain order, such as from smallest to largest. By analyzing the neighboring search results for each search radius and performing steps S121 to S123 above, the curvature percentage curve for each search radius can be obtained.

[0078] Thus, this embodiment has obtained the curvature percentage change curve for each search radius.

[0079] S2, determine the target curvature threshold based on the fluctuation of each curvature percentage change curve; use the target curvature threshold to classify all curvature values ​​corresponding to the target search radius to obtain each curvature level.

[0080] Grouping curvature features across different curvature ranges can be achieved by determining a curvature threshold. However, obtaining the curvature threshold based on the proportion of curvature within different search radii will yield different results. To avoid overlooking the impact of different search methods on the obtained curvature when determining the threshold using a single search radius, we analyze the fluctuations in the curvature proportion curves across different search radii to determine a suitable curvature threshold and classify all curvature levels in the initial point cloud model.

[0081] Step S2 above can be achieved through steps S21 to S22 (not shown in the figure):

[0082] S21, determine the target curvature threshold based on the fluctuation of each curvature percentage change curve.

[0083] Here, there can be multiple target curvature thresholds. The target curvature threshold is determined by analyzing the curvature search results of multiple different search radii. It can comprehensively consider the influence of different search methods on the obtained curvature, which helps to improve the numerical accuracy of the search threshold and facilitates more reliable classification of curvature in the future.

[0084] The previous step S21 passed Figure 2 Steps S211 to S216 shown are implemented as follows:

[0085] S211, determine the minimum and maximum points in each curvature percentage change curve, and segment the curvature percentage change curve using the minimum value as the segmentation point to obtain each initial curvature segment.

[0086] Here, the maximum point in the curvature proportion change curve corresponds to the high-frequency concentration area of ​​the curvature distribution, such as the peak of the curvature distribution, which may represent the concentrated occurrence of a certain significant feature, such as the edge of a tooth or pits and fissures; the minimum value in the curvature proportion change curve is located at the valley between two maximum points, which can reflect the transition area or category decomposition of the curvature distribution, that is, the natural separation point between different curvature features.

[0087] In this embodiment, the process of determining the maximum and minimum points in the curvature percentage change curve is existing technology and is not within the scope of protection of this invention, and will not be described in detail here. Each curvature percentage change curve has several corresponding initial curvature segments.

[0088] It should be noted that using the minimum point as the segmentation point results in a low proportion of both types of curvature features. This segmentation maximizes intra-segment consistency, meaning that curvature features within the same segment are similar, which helps ensure low heterogeneity for each curvature segment. Compared to the maximum point, the curvature distribution changes more gently in the transition region, resulting in stronger stability of the divided curvature segments.

[0089] S212, for any initial curvature segment and any adjacent initial curvature segment, determine the second merging bias value based on the curvature difference between the maxima in the two adjacent initial curvature segments.

[0090] Here, the second merging bias value refers to the probability of merging two adjacent curvature segments. It can be determined by analyzing the similarity of the curvature distribution characteristics of two adjacent initial curvature segments.

[0091] As an exemplary implementation, the step of determining the second merging bias value includes:

[0092] The first step is to determine the absolute value of the difference in curvature between the maxima in two adjacent initial curvature segments, thus obtaining the first merging bias factor.

[0093] In this embodiment, the smaller the difference in the magnitude of the curvature values ​​corresponding to the maxima in two adjacent initial curvature segments, the more similar the curvature values ​​corresponding to the significant curvature features of the two initial curvature segments are, and the greater the possibility that the two adjacent initial curvature segments will be merged.

[0094] The second step is to obtain the absolute value of the difference between the first proportion values ​​corresponding to the curvature of the maximum point in two adjacent initial curvature segments, and use the ratio of the absolute value of the difference between the first proportion values ​​to the absolute value of the difference between the maximum proportion values ​​as the second merging bias factor.

[0095] In this embodiment, the high-frequency concentration area of ​​the curvature distribution corresponding to the maximum point can represent the concentrated occurrence of a certain significant feature. Therefore, if the curvature distribution ratio of the maximum points of two adjacent initial curvature segments is similar, the curvature features of the two adjacent initial curvature segments are similar. At this time, the possibility of merging is smaller, and it can be used to determine the second merging bias value.

[0096] The third step is to combine the first and second pooling bias factors to determine the second pooling bias value.

[0097] In this embodiment, the possibility of merging two adjacent initial curvature segments is analyzed. The differences in curvature characteristics are analyzed from two perspectives: the numerical differences between curvatures and the differences between the proportions of significant curvature distributions. By integrating the merging factors from both aspects, the numerical accuracy of the second merging bias value can be effectively improved, which facilitates the subsequent merging operation of the initial curvature segments.

[0098] As an example, the formula for calculating the second merge bias value corresponding to the j-th initial curvature segment and the (j+1)-th initial curvature segment can be:

[0099] ;

[0100] In the formula, This represents the second merge bias value corresponding to the j-th initial curvature segment and the (j+1)-th initial curvature segment. This represents the absolute value of the difference in curvature between the maxima in the j-th and (j+1)-th initial curvature segments. This represents the first merging bias factor corresponding to the j-th and (j+1)-th initial curvature segments. This represents the absolute value of the difference between the first proportion values ​​corresponding to the curvature of the maximum point in the j-th and (j+1)-th initial curvature segments. This represents the maximum absolute value of the differences between all the first percentage values. This represents the second merging bias factor corresponding to the j-th and (j+1)-th initial curvature segments.

[0101] In the formula for calculating the second merging bias value The larger the value, the worse the curvature similarity between the maxima in the j-th and (j+1)-th initial curvature segments, and the less likely it is to merge the j-th and (j+1)-th initial curvature segments. Therefore, the value should be... As the first pooling bias factor, under normal circumstances There is no possibility that it is zero. If there is an extreme case, a non-zero constant is added to the denominator of the first merging bias factor, with an empirical value of 0.01. This represents the ratio of the difference in the proportion of curvature distribution between the maxima and the difference in the proportion of the maximum curvature distribution in the j-th and (j+1)-th initial curvature segments. The larger the value, the smaller the proportion of curvature distribution at one minimum point in two adjacent initial curvature segments compared to the proportion of curvature distribution at the other minimum point. This indicates that the segments are not suitable as a single curvature segment and the j-th and (j+1)-th initial curvature segments should be merged. The second merging bias value should be larger.

[0102] S213, the initial curvature segments are merged and analyzed using the second merging bias value to obtain the merged curvature segments.

[0103] Here, in order to reduce the amount of analysis data for the target curvature threshold and improve the efficiency of determining the target curvature threshold, the second merging bias value determined in step S212 is used for merging analysis to perform merging operations on the initial curvature segments and obtain merged curvature segments.

[0104] In this embodiment, in order to facilitate comparative analysis and determine whether to perform merging processing, the second merging bias value can be normalized. For example, the sigmoid function can be used to normalize the second merging bias value to obtain the normalized second merging bias value.

[0105] As an exemplary implementation, the steps for obtaining the merged curvature segments include:

[0106] For each curvature percentage change curve, the normalized second merge bias value corresponding to the first and second initial curvature segments in the curve can be calculated in order. If the normalized second merge bias value is greater than the merge bias threshold, the merge bias threshold can be taken as an empirical value of 0.7, then the first and second initial curvature segments are merged to obtain a merged curvature segment.

[0107] Next, calculate the normalized second merge bias value corresponding to the first merged curve segment and the third initial curvature segment. If the normalized second merge bias value is still greater than the merge bias threshold, then merge the first merged curve segment and the third initial curvature segment to obtain a new merged curve segment. If the normalized second merge bias value is not greater than the merge bias threshold, then take the first merged curve segment as the final merged curve segment. Then, calculate the second merge bias value corresponding to the third and fourth initial curvature segments and perform the merge judgment operation again.

[0108] Thus, by performing the merging and judgment operations sequentially according to the distribution order of each initial curvature segment in the curvature, we can obtain each merged curvature segment corresponding to each curvature percentage change curve.

[0109] S214, determine the curvature values ​​corresponding to the two endpoints of each merged curvature segment as suspected curvature thresholds, and obtain each suspected curvature threshold corresponding to each search radius.

[0110] In this embodiment, the curvature threshold is used to divide the curvature range. Therefore, the curvature values ​​at the two endpoints of the merged curvature segments can be used as suspected curvature thresholds. This allows us to obtain several suspected curvature thresholds corresponding to the curvature percentage change curve for each search radius. Each suspected curvature threshold may become the final target curvature threshold, used to divide the curvature range.

[0111] S215. Determine the selection conformity based on the second proportion of the occurrence frequency of each suspected curvature threshold in the total number of curvature proportion change curves and the first proportion of the number of discrete points corresponding to different search radii in the total number of discrete points.

[0112] Here, each suspected curvature threshold is a curvature threshold with a different numerical value. Each suspected curvature threshold meets the selection requirements of the target curvature threshold to a different degree. In order to select the most suitable curvature threshold, i.e. the target curvature threshold, from all suspected curvature thresholds, it is necessary to determine the selection compliance degree of each suspected curvature threshold.

[0113] In this embodiment, the more frequently a suspected curvature threshold appears on curves showing changes in the proportion of different curvature values, the stronger the reliability of the suspected curvature threshold and the higher its selection confidence. The smaller the proportion of the number of discrete points corresponding to a suspected curvature threshold in the total number of discrete points within the corresponding search radius, the more the suspected curvature threshold matches the characteristics of minimum data, and the greater its likelihood of serving as a segmentation point, thus resulting in a higher selection consistency. Therefore, the second proportion value is positively correlated with the selection consistency, while the first proportion value is negatively correlated with the selection consistency.

[0114] As an example, the formula for calculating the compliance of the selection of the m-th suspected curvature threshold can be:

[0115] ;

[0116] In the formula, This indicates the degree of agreement in selecting the m-th suspected curvature threshold. This represents the number of times the m-th suspected curvature threshold appears, i.e., the number of curvature percentage change curves that appear at the m-th suspected curvature threshold. N represents the total number of all curvature percentage change curves. This represents the second proportion value corresponding to the m-th suspected curvature threshold. This represents the first proportion value corresponding to the m-th suspected curvature threshold on the curvature proportion change curve of the k-th and m-th suspected curvature thresholds.

[0117] Following the calculation process of the selection compliance of the m-th suspected curvature threshold, the selection compliance of each suspected curvature threshold can be obtained.

[0118] S216, by selecting the conformity, all suspected curvature thresholds are screened to obtain several target curvature thresholds.

[0119] In this embodiment, a selection compliance threshold is set, and an empirical value of 0.7 can be used. To compare and analyze with the selection compliance threshold, the selection compliance of each suspected curvature threshold is normalized, for example, using the sigmoid function to normalize the selection compliance, resulting in a normalized selection compliance. After obtaining the selection compliance threshold and the normalized selection compliance, the normalized selection compliance and the selection compliance threshold are compared. The suspected curvature threshold corresponding to the selection compliance greater than the selection compliance threshold is taken as the final target curvature threshold, thus obtaining several target curvature thresholds.

[0120] It should be noted that in extreme cases where there are fewer than two target curvature thresholds, the value of the selected threshold can be adjusted to achieve more accurate curvature value classification. For example, the selected threshold can be adjusted from 0.7 to 0.6, and the target curvature threshold filtering operation can be performed again. Of course, implementers can set or adjust the selected threshold according to specific circumstances; no specific limitations are imposed here.

[0121] S22, use the target curvature threshold to classify all curvature values ​​corresponding to the target search radius to obtain the curvature range of each curvature level.

[0122] Here, the target search radius is the search radius with the highest number of identical suspected curvature thresholds to the target curvature threshold. The more identical threshold values ​​there are, the better the search effect of the corresponding search radius. Of course, the target search radius can also be any search radius.

[0123] Step S22 above can be achieved through steps S221 to S222 (not shown in the figure):

[0124] S221, obtain the number of times the suspected curvature threshold and the target curvature threshold are the same for each search radius, and take the search radius corresponding to the largest number of the same values ​​as the target search radius.

[0125] S222: Arrange the target curvature thresholds in order, and perform a classification operation on all curvature values ​​corresponding to the target search radius using the arranged target curvature thresholds to obtain each curvature level.

[0126] For example, if the target curvature thresholds, arranged from smallest to largest, are a1, a2, and a3, then the curvature values ​​less than or equal to a1 corresponding to the target search radius are grouped into one level, designated as the first curvature level (level 1). Next, the curvature values ​​greater than a1 and less than or equal to a2 corresponding to the target search radius are grouped into another level, designated as the second curvature level (level 2). Finally, the curvature values ​​greater than a3 corresponding to the target search radius are grouped into another level, designated as the third curvature level (level 3). The larger the curvature, the higher the corresponding level; the third curvature level is higher than the second curvature level, and the second curvature level is higher than the first curvature level.

[0127] Thus, this embodiment achieves a more accurate division of each curvature level.

[0128] S3. Perform consistency analysis on the regional features of the point cloud regions before and after curvature expansion corresponding to several selected first curvature levels to update the point cloud regions of the first curvature levels and obtain the updated point cloud regions of each first curvature level.

[0129] Each curvature level obtained in step S2 can be used to divide the initial oral cavity point cloud model into point cloud regions. However, the divided point cloud regions may not accurately represent the characteristics of the location of oral tissues. For example, for the boundary between the soft and hard palates, when the curvature range is small, only the boundary line between the soft and hard palates is obtained, and the divided point cloud regions do not adequately represent the connection between the soft and hard palates. Therefore, for point cloud regions corresponding to curvature levels with larger curvature, the regional characteristics of the corresponding point cloud regions can be analyzed by adding or reducing the curvature within the curvature range to represent changes in the point cloud regions.

[0130] In this embodiment, the curvature range of the first curvature level is a range of curvatures with relatively large curvatures. This embodiment uses curvature levels whose curvature range includes curvature values ​​greater than a curvature threshold as the first curvature level. The curvature threshold can be set to 0.6, so curvature levels containing curvature values ​​greater than the curvature threshold of 0.6 can be selected from all curvature levels as the first curvature level. Furthermore, all curvatures in this embodiment are assumed to be standardized curvatures, meaning the curvature values ​​range from 0 to 1.

[0131] It should be noted that not updating the point cloud region for each curvature level can improve the efficiency of thinning the oral cavity point cloud model while ensuring the thinning effect.

[0132] The above step S3 can be achieved through Figure 3 The steps S31 to S33 shown are implemented as follows:

[0133] S31, for any first curvature level, obtain any point cloud region of the first curvature level as the point cloud region before expansion; update any curvature value smaller than the minimum curvature value in the curvature range of the first curvature level adjacent to the minimum curvature value in the initial oral cavity point cloud model to the curvature range of the first curvature level, and obtain the point cloud region of the updated curvature range as the expanded point cloud region.

[0134] Here, the curvature value smaller than the minimum curvature value corresponding to the first curvature level in the initial oral cavity point cloud model is obtained. Adding this smaller curvature value to the curvature range of the first curvature level expands the curvature features of the current curvature range. Through this expanded curvature range, the initial point cloud region is updated, resulting in a new point cloud region. Each addition of a new curvature value constitutes a curvature expansion of the curvature range of the first curvature level.

[0135] In this embodiment, each curvature level may correspond to multiple different point cloud regions. For ease of description, we take any point cloud region corresponding to a single curvature level as an example, and analyze any point cloud region corresponding to any first curvature level to complete the point cloud region update.

[0136] First, obtain the point cloud regions before and after a single expansion corresponding to any first curvature level. This allows for subsequent analysis of the consistency of the performance characteristics of the point cloud regions before and after expansion, determining whether they belong to the same feature range. Specifically, analyzing the consistency of the edge contours of the point cloud regions before and after expansion is to verify whether the expanded and unexpanded point cloud regions belong to the same geometric feature. Stronger consistency in the regional features of the point cloud regions before and after expansion indicates that the regional features belong to the same surface feature.

[0137] It should be noted that after a single curvature expansion is completed, if further expansion is required, the next curvature expansion must be performed based on the curvature range of the previous expansion.

[0138] S32, determine the corresponding point of each boundary point of the point cloud region before expansion in the expanded point cloud region; determine the consistency index of the point cloud feature range before and after expansion based on the distance between each boundary point of the point cloud region before expansion and its corresponding point.

[0139] Here, a corresponding point refers to a point located on the boundary of the expanded point cloud region along the line connecting the boundary point and the center point of the point cloud region before expansion; the consistency index of the point cloud feature range indicates the consistency of the distance changes between the boundary points and corresponding points between the point cloud regions before and after expansion.

[0140] S321, determine the corresponding point in the expanded point cloud region for each boundary point of the point cloud region before expansion.

[0141] The first step is to determine the center point of the point cloud region before expansion, and then obtain the connection between each boundary point of the point cloud region before and after expansion and the center point.

[0142] In this embodiment, the center point of the point cloud region before expansion is calculated using the centroid formula. The method for determining the center point is existing technology and is not protected by this invention; therefore, it will not be described in detail here.

[0143] The second step is to take a boundary point in the expanded point cloud region as the corresponding point of any boundary point when the line connecting any boundary point in the expanded point cloud region coincides with the line connecting any boundary point in the expanded point cloud region.

[0144] In this embodiment, the directed line connecting the boundary points of the point cloud region before expansion to its center point is determined as the first line. Then, the directed line connecting the boundary points of the expanded point cloud region to the center point of the point cloud region before expansion is determined as the second line. When the first and second lines coincide, the boundary points of the first and second lines are considered corresponding points. For ease of subsequent analysis, the boundary points of the second line can be directly used as the corresponding points of the boundary points of the first line, thus obtaining the corresponding points of each boundary point of the point cloud region before expansion.

[0145] Generally, the boundary of the point cloud region after curvature expansion will be larger than the boundary before curvature expansion. Therefore, each boundary point of the point cloud region before expansion has its corresponding point. In extreme cases, some boundary points of the point cloud region before expansion do not have corresponding points. In this case, these boundary points will not participate in the subsequent calculation of consistency index.

[0146] S322, Based on the distance between each boundary point of the point cloud region before expansion and its corresponding point, determine the consistency index of the feature range of the point cloud before and after expansion.

[0147] In this embodiment, when the distance between each boundary point and its corresponding point in the point cloud region before expansion changes more consistently during a single expansion, the point cloud region before and after expansion is more likely to have the same feature range. Therefore, the consistency of the feature range of the point cloud before and after expansion can be quantitatively analyzed by the distance between each boundary point and its corresponding point.

[0148] The first step is to construct a distance fluctuation curve with each boundary point number as the x-axis and the distance of each boundary point as the y-axis.

[0149] In this embodiment, the boundary point numbering is set according to the distribution of the boundary points, and the result of setting the boundary point numbering is not unique; the distance of each boundary point refers to the distance between the boundary point of the expanded point cloud region and its corresponding point. Given the coordinate positions of two points, the distance between the two points can be calculated directly, which is the existing implementation process and will not be described in detail here.

[0150] The second step is to perform dimensionality reduction segmentation on the ordinate data of the distance fluctuation curve to obtain each dimensionality reduction segment.

[0151] In this embodiment, the ordinate data (distance data) of the distance fluctuation curve is reduced in dimensionality using Piecewise Aggregate Approximation (PAA) to obtain various dimensionality-reduced segments. Piecewise Aggregate Approximation is existing technology and is not within the scope of this invention; therefore, it will not be described in detail here.

[0152] It should be noted that using consistency metrics based on dimensionality reduction and segmentation can achieve an optimal balance between information compression and feature preservation. Compared with other existing distance change similarity quantification methods, it can significantly reduce computational costs by suppressing high-frequency noise through segmented smoothing.

[0153] The third step is to determine the consistency index of the point cloud feature range before and after expansion based on the ratio of the maximum number of curve data points in all dimensionality reduction segments to the total number of boundary points in the expanded point cloud region, as well as the distance between the center points of two adjacent dimensionality reduction segments.

[0154] The consistency index of the point cloud feature range before and after expansion is determined by judging the proportion of the number of points with relatively consistent lengths. It needs to consider two factors: the similarity of the distance between the center points of every two adjacent dimensionality reduction segments and the proportion of the maximum number of curve data points in the dimensionality reduction segments to the total number of boundary points of the expanded point cloud region. The more similar the distance and the larger the number of curve data points in the dimensionality reduction segments, the greater the consistency index of the point cloud feature range before and after expansion.

[0155] As an example, the formula for calculating the consistency index of the point cloud feature range before and after the g-th expansion can be:

[0156] ;

[0157] In the formula, The consistency index representing the feature range of the point cloud before and after the g-th expansion. This represents the number of curve data points in the v-th dimensionality reduction segment. This represents the total number of boundary points in the expanded point cloud region before and after the g-th expansion, i.e., the number of boundary points corresponding to the distance fluctuation curve. This represents the ratio of the number of curve data points in the v-th dimensionality reduction segment to the total number of boundary points in the expanded point cloud region. The number of curve data points in the v-th dimensionality reduction segment is the maximum number of curve data points across all dimensionality reduction segments. , This represents the cumulative difference in distance between the center points of every two adjacent dimensionality reduction segments before and after the g-th expansion, where V represents the number of dimensionality reduction segments. This represents the distance between the center points of the v-th and v-1-th dimensionality-reduced segments.

[0158] In the formula for calculating the consistency index, The larger the value, the greater the proportion of the number of curve data points in the v-th dimensionality reduction segment to the number of boundary points corresponding to the expanded point cloud region. The larger the value, the smaller the distance between the center points of adjacent segments. At this time, the distance changes are more consistent, and the point cloud regions before and after the g-th expansion are more of the same feature point cloud range.

[0159] By referring to the process of determining the consistency index of the point cloud feature range before and after the g-th expansion, the consistency index of the point cloud feature range before and after each expansion can be obtained.

[0160] It is worth noting that each dimensionality reduction segment can represent a stage of distance change of a certain type. The consistency index determined by each dimensionality reduction segment has higher numerical accuracy because it can ignore small fluctuations within the segment and focus on the overall trend.

[0161] S33, obtain the consistency index for several curvature expansions, select the optimal curvature expansion based on the difference between the consistency indexes of two adjacent expansions, and use the point cloud region corresponding to the optimal curvature expansion as the updated point cloud region of the first curvature level.

[0162] Here, two adjacent expansions indicate that the next expansion is based on the previous expansion.

[0163] For any point cloud region of any first curvature level, specifically, following the method for obtaining the consistency index of the point cloud feature range before and after the g-th expansion, several consistency indices are obtained during curvature expansion. The number of curvature expansions can be set according to the distribution of smaller curvatures around the minimum curvature in the point cloud region of the first curvature level, and is not specifically limited here. Then, the difference between the consistency indices of every two adjacent expansions is calculated, that is, the consistency index of the later expansion minus the consistency index of the previous expansion. The largest consistency index difference is selected as the denominator of the ratio. The difference between the consistency indices of every two adjacent expansions is used as the numerator of the ratio in the order before and after expansion. The comparison values ​​are normalized to obtain the feature range conformity. When the feature range conformity corresponding to two expansions is greater than the preset conformity threshold, which can be taken as an empirical value of 0.7, the later expansion of the two expansions is taken as the optimal curvature expansion. The point cloud region corresponding to the optimal curvature expansion is taken as the updated point cloud region of the first curvature level, and the updated point cloud region of the first curvature level is obtained.

[0164] Referring to the method for determining the updated point cloud region of the first curvature level described above, the updated point cloud region of each first curvature level can be obtained.

[0165] In special cases, when the feature range conformity of the first and second curvature expansions of a certain first curvature level is less than the preset conformity threshold, the original point cloud region directly corresponding to it can be used as the updated point cloud region. In other cases, there may be multiple feature range conformities of a certain first curvature level that meet the preset conformity threshold requirement. In this case, the subsequent expansion corresponding to the largest feature range conformity can be used as the optimal second curvature expansion.

[0166] Thus, this embodiment has obtained the updated point cloud regions for each first curvature level.

[0167] S4. Determine the first merging bias value based on the spatial values ​​and distribution range of the selected point cloud regions of several second curvature levels, in order to determine whether the second curvature level is updated to the matching curvature level. If it is updated, obtain the point cloud regions corresponding to the matching curvature level.

[0168] Here, the second curvature level is a curvature level that is lower than the preset level, which can be set to 3. For each curvature level, the following example illustrates the level: If there are currently 10 curvature levels, then the maximum curvature level is 10, and the levels are 9, 8, 7, 6, 5, 4, 3, 2, and 1 respectively.

[0169] In oral point cloud models, there are situations where regions with low curvature are surrounded by multiple regions with high curvature. When the spatial value of the region with low curvature is small, in order to emphasize the surrounding regions with high curvature, the feature space range of the region with high curvature can be larger and more obvious when the point cloud is thinned. After thinning the feature space range, the point cloud region retains more features of the feature space range. Curvature levels with low curvature can be merged to obtain each matching curvature level.

[0170] The first merging bias value is determined based on the spatial values ​​and distribution range proportions of several selected point cloud regions of the second curvature level. This can be achieved through... Figure 4 Steps S41 to S43 shown are implemented as follows:

[0171] S41, for any second curvature level, the point cloud regions of other curvature levels that are adjacent to the point cloud region of the second curvature level and have a higher level than the second curvature level are used as the comparison point cloud regions.

[0172] S42, determine the spatial value of the point cloud region of the second curvature level, and then determine the ratio of the number of discrete points in the point cloud region of the second curvature level to the number of discrete points in each comparison point cloud region.

[0173] S43, based on the spatial values ​​and the various ratios, determine the first merging bias value between the second curvature level and each of the other curvature levels.

[0174] In this embodiment, when the spatial value of the point cloud region of the second curvature level is smaller and the area ratio of the point cloud region to the comparison point cloud region is larger, the point cloud region of the second curvature level should be merged into the point cloud region of the curvature level of the comparison point cloud region.

[0175] As an example, the formula for calculating the first merge bias value of the f-th second curvature level and the s-th other curvature level can be:

[0176] ;

[0177] In the formula, This represents the first merge bias value between the f-th second curvature level and the s-th other curvature level. This represents the ratio of the number of discrete points in the point cloud region of the f-th second curvature level to the number of discrete points in the s-th comparison point cloud region. This represents the spatial value of the point cloud region at the f-th second curvature level. The calculation of the spatial size of the point cloud region is based on existing technology and will not be elaborated further. Generally, the spatial value cannot be zero. However, in extreme cases, the value will be zero. Add a non-zero constant to the denominator, with an empirical value of 0.01.

[0178] After obtaining the first merge bias value of the second curvature level and each of the other curvature levels, it is determined whether the second curvature level has been updated to the matching curvature level. If updated, the point cloud regions corresponding to the matching curvature level are obtained, including:

[0179] If the first merging bias value of another curvature level is greater than the merging threshold and is at its maximum, then that other curvature level is used as the matching curvature level of the second curvature level, and the second curvature level is updated to the matching curvature level to obtain the point cloud regions corresponding to the matching curvature level; if the first merging bias values ​​of all other curvature levels are not greater than the merging threshold, then the second curvature level is not updated, and the original point cloud region corresponding to the second curvature level remains unchanged, and continues to participate in the implementation process of step S5.

[0180] The merging threshold is taken as an empirical value of 0.7, which implementers can set according to the specific curvature level merging requirements without specific limitations. The matched curvature level includes not only its own corresponding point cloud region but also the point cloud regions corresponding to the second curvature level, thus the matched curvature level corresponds to multiple point cloud regions. Of course, there is also a case where the matched curvature level is the first curvature level, i.e., the curvature range corresponding to the matched curvature level contains curvature values ​​greater than the curvature threshold of 0.6. In this case, the matched curvature level includes not only its own updated point cloud region but also the point cloud regions corresponding to the second curvature level, collectively referred to as the point cloud regions corresponding to the matched curvature level.

[0181] In this embodiment, the implementer can set all threshold values ​​according to the specific actual situation, and no specific limitation is made here.

[0182] Referring to the process of determining whether to update to the matching curvature level for the fth second curvature level, we can obtain the point cloud regions corresponding to all matching curvature levels.

[0183] Thus, this embodiment has obtained the point cloud regions corresponding to each matching curvature level.

[0184] S5. Based on the spatial size and level of the updated point cloud region of each first curvature level, the point cloud region corresponding to each matching curvature level, and the point cloud regions of the remaining curvature levels other than the first curvature level and the matching curvature level, determine the size of the thinned voxels to perform thinning processing on the point cloud regions of the corresponding curvature levels, and obtain the thinned oral point cloud model.

[0185] It should be noted that different tissues or different locations within the oral cavity may exhibit varying curvature. To facilitate the assessment of a patient's oral condition based on an oral point cloud model and to reduce the storage size of the model, the point cloud model should be thinned while preserving its individual features. This results in a more clearly defined, thinned oral point cloud model. To better preserve the features of each part of the oral point cloud model, different thinning methods should be used for different curvature feature representations. Specifically, the thinning voxel size should be adaptively determined for each curvature range, and the thinning operation should be performed on the point cloud regions representing different curvature features based on the determined voxel size.

[0186] The determination of the thinned voxel size based on the spatial size and level of the updated point cloud region for each first curvature level, the point cloud regions corresponding to each matched curvature level, and the point cloud regions for the remaining curvature levels other than the first and matched curvature levels can be achieved through steps S51 to S53 (not shown in the figure):

[0187] S51, for any undetermined curvature level, determine the ratio of the level of the undetermined curvature level to the maximum level.

[0188] S52, calculate the product of the spatial value and the ratio of the level to be determined curvature level, and perform negative correlation normalization on the product of the spatial value and the ratio of the level to obtain the size correction coefficient of the level to be determined curvature level.

[0189] S53 uses a size correction factor to correct the initial sludge voxel size and determines the sludge voxel size for the desired curvature level.

[0190] Here, the undetermined curvature level is the first curvature level, the matched curvature level, or the remaining curvature levels other than the first curvature level and the matched curvature level.

[0191] In this embodiment, a higher curvature level indicates more pronounced features of the corresponding point cloud region in the oral cavity space. Furthermore, a smaller proportion of all point cloud regions corresponding to a curvature level within the overall oral cavity indicates more unique features of the corresponding point cloud region, requiring greater retention. Therefore, a smaller thinning degree should be used when thinning the point cloud region corresponding to the curvature level. The control of the thinning degree mainly depends on the voxel size. Larger voxel sizes result in a greater degree of thinning through voxel filtering, leading to fewer discrete points in the final thinned point cloud model.

[0192] As an example, the formula for calculating the sparse voxel size of the h-th undetermined curvature level can be:

[0193] ;

[0194] In the formula, This represents the dimpled voxel size for the h-th undetermined curvature level, and Y represents the initial dimpled voxel size, which can be set to 0.5 mm. This represents the hyperbolic tangent function, used for normalization. This represents the total spatial value of all point cloud regions corresponding to the h-th undetermined curvature level. This represents the level of the h-th undetermined curvature level. A higher curvature level index corresponds to a higher level. Indicates the highest level. This represents the size correction factor for the h-th undetermined curvature level.

[0195] For each curvature level, the size of the thinned voxels is determined, and the point cloud region corresponding to the curvature level is thinned using voxel filtering with different voxel sizes, ultimately obtaining the thinned oral cavity point cloud model. Notably, the thinned voxel sizes and the degree of thinning are the same for all point cloud regions within the same curvature level.

[0196] After obtaining the thinned oral point cloud model, this embodiment also includes: visualizing the thinned oral point cloud model and marking point cloud regions with different curvature levels with different colors.

[0197] In this embodiment, the position coordinates and corresponding curvature levels of each discrete point in the oral cavity point cloud model after thinning are first stored. Then, the position coordinates of each discrete point are visualized on the computer screen, and discrete points with different curvature levels are marked with different colors to facilitate subsequent treatment judgment operations by staff.

[0198] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions 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 invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction, characterized in that, Includes the following steps: An initial oral cavity point cloud model is obtained by laser scanning of the oral cavity; nearest neighbor search is performed on the initial oral cavity point cloud model with several search radii to obtain the curvature ratio change curve of each search radius; The target curvature threshold is determined based on the fluctuation of each of the curvature percentage change curves; The target curvature threshold is used to classify all curvature values ​​corresponding to the target search radius to obtain various curvature levels. Consistency analysis is performed on the regional features of the point cloud regions before and after curvature expansion corresponding to several selected first curvature levels to update the point cloud regions of the first curvature levels, thereby obtaining the updated point cloud regions for each first curvature level; wherein, the first curvature level is a curvature level whose curvature range contains curvature values ​​greater than the curvature threshold. A first merging bias value is determined based on the spatial values ​​and distribution range proportions of the selected point cloud regions of several second curvature levels, in order to determine whether the second curvature level is updated to a matching curvature level. If updated, the point cloud regions corresponding to the matching curvature level are obtained. Wherein, the second curvature level is a curvature level with a level lower than a preset level. The size of the thinned voxels is determined based on the spatial size and level of the updated point cloud region for each first curvature level, the point cloud regions corresponding to each matched curvature level, and the point cloud regions of the remaining curvature levels other than the first curvature level and the matched curvature level. This is used to perform thinning processing on the point cloud regions of the corresponding curvature levels, resulting in a thinned oral cavity point cloud model.

2. The method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction according to claim 1, characterized in that, The step of performing nearest neighbor searches on the initial oral cavity point cloud model using several search radii to obtain the curvature percentage change curve for each search radius includes: For any search radius, a nearest neighbor search is performed on the initial oral cavity point cloud model using the search radius to obtain the set of nearest neighbors for each discrete point; Based on the nearest neighbor set, a covariance matrix is ​​constructed using the covariance calculation method; the eigenvalues ​​of the covariance matrix are obtained, and the curvature value of each discrete point under the search radius is obtained based on the eigenvalues; Determine the first proportion of the number of discrete points corresponding to each curvature value in the total number of discrete points. Based on each curvature value and the first proportion corresponding to each curvature value, construct the curvature proportion change curve of the search radius.

3. The method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction according to claim 1, characterized in that, The step of determining the target curvature threshold based on the fluctuation of each of the curvature percentage change curves includes: Determine the minimum and maximum points in each of the curvature percentage change curves, and segment the curvature percentage change curves using the minimum value as the segmentation point to obtain each initial curvature segment. For any initial curvature segment and any adjacent initial curvature segment, the second merging bias value is determined based on the curvature difference between the maxima in the two adjacent initial curvature segments. The initial curvature segments are merged and analyzed using the second merging bias value to obtain the merged curvature segments. The curvature values ​​corresponding to the two endpoints of each merged curvature segment are determined as suspected curvature thresholds, thus obtaining each suspected curvature threshold corresponding to each search radius; The selection conformity is determined based on the second proportion of the occurrence frequency of each suspected curvature threshold in the total number of curvature proportion change curves and the first proportion of the number of discrete points corresponding to different search radii in the total number of discrete points. By selecting the conformity, all the suspected curvature thresholds are filtered to obtain several target curvature thresholds.

4. The method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction according to claim 3, characterized in that, The step of determining the second merging bias value based on the curvature difference between the maxima in two adjacent initial curvature segments includes: The absolute value of the difference in curvature between the maxima in two adjacent initial curvature segments is determined to obtain the first merging bias factor; Obtain the absolute value of the difference between the first proportion values ​​corresponding to the curvature of the maximum points in two adjacent initial curvature segments, and use the ratio of the absolute value of the difference between the first proportion values ​​to the absolute value of the maximum proportion difference as the second merging bias factor; wherein, the absolute value of the maximum proportion difference is the maximum value among all the absolute values ​​of the difference between the first proportion values. The second merging bias value is determined by combining the first merging bias factor and the second merging bias factor.

5. The method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction according to claim 4, characterized in that, The step involves classifying all curvature values ​​corresponding to the target search radius using the target curvature threshold to obtain various curvature levels, including: Obtain the number of identical values ​​between the suspected curvature threshold and the target curvature threshold corresponding to each search radius, and take the search radius corresponding to the largest number of identical values ​​as the target search radius. The target curvature thresholds are arranged in order, and the curvature values ​​corresponding to the target search radius are graded using the arranged target curvature thresholds to obtain each curvature level.

6. The method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction according to claim 1, characterized in that, The process involves performing a consistency analysis on the regional features of the point cloud regions before and after curvature expansion corresponding to several selected first curvature levels to update the point cloud regions of the first curvature levels, resulting in the updated point cloud regions for each first curvature level, including: For any first curvature level, obtain any point cloud region of the first curvature level as the point cloud region before expansion; update any curvature value smaller than the minimum curvature value in the curvature range of the first curvature level in the initial oral cavity point cloud model to the curvature range of the first curvature level, and obtain the point cloud region of the updated curvature range as the expanded point cloud region. Determine the corresponding point of each boundary point of the point cloud region before expansion in the expanded point cloud region; determine the consistency index of the point cloud feature range before and after expansion based on the distance between each boundary point of the point cloud region before expansion and its corresponding point. The consistency index is obtained for several curvature expansions. The optimal curvature expansion is selected based on the difference between the consistency indexes between two adjacent expansions. The point cloud region corresponding to the optimal curvature expansion is used as the updated point cloud region of the first curvature level. The two adjacent expansions indicate that the next expansion is based on the previous expansion.

7. The method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction according to claim 6, characterized in that, Determining the corresponding point in the expanded point cloud region for each boundary point of the unexpanded point cloud region includes: Determine the center point of the point cloud region before expansion, and then obtain the line connecting each boundary point of the point cloud region before and after expansion to the center point; When the line connecting any boundary point of the point cloud region before expansion coincides with the line connecting a boundary point of the point cloud region after expansion, the boundary point of the point cloud region after expansion is taken as the corresponding point of the any boundary point.

8. The method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction according to claim 6, characterized in that, The step of determining the consistency index of the point cloud feature range before and after expansion based on the distance between each boundary point of the point cloud region before expansion and its corresponding point includes: A distance fluctuation curve is constructed using the boundary point number as the x-axis and the distance of each boundary point as the y-axis. The y-axis data of the distance fluctuation curve is then subjected to dimensionality reduction segmentation to obtain each dimensionality reduction segment. Based on the ratio of the maximum number of curve data points in all dimensionality reduction segments to the total number of boundary points in the expanded point cloud region, and the distance between the center points of two adjacent dimensionality reduction segments, the consistency index of the point cloud feature range before and after expansion is determined.

9. The method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction according to claim 1, characterized in that, The determination of the first merging bias value based on the spatial values ​​and distribution range proportions of the selected point cloud regions of several second curvature levels includes: For any second curvature level, the point cloud regions of other curvature levels that are adjacent to the point cloud region of the second curvature level and have a higher level than the second curvature level are used as the comparison point cloud regions. Determine the spatial value of the point cloud region at the second curvature level, and then determine the ratio of the number of discrete points in the point cloud region at the second curvature level to the number of discrete points in each comparative point cloud region. Based on the spatial values ​​and the respective ratios, a first merging bias value is determined between the second curvature level and each of the other curvature levels; wherein the spatial values ​​are negatively correlated with the first merging bias value, and the ratios are positively correlated with the first merging bias value.

10. The method for three-dimensional scanning and data reconstruction of the oral cavity based on laser diffraction according to claim 1, characterized in that, The process of determining the thinned voxel size based on the spatial size and level of the updated point cloud region for each first curvature level, the point cloud regions corresponding to each matched curvature level, and the point cloud regions for the remaining curvature levels other than the first and matched curvature levels includes: For any undetermined curvature level, determine the ratio of the level of the undetermined curvature level to the maximum level; wherein, the undetermined curvature level is the first curvature level, the matched curvature level, or the remaining curvature levels other than the first curvature level and the matched curvature level; Calculate the product of the spatial value and the level ratio of the undetermined curvature level, and perform negative correlation normalization on the product of the spatial value and the level ratio to obtain the size correction coefficient of the undetermined curvature level; The initial sludge volumetric size is corrected using the size correction factor to determine the sludge volumetric size of the undetermined curvature level.

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