Method for analyzing degree of fit between complete edentulous jaw false tooth and titanium alloy abutment

Abstract

The invention relates to the technical field of point cloud matching, in particular to a full-mouth edentulous jaw false tooth and titanium alloy abutment fitness analysis method, which comprises the following steps: acquiring a point cloud set of the full-mouth edentulous jaw false tooth and the inner surface of the oral cavity; marking points in the point cloud set; determining a point cloud space of each point cloud set; dividing the point cloud space to obtain a plurality of voxels; the Gaussian curvature of each point is obtained; analyzing the distribution difference of the points in the different point cloud sets in the point cloud space, obtaining the curvature space similarity between the points in the different point cloud sets in combination with the Gaussian curvature, and obtaining the inter-point difference between the points in the different point cloud sets on the basis of the distance between the points; matching the points in the two point cloud sets to obtain an objective function of an ICP algorithm, and obtaining the degree of fit between the complete edentulous jaw false tooth and the titanium alloy abutment. The invention aims to improve the accuracy of the fitness analysis of the complete edentulous jaw false tooth and the titanium alloy abutment.

Description

Technical Field

[0001] The present application relates to the technical field of point cloud matching, and in particular to a method for analyzing the fit between a full-mouth edentulous denture and a titanium alloy abutment. Background Art

[0002] The abutment is the key part that connects the implant and the restoration. The abutment not only carries the retention and stability of the restoration, but also affects the aesthetics and function of the implant. Titanium alloy abutments have good biocompatibility and mechanical properties, can reduce stress concentration in bone tissue, and assist users in restoring chewing function. They are currently the most widely used abutment type. However, due to differences in oral conditions and tissue structures of users, in order to ensure a good user experience during the customization process, the compatibility of titanium alloy abutments and edentulous dentures must meet certain standards.

[0003] At present, the evaluation of the fit between titanium alloy abutments and edentulous dentures is divided into parameter accuracy measurement, software detection, visual analysis and other methods. Among them, ICP (Iterative Closest Point) matching technology evaluates the fit between full-mouth edentulous dentures and titanium alloy abutments by analyzing the matching degree of point cloud data between them. It is a non-loss, non-contact fit evaluation method. However, when analyzing the point cloud data between full-mouth edentulous dentures and titanium alloy abutments, ICP matching technology tends to ignore the structural characteristics of the denture and the titanium alloy abutment, and only selects the matching point cloud data points by the size of the Euclidean distance. This method not only has a slow processing speed but also loses some structural detail features, affecting the analysis results of the fit between full-mouth edentulous dentures and titanium alloy abutments. Summary of the invention

[0004] In view of the above, it is necessary to provide a method for analyzing the fit between full-mouth edentulous dentures and titanium alloy abutments to solve the above problems.

[0005] An embodiment of the present application provides a method for analyzing the fit between a complete edentulous denture and a titanium alloy abutment, the method comprising: The set of point cloud data of the full-mouth edentulous denture and the set of point cloud data of the inner surface of the oral cavity are respectively recorded as point cloud set P and point cloud set Q; Cluster the points in each point cloud set, and mark the points in the point cloud set according to the density of each point in the point cloud set; Based on the coordinates of all points in each point cloud set, the point cloud space of each point cloud set is determined; the point cloud space is divided to obtain a number of voxels; based on the surface distribution of the points in each voxel, the Gaussian curvature of each point is obtained; the distribution differences of points in different point cloud sets in the point cloud space are analyzed, and the curvature space similarity between points in different point cloud sets is obtained in combination with the Gaussian curvature, and the inter-point difference between points in different point cloud sets is obtained based on the distance between the points; According to the marking results of each point in the point cloud set P, matching points are selected from the point cloud set Q to obtain the objective function of the ICP algorithm and the fit between the edentulous denture and the titanium alloy abutment.

[0006] The step of marking the points in the point cloud set is as follows: A clustering algorithm is used for all points in the point cloud set P to obtain the local density of each point; a threshold segmentation algorithm is used to obtain a first segmentation threshold based on the local density of all points in the point cloud set P, and points with a local density greater than the first segmentation threshold are marked as resin gingival points, and marked points with a local density less than the first segmentation threshold are marked as resin perforation points; The local density and the second segmentation threshold of each point in the point cloud set Q are obtained by the same method as the point cloud set P. Points with local density greater than the second segmentation threshold are marked as titanium alloy abutment points, and points with local density less than the second segmentation threshold are marked as oral tissue points.

[0007] The point cloud space of each point cloud set is specifically a space composed of the interval range of all points in each point cloud set on each coordinate axis.

[0008] The point cloud space is divided to obtain a number of voxels, specifically: The minimum interval length of each point cloud set in all coordinate axes is obtained, and the ratio between the minimum interval length and a preset value greater than zero is rounded as the side length of the voxel to segment the point cloud space.

[0009] The Gaussian curvature of each point is obtained by performing quadratic surface fitting on all points in the voxel.

[0010] The curvature space similarity between points in different point cloud sets is obtained as follows: For each point cloud set, obtain the minimum distance between each point and the boundary in each coordinate axis direction in the point cloud space, and take the cumulative sum of all the minimum distances of each point as the minimum boundary distance of each point; The difference in the minimum boundary distance between the points in the point cloud set P and the points in the point cloud set Q and the difference in Gaussian curvature are calculated, and the two differences are combined to obtain the curvature space similarity between the points in the point cloud set P and the points in the point cloud set Q.

[0011] The process of obtaining the minimum boundary distance of each point is as follows: The minimum boundary distance of the i-th point in the point cloud set P is recorded as , its formula form is: ;in, Represents the absolute value of the difference between the X coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the X coordinate axis, Represents the absolute value of the difference between the X coordinate of the i-th point of the point cloud set P and the maximum value of the point cloud space on the X coordinate axis, Represents the absolute value of the difference between the Y coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the Y coordinate axis, Represents the absolute value of the difference between the Y coordinate of the i-th point in the point cloud set P and the maximum value of the point cloud space on the Y coordinate axis, Represents the absolute value of the difference between the Z coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the Z coordinate axis; Represents the absolute value of the difference between the Z coordinate of the i-th point of the point cloud set P and the maximum value of the point cloud space on the Z coordinate axis, Represents the minimum function.

[0012] The specific steps of obtaining the inter-point differences between points in different point cloud sets are as follows: The distance between each point in the point cloud set P and each point in the point cloud set Q is obtained, and is merged with the curvature space similarity to obtain the difference between each point in the point cloud set P and each point in the point cloud set Q.

[0013] The process of selecting matching points is specifically as follows: If the i-th point in the point cloud set P is marked as a resin perforation point, find the point with the smallest difference from the i-th point among all the points marked as titanium alloy abutment points in the point cloud set Q as the matching point of the i-th point in the point cloud set P; If the i-th point is marked as a resin gingival point, the point with the smallest difference between the i-th point and all the points marked as oral tissue points in the point cloud set Q is found as the matching point of the i-th point in the point cloud set P.

[0014] The objective function of the ICP algorithm is obtained to obtain the degree of fit between the full-mouth edentulous denture and the titanium alloy abutment, including: The objective function of the ICP algorithm is: ;in, is the matching objective function of point cloud set P and point cloud set Q; It represents the parameter value at which a function takes its minimum value in its domain; Indicate point With point The difference between the points; is the kth point of the point cloud set P after rotation and translation transformation; For point Matching points in the point cloud set Q; represents the kth point of the point cloud set P before the rotation and translation transformation; M represents the total number of points in the point cloud set P; R is the rotation matrix; t is the translation vector; The negative correlation mapping result of the objective function value finally obtained is used as the fit between the full-mouth edentulous denture and the titanium alloy abutment.

[0015] This application has at least the following beneficial effects: The present application first marks the points in the two point cloud sets respectively, so that when the points are subsequently matched, the search is performed based on the marking results, without having to search all the points, so that the calculation efficiency of the ICP algorithm is improved; the curvature space similarity between the points in the two point cloud sets is obtained, which has the beneficial effect of characterizing the similarity characteristics between the point cloud data of the full-mouth edentulous denture and the titanium alloy base according to the curvature characteristics and position characteristics of the two, fully considering the detailed characteristics of the distribution of the point cloud data, so that the accuracy of the subsequent matching point pairs is improved, and further combining the distance to obtain the difference between the points, which has the beneficial effect of making the objective function of the subsequent ICP algorithm better characterize the similarity characteristics of the distribution between the point cloud data, making the alignment result more accurate, thereby improving the accuracy of the fitness analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a method for analyzing the fit between a complete edentulous denture and a titanium alloy abutment provided in this application; Figure 2 This is the marking result diagram of the point cloud set P and the point cloud set Q provided in this application. DETAILED DESCRIPTION

[0017] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0019] It should also be noted that the terms "first" and "second" in this application and its drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The method disclosed in the embodiments of the present application or the method shown in the flow chart includes one or more steps for implementing the method. Without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0020] 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.

[0021] This application proposes a method for analyzing the fit between a full-mouth edentulous denture and a titanium alloy abutment, which is applied in the field of point cloud matching technology. Figure 1 , the method comprises the following steps: S1: The set of point cloud data of the full-mouth edentulous denture and the set of point cloud data of the inner surface of the oral cavity are recorded as point cloud set P and point cloud set Q respectively.

[0022] Full-mouth dental implants generally require multiple abutments, and the specific number needs to be determined after evaluation by a professional oral implantologist. Common full-mouth implant plans may include All-on-4, All-on-6, etc., which use 4 or 6 abutments to support the restoration of a whole row of teeth. The abutment area of ​​the full-mouth edentulous denture to be installed is concave, and the surface of each titanium alloy abutment in the mouth is convex. The concave areas of the full-mouth edentulous denture and the convex surfaces of each titanium alloy abutment need to have a high degree of adaptability to avoid loosening and falling off of the denture.

[0023] The concave surface of the full-mouth edentulous denture and the intraoral surface of the implanted titanium alloy base are scanned by an intraoral scanner to obtain point cloud data of the concave surface and the intraoral surface of the full-mouth edentulous denture. The point cloud set of the full-mouth edentulous denture is recorded as point cloud set P; the point cloud set of the intraoral surface is recorded as point cloud set Q. The point cloud data is denoised by a statistical filtering method. Statistical filtering is a well-known technology, and the specific process is not repeated here.

[0024] S2: Cluster the points in each point cloud set and mark the points in the point cloud set according to the density of each point in the point cloud set.

[0025] The traditional ICP algorithm needs to calculate the Euclidean distance between all points in the point cloud set P and the point cloud set Q at each iteration, and take the two points with the closest Euclidean distance in the two point cloud sets as matching point pairs, which will generate a lot of calculation requirements and take too long.

[0026] In the concave surface of the full-mouth edentulous denture, there is a resin gingival area composed of red resin material and a resin perforated area that appears to match the titanium alloy base. Among them, the resin gingival surface directly contacts the user's oral tissue and has a relatively smooth surface. Therefore, the resin gingival area has a relatively high reflectivity to the laser, and the point cloud of the resin gingival area is relatively dense in the point cloud data; and in order to make the full-mouth edentulous denture contact with the titanium alloy base more firmly, the surface of the resin perforated area is usually roughened, so the reflectivity of the resin perforated area to the laser is relatively low, and the point cloud of the resin perforated area in the point cloud data will be sparse.

[0027] Based on the above analysis, the three-dimensional coordinates of all points in the point cloud set P are used as the input of the Density Peak Clustering (DCP) algorithm. In this embodiment, the cutoff distance is set to a value that makes the number of neighbor points of each point in the point cloud 2% of all points, and the local density of each point is obtained. The local density of all points in the point cloud set P is used as the input of the Otsu threshold segmentation algorithm to obtain the first segmentation threshold, and the points with local density greater than the first segmentation threshold are marked as resin gingival points, and the points with local density less than the first segmentation threshold are marked as resin perforation points.

[0028] When scanning the inner surface of the oral cavity, the titanium alloy abutment is usually implanted in the user's oral cavity, so the point cloud data of the inner surface of the oral cavity includes the point cloud data of the titanium alloy abutment area and the point cloud data of the real oral tissue. The surface of the real oral tissue is relatively soft, and when the laser reaches the tissue surface, most of it will be absorbed or scattered, so the point cloud at the real oral tissue will be relatively sparse; relative to the real oral tissue, the reflectivity of the titanium alloy abutment surface to the laser will be greater than the reflectivity of the real oral tissue to the laser, so in the point cloud data of the inner surface of the oral cavity, the point cloud at the titanium alloy abutment will be denser than the point cloud at the real oral tissue.

[0029] Based on the above analysis, the point cloud set Q is clustered using the same clustering method as the point cloud set P to obtain the local density of each point. The local density of all points in the point cloud set Q is used as the input of the Otsu threshold segmentation algorithm to obtain the second segmentation threshold, and the points with local density greater than the second segmentation threshold are marked as titanium alloy abutment points, and the points with local density less than the second segmentation threshold are marked as oral tissue points.

[0030] Among them, the marking results of point cloud set P and point cloud set Q are shown in the figure below: Figure 2 shown.

[0031] S3: Based on the coordinates of all points in each point cloud set, determine the point cloud space of each point cloud set; divide the point cloud space to obtain a number of voxels; based on the surface distribution of the points in each voxel, obtain the Gaussian curvature of each point; analyze the distribution differences of points in different point cloud sets in the point cloud space, combine the Gaussian curvature to obtain the curvature space similarity between points in different point cloud sets, and obtain the point-to-point difference between points in different point cloud sets based on the distance between points.

[0032] When searching for matching point pairs, matching points are searched for resin gingival points in point cloud set P from oral tissue points in point cloud set Q, and matching points are searched for resin perforation points in point cloud set P from titanium alloy abutment points in point cloud set Q. Matching point pairs are searched in the above regional search method, which reduces the computational complexity of the ICP algorithm.

[0033] When searching for matching point pairs, the traditional ICP algorithm only searches based on the Euclidean distance, resulting in low accuracy and efficiency. When matching points in point cloud set P and point cloud set Q, due to the similar shapes between the bases, it is easy to match a point in a hole with a point on another unmatched base when matching feature points.

[0034] Obtain the minimum and maximum values ​​of all points in the point cloud set P in the direction of the coordinate axis to determine the point cloud space of the point cloud set P. It should be understood that the minimum and maximum values ​​of all points in the point cloud set P in the direction of the coordinate axis constitute the interval range of the point cloud set P in each coordinate axis direction, and the space composed of all interval ranges is regarded as the point cloud space of the point cloud set P.

[0035] Then the point cloud space is evenly divided into voxels of size s×s×s. In this embodiment, s is , A is the minimum side length of the point cloud space, is a rounding function used to round the input data to an integer. 20 is the preset value, and the implementer can select a preset value greater than 0 according to the actual situation. The points in each voxel are fitted with a quadratic surface to obtain the Gaussian curvature of each point in the point cloud set P. The point cloud set Q is voxelized using the same method, and the Gaussian curvature of each point in the point cloud set Q is calculated.

[0036] Since the curved surface of each position of the concave surface of the full-mouth edentulous denture is not flat, the Gaussian curvature in different positions is different. The higher the degree of fit between the full-mouth edentulous denture and the titanium alloy base, the closer the curved surface shapes of the two point cloud spaces are, and the closer the Gaussian curvatures of the matching point pairs are. When the Gaussian curvature of a point in the point cloud set P is close to the Gaussian curvature of a point in the point cloud set Q, it means that the two points are more likely to be a matching point pair. In addition, when two points are a matching point pair, the distance between the two points and the boundary of the point cloud space in the two point cloud spaces will be closer.

[0037] Based on this, the distribution differences of points in different point cloud sets in the point cloud space are analyzed, and combined with Gaussian curvature, the curvature space similarity between points in different point cloud sets is obtained: the minimum distance of each point from the boundary in each coordinate axis direction in the point cloud space is obtained, and the cumulative sum of all the minimum distances of each point is taken as the minimum boundary distance of each point; the difference in the minimum boundary distance between the points in the point cloud set P and the points in the point cloud set Q and the difference between the Gaussian curvatures are calculated to obtain the curvature space similarity between the points.

[0038] In this embodiment, the minimum boundary distance of the i-th point in the point cloud set P is recorded as , its formula form is: ;in, Represents the absolute value of the difference between the X coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the X coordinate axis, Represents the absolute value of the difference between the X coordinate of the i-th point of the point cloud set P and the maximum value of the point cloud space on the X coordinate axis, Represents the absolute value of the difference between the Y coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the Y coordinate axis, Represents the absolute value of the difference between the Y coordinate of the i-th point in the point cloud set P and the maximum value of the point cloud space on the Y coordinate axis, Represents the absolute value of the difference between the Z coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the Z coordinate axis; Represents the absolute value of the difference between the Z coordinate of the i-th point of the point cloud set P and the maximum value of the point cloud space on the Z coordinate axis, Represents the minimum function.

[0039] It should be understood that , , It represents the minimum distance between the i-th point in the point cloud set P and the boundary of the point cloud space in each coordinate axis direction.

[0040] The minimum boundary distance and Gaussian curvature of the i-th point in the point cloud set P are respectively denoted as , , the minimum boundary distance and Gaussian curvature of the jth point in the point cloud set Q are respectively recorded as , , the curvature space similarity between the i-th point in the point cloud set P and the j-th point in the point cloud set Q is recorded as , its formula form is: .

[0041] It should be understood that The smaller the value of , the closer the Gaussian curvature of the i-th point in the point cloud set P is to the j-th point in the point cloud set Q, and the more likely these two points are a matching point pair. The smaller the value of , the closer the minimum boundary distance between the i-th point in the point cloud set P and the j-th point in the point cloud set Q is, and the higher the probability of matching these two points is.

[0042] Furthermore, based on the distance between points, the inter-point difference between points in different point cloud sets is obtained to characterize the degree of mismatch between points in two point cloud sets; the inter-point difference between the i-th point of point cloud set P and the j-th point of point cloud set Q is recorded as , its formula form is: ; In the formula, Represents the difference between the i-th point in the point cloud set P and the j-th point in the point cloud set Q; Represents the Euclidean distance between the i-th point in the point cloud set P and the j-th point in the point cloud set Q.

[0043] It should be understood that The smaller the value of , the closer the distance between the i-th point in the point cloud set P and the j-th point in the point cloud set Q is, and the more the two points match. The smaller the value of , the closer the curvature and spatial position of the i-th point in the point cloud set P and the j-th point in the point cloud set Q are, and the more the two points match. The smaller the value of , the smaller the difference between the i-th point in the point cloud set P and the j-th point in the point cloud set Q, and the more the two points match.

[0044] S4: According to the marking result of each point in the point cloud set P, matching points are selected from the point cloud set Q, the objective function of the ICP algorithm is obtained, and the fit between the full-mouth edentulous denture and the titanium alloy abutment is obtained.

[0045] After all points in the point cloud set P are transformed by rotation and translation, the resulting points are recorded as , represents the kth point in the point cloud set P, R is the rotation matrix, and t is the translation vector; in this embodiment, two point cloud sets are used as the input of the random sample consensus (RANSAC) algorithm to obtain the initial rotation matrix R and the initial translation vector t. The rotation and translation transformation does not affect the spatial characteristics of each point in the point cloud set. In the point cloud set P after rotation and translation, if the i-th point is a resin perforation point, the point with the smallest inter-point difference with the i-th point is found from all titanium alloy abutment points in the point cloud set Q as the matching point of the i-th point in the point cloud set P; if the i-th point is a resin gingival point, the point with the smallest inter-point difference with the i-th point is found from all oral tissue points in the point cloud set Q as the matching point of the i-th point in the point cloud set P. And so on, it is the matching point of all points in the point cloud set P in the point cloud set Q.

[0046] The objective function is constructed based on the matching point pairs to determine the fit between the full-mouth edentulous denture and the titanium alloy abutment. The objective function is constructed: ;in, is the matching objective function of point cloud set P and point cloud set Q; It represents the parameter value at which a function takes its minimum value in its domain; Indicate point With point The difference between the points; is the kth point of the point cloud set P after rotation and translation transformation; For point Matching points in the point cloud set Q; represents the kth point of the point cloud set P before the rotation and translation transformation; M represents the total number of points in the point cloud set P.

[0047] Use the singular value decomposition method to solve the values ​​of R and t so that the objective function Using the latest R and t, continue to transform the point cloud set P to obtain a new point cloud set . The new point cloud is collected Follow the above steps to solve the values ​​of R and t again with the point cloud set Q so that the objective function The value of is the smallest. Repeat this process until the value of the objective function no longer decreases.

[0048] The inverse value of the objective function finally obtained can reflect the degree of fit between the two point cloud spaces, that is, the degree of fit between the full-mouth edentulous denture and the titanium alloy abutment. The smaller the value of the objective function, the larger its inverse value, which means that the degree of fit between the full-mouth edentulous denture and the titanium alloy abutment is higher.

[0049] The present application provides a method for analyzing the compatibility of a complete edentulous denture and a titanium alloy base, the method comprising: firstly marking the points in two point cloud sets respectively, so that when matching between points subsequently, a search is performed based on the marking results, without searching all the points, so that the calculation efficiency of the ICP algorithm is improved; the curvature space similarity between the points in the two point cloud sets is obtained, and the beneficial effect is that the similarity characteristics between the point cloud data of the complete edentulous denture and the titanium alloy base are characterized according to the curvature characteristics and position characteristics of the two, and the detailed characteristics of the distribution of the point cloud data are fully considered, so that the accuracy of the subsequent matching point pairs is improved, and the distance is further combined to obtain the difference between the points, and the beneficial effect is that the objective function of the subsequent ICP algorithm better characterizes the similarity characteristics of the distribution between the point cloud data, so that the matching result is more accurate, thereby improving the accuracy of the fitness analysis.

[0050] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two continuous operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0051] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions 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 method for analyzing the fit between a complete edentulous denture and a titanium alloy abutment, characterized in that: The method comprises the following steps: The set of point cloud data of the full-mouth edentulous denture and the set of point cloud data of the inner surface of the oral cavity are respectively recorded as point cloud set P and point cloud set Q; Cluster the points in each point cloud set, and mark the points in the point cloud set according to the density of each point in the point cloud set; Based on the coordinates of all points in each point cloud set, the point cloud space of each point cloud set is determined; the point cloud space is divided to obtain a number of voxels; based on the surface distribution of the points in each voxel, the Gaussian curvature of each point is obtained; the distribution differences of points in different point cloud sets in the point cloud space are analyzed, and the curvature space similarity between points in different point cloud sets is obtained in combination with the Gaussian curvature, and the inter-point difference between points in different point cloud sets is obtained based on the distance between the points; According to the marking results of each point in the point cloud set P, matching points are selected from the point cloud set Q to obtain the objective function of the ICP algorithm and the fit between the edentulous denture and the titanium alloy abutment.

2. The method for analyzing the fit between a complete edentulous denture and a titanium alloy abutment according to claim 1, characterized in that: The marking of points in the point cloud set is specifically as follows: A clustering algorithm is used for all points in the point cloud set P to obtain the local density of each point; a threshold segmentation algorithm is used to obtain a first segmentation threshold based on the local density of all points in the point cloud set P, and points with a local density greater than the first segmentation threshold are marked as resin gingival points, and marked points with a local density less than the first segmentation threshold are marked as resin perforation points; The local density and the second segmentation threshold of each point in the point cloud set Q are obtained by the same method as the point cloud set P. Points with local density greater than the second segmentation threshold are marked as titanium alloy abutment points, and points with local density less than the second segmentation threshold are marked as oral tissue points.

3. The method for analyzing the fit between a complete edentulous denture and a titanium alloy abutment according to claim 1, characterized in that: The point cloud space of each point cloud set is specifically a space composed of the interval range of all points in each point cloud set on each coordinate axis.

4. The method for analyzing the fit between a complete edentulous denture and a titanium alloy abutment according to claim 1, characterized in that: The point cloud space is divided to obtain a number of voxels, specifically: The minimum interval length of each point cloud set in all coordinate axes is obtained, and the ratio between the minimum interval length and a preset value greater than zero is rounded as the side length of the voxel to segment the point cloud space.

5. The method for analyzing the fit between a complete edentulous denture and a titanium alloy abutment according to claim 1, characterized in that: The Gaussian curvature of each point is obtained by performing quadratic surface fitting on all points in the voxel.

6. The method for analyzing the fit between a complete edentulous denture and a titanium alloy abutment according to claim 1, characterized in that: The curvature space similarity between points in different point cloud sets is obtained as follows: For each point cloud set, obtain the minimum distance between each point and the boundary in each coordinate axis direction in the point cloud space, and take the cumulative sum of all the minimum distances of each point as the minimum boundary distance of each point; The difference in the minimum boundary distance between the points in the point cloud set P and the points in the point cloud set Q and the difference in Gaussian curvature are calculated, and the two differences are combined to obtain the curvature space similarity between the points in the point cloud set P and the points in the point cloud set Q.

7. The method for analyzing the fit between a complete edentulous denture and a titanium alloy abutment according to claim 6, characterized in that: The process of obtaining the minimum boundary distance of each point is as follows: The minimum boundary distance of the i-th point in the point cloud set P is recorded as , its formula form is: ;in, Represents the absolute value of the difference between the X coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the X coordinate axis, Represents the absolute value of the difference between the X coordinate of the i-th point of the point cloud set P and the maximum value of the point cloud space on the X coordinate axis, Represents the absolute value of the difference between the Y coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the Y coordinate axis, Represents the absolute value of the difference between the Y coordinate of the i-th point in the point cloud set P and the maximum value of the point cloud space on the Y coordinate axis, Represents the absolute value of the difference between the Z coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the Z coordinate axis; Represents the absolute value of the difference between the Z coordinate of the i-th point of the point cloud set P and the maximum value of the point cloud space on the Z coordinate axis, Represents the minimum function.

8. The method for analyzing the fit between a complete edentulous denture and a titanium alloy abutment according to claim 1, characterized in that: The specific steps of obtaining the inter-point differences between points in different point cloud sets are: The distance between each point in the point cloud set P and each point in the point cloud set Q is obtained, and is merged with the curvature space similarity to obtain the difference between each point in the point cloud set P and each point in the point cloud set Q.

9. The method for analyzing the fit between a complete edentulous denture and a titanium alloy abutment according to claim 1, characterized in that: The process of selecting matching points is specifically as follows: If the i-th point in the point cloud set P is marked as a resin perforation point, find the point with the smallest difference from the i-th point among all the points marked as titanium alloy abutment points in the point cloud set Q as the matching point of the i-th point in the point cloud set P; If the i-th point is marked as a resin gingival point, the point with the smallest difference between the i-th point and all the points marked as oral tissue points in the point cloud set Q is found as the matching point of the i-th point in the point cloud set P.

10. The method for analyzing the fit between a complete edentulous denture and a titanium alloy abutment according to claim 1, characterized in that: The objective function of the ICP algorithm is obtained to obtain the degree of fit between the full-mouth edentulous denture and the titanium alloy abutment, including: The objective function of the ICP algorithm is: ;in, is the matching objective function of point cloud set P and point cloud set Q; It represents the parameter value at which a function takes its minimum value in its domain; Indicate point With point The difference between the points; is the kth point of the point cloud set P after rotation and translation transformation; For point Matching points in the point cloud set Q; represents the kth point of the point cloud set P before the rotation and translation transformation; M represents the total number of points in the point cloud set P; R is the rotation matrix; t is the translation vector; The negative correlation mapping result of the objective function value finally obtained is used as the fit between the full-mouth edentulous denture and the titanium alloy abutment.

Images