Subperiosteal implant manufacturing dimensional deviation optimization method and device

Abstract

The invention discloses a subperiosteal implant manufacturing dimensional deviation optimization method and device, and the method comprises the steps: obtaining the data of an optical scanner at a position, where a subperiosteal implant is to be placed, of a patient, and generating an initial three-dimensional model of the subperiosteal implant to be optimized; acquiring three-dimensional coordinate information of the initial three-dimensional model, and converting the three-dimensional coordinate information into point cloud data; grouping according to the attribute of each point cloud point in the point cloud data to obtain a plurality of point cloud point attribute groups; for each point cloud point attribute group, selecting a preset optimization algorithm to optimize the point cloud point attribute groups according to attributes corresponding to the point cloud point attribute groups to obtain optimized point cloud data, and generating an optimized three-dimensional model according to the optimized point cloud data; fitting the optimized three-dimensional model and the initial three-dimensional model, determining a deviation compensation margin, and performing deviation compensation on the initial three-dimensional model according to the deviation compensation margin to obtain a deviation optimized three-dimensional model; and optimizing the three-dimensional model according to the deviation to manufacture the subperiosteal implant.

Description

Technical Field

[0001] The invention relates to the technical field of subperiosteal implants, and in particular to a method and a device for optimizing the manufacturing dimension deviation of a subperiosteal implant. Background Art

[0002] Subperiosteal implants are a special oral implant technology. They are not implanted inside the jawbone, but are placed between the periosteum and the bone surface. This implantation method is mainly suitable for patients who cannot undergo traditional intraosseous implantation due to jaw atrophy, osteoporosis and other reasons. In oral treatment, the manufacturing size of subperiosteal implants needs to be customized according to the patient's oral condition. The existing method of manufacturing subperiosteal implants is mainly for medical staff to measure the size of the patient's implant with a vernier caliper; however, due to the different implant shapes corresponding to different patients, this vernier caliper contact measurement method will have measurement errors in some positions where the vernier caliper does not have enough space to reach, resulting in a deviation in the size of the final subperiosteal implant and the actual subperiosteal implant. When it is placed between the patient's periosteum and the bone surface, there will be a phenomenon of non-fitting. Therefore, there is an urgent need for a method to reduce the manufacturing size deviation of subperiosteal implants to improve the fit and stability of the subperiosteal implants after manufacturing, thereby improving the treatment effect of patients. Summary of the invention

[0003] The embodiment of the present invention provides a method and device for optimizing the manufacturing size deviation of a subperiosteal implant, which can reduce the manufacturing size deviation of the subperiosteal implant, improve the fit and stability of the subperiosteal implant after manufacturing, and improve the treatment effect of the patient.

[0004] An embodiment of the present invention provides a method for optimizing dimension deviation of a subperiosteal implant, comprising:

[0005] Acquiring optical scanner data of the patient's location where the subperiosteal implant is to be placed, and generating an initial three-dimensional model of the subperiosteal implant to be optimized based on the optical scanner data;

[0006] Acquire the three-dimensional coordinate information of each discrete point in the initial three-dimensional model, and convert the three-dimensional coordinate information of each discrete point into point cloud data; wherein the point cloud data includes a plurality of point cloud points;

[0007] Grouping each point cloud point in the point cloud data according to the attributes of each point cloud point in the point cloud data to obtain a number of point cloud point attribute groups; wherein the attributes include: density and point cloud point noise;

[0008] For each point cloud point attribute grouping, a preset optimization algorithm is selected according to the attributes corresponding to the point cloud point attribute grouping to optimize the point cloud point attribute grouping to obtain optimized point cloud data, and an optimized three-dimensional model is generated according to the optimized point cloud data;

[0009] Fitting the optimized three-dimensional model and the initial three-dimensional model, determining a deviation compensation margin, and using the deviation compensation margin to perform deviation compensation on the initial three-dimensional model to obtain a deviation-optimized three-dimensional model of the subperiosteal implant to be optimized;

[0010] The subperiosteal implant is manufactured by optimizing the three-dimensional model according to the deviation.

[0011] Furthermore, the fitting of the optimized three-dimensional model and the initial three-dimensional model to determine the deviation compensation margin includes:

[0012] Fitting the optimized three-dimensional model and the initial three-dimensional model to determine a size deviation cloud map;

[0013] Determine the deviation compensation allowance based on the dimensional deviation cloud diagram.

[0014] Furthermore, generating an optimized three-dimensional model according to the optimized point cloud data includes:

[0015] Generate mesh data according to the optimized point cloud data; wherein the mesh data includes: a plurality of triangular meshes and a plurality of polygonal meshes;

[0016] Smoothing the grid data to obtain grid data after first data preprocessing;

[0017] Filling holes in the grid data after the first data preprocessing to obtain grid data after the second data preprocessing;

[0018] Extracting geometric feature data of each grid in the grid data after the second data preprocessing, and simplifying the grid data after the second data preprocessing according to the geometric feature data to obtain grid data after the third data preprocessing;

[0019] An optimized three-dimensional model is generated according to the mesh data preprocessed by the third data.

[0020] Furthermore, the fitting of the optimized three-dimensional model and the initial three-dimensional model to determine the size deviation cloud map includes:

[0021] Aligning and fitting the optimized three-dimensional model and the initial three-dimensional model to obtain an alignment result graph of the optimized three-dimensional model and the initial three-dimensional model;

[0022] Extracting a plurality of error determination data groups from the alignment result graph; wherein each error determination data group includes a three-dimensional coordinate data obtained from the optimized three-dimensional model and a three-dimensional coordinate data obtained from the initial three-dimensional model; a distance threshold between the three-dimensional coordinate data obtained from the optimized three-dimensional model and the three-dimensional coordinate data obtained from the initial three-dimensional model is less than a preset distance threshold;

[0023] A dimensional deviation cloud diagram is generated based on a plurality of error determination data sets and an initial three-dimensional model.

[0024] Furthermore, the step of generating a dimension deviation cloud map based on a plurality of error determination data sets and an initial three-dimensional model includes:

[0025] Determine the dimensional deviation corresponding to each error determination data group according to each error determination data group;

[0026] Mapping the size deviation corresponding to each error determination data group to the color space through color mapping to obtain a color space mapping diagram;

[0027] The color space map is superimposed on the initial 3D model to generate a size deviation cloud map.

[0028] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0029] An embodiment of the present invention provides a device for optimizing the manufacturing size deviation of a subperiosteal implant, including: an initial three-dimensional model building module, an optimized three-dimensional model building module, and a deviation compensation optimization module;

[0030] The initial three-dimensional model building module is used to obtain optical scanner data of the patient's subperiosteal implant to be placed, and generate an initial three-dimensional model of the subperiosteal implant to be optimized based on the optical scanner data;

[0031] The optimized three-dimensional model construction module is used to obtain the three-dimensional coordinate information of each discrete point in the initial three-dimensional model, and convert the three-dimensional coordinate information of each discrete point into point cloud data; wherein the point cloud data includes a plurality of point cloud points; each point cloud point in the point cloud data is grouped according to the attributes of each point cloud point in the point cloud data to obtain a plurality of point cloud point attribute groups; wherein the attributes include: density and point cloud point noise; for each point cloud point attribute group, a preset optimization algorithm is selected according to the attributes corresponding to the point cloud point attribute group to optimize the point cloud point attribute group to obtain optimized point cloud data, and an optimized three-dimensional model is generated according to the optimized point cloud data;

[0032] The deviation compensation optimization module is used to fit the optimized three-dimensional model and the initial three-dimensional model, determine the deviation compensation margin, and use the deviation compensation margin to compensate the deviation of the initial three-dimensional model to obtain the deviation optimized three-dimensional model of the subperiosteal implant to be optimized; and manufacture the subperiosteal implant according to the deviation optimized three-dimensional model.

[0033] Furthermore, the fitting of the optimized three-dimensional model and the initial three-dimensional model to determine the deviation compensation margin includes:

[0034] Fitting the optimized three-dimensional model and the initial three-dimensional model to determine a size deviation cloud map;

[0035] Determine the deviation compensation allowance based on the dimensional deviation cloud diagram.

[0036] Furthermore, generating an optimized three-dimensional model according to the optimized point cloud data includes:

[0037] Generate mesh data according to the optimized point cloud data; wherein the mesh data includes: a plurality of triangular meshes and a plurality of polygonal meshes;

[0038] Smoothing the grid data to obtain grid data after first data preprocessing;

[0039] Filling holes in the grid data after the first data preprocessing to obtain grid data after the second data preprocessing;

[0040] Extracting geometric feature data of each grid in the grid data after the second data preprocessing, and simplifying the grid data after the second data preprocessing according to the geometric feature data to obtain grid data after the third data preprocessing;

[0041] An optimized three-dimensional model is generated according to the mesh data preprocessed by the third data.

[0042] Furthermore, the fitting of the optimized three-dimensional model and the initial three-dimensional model to determine the size deviation cloud map includes:

[0043] Aligning and fitting the optimized three-dimensional model and the initial three-dimensional model to obtain an alignment result graph of the optimized three-dimensional model and the initial three-dimensional model;

[0044] Extracting a plurality of error determination data groups from the alignment result graph; wherein each error determination data group includes a three-dimensional coordinate data obtained from the optimized three-dimensional model and a three-dimensional coordinate data obtained from the initial three-dimensional model; a distance threshold between the three-dimensional coordinate data obtained from the optimized three-dimensional model and the three-dimensional coordinate data obtained from the initial three-dimensional model is less than a preset distance threshold;

[0045] A dimensional deviation cloud diagram is generated based on a plurality of error determination data sets and an initial three-dimensional model.

[0046] Furthermore, the step of generating a dimension deviation cloud map based on a plurality of error determination data sets and an initial three-dimensional model includes:

[0047] Determine the dimensional deviation corresponding to each error determination data group according to each error determination data group;

[0048] Mapping the size deviation corresponding to each error determination data group to the color space through color mapping to obtain a color space mapping diagram;

[0049] The color space map is superimposed on the initial 3D model to generate a size deviation cloud map.

[0050] The following beneficial effects are achieved by implementing the present invention:

[0051] The present invention provides a method and device for optimizing the manufacturing size deviation of a subperiosteal implant. The method obtains optical scanner data of a patient's subperiosteal implant to be placed, and generates an initial three-dimensional model of the subperiosteal implant to be optimized based on the optical scanner data. The acquisition of the optical scanner data avoids the problem of large errors caused by manual vernier caliper measurement, and improves the accuracy of data measurement of the subperiosteal implant to be placed.

[0052] Furthermore, three-dimensional coordinate data of each discrete point in the initial three-dimensional model is obtained, and then the obtained three-dimensional coordinate data is converted into point cloud data, and after attribute grouping processing is performed on the point cloud data, optimized point cloud data is obtained, and then an optimized three-dimensional model is generated according to the optimized point cloud data; the optimized three-dimensional model and the initial three-dimensional model are fitted, the deviation compensation margin is determined, and the initial three-dimensional model is deviation-compensated by the deviation compensation margin to obtain a deviation-optimized three-dimensional model of the subperiosteal implant to be optimized; after obtaining the optimized three-dimensional model by further optimizing the model on the basis of the initial three-dimensional model, the deviation compensation margin between the optimized three-dimensional model and the initial three-dimensional model is determined, and the initial three-dimensional model is deviation-compensated by the deviation compensation margin to achieve secondary deviation optimization of the initial three-dimensional model, thereby further reducing the size deviation problem in the initial three-dimensional model generated by the optical scanner data, and the subperiosteal implant manufactured by the deviation-optimized three-dimensional model obtained after the secondary optimization can better fit the subperiosteal implant to be placed, thereby improving the fit and stability of the subperiosteal implant after manufacturing, thereby improving the treatment effect of the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a method for optimizing dimension deviation in manufacturing a subperiosteal implant provided by an embodiment of the present invention.

[0054] Figure 2It is a structural schematic diagram of a device for optimizing dimension deviation in manufacturing a subperiosteal implant provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0058] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0059] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0060] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0061] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0062] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0063] like Figure 1 FIG. 1 is a method for optimizing dimension deviation of a subperiosteal implant according to an embodiment of the present invention, comprising:

[0064] Step S1: acquiring optical scanner data of a patient's location where a subperiosteal implant is to be placed, and generating an initial three-dimensional model of the subperiosteal implant to be optimized based on the optical scanner data;

[0065] Step S2: obtaining three-dimensional coordinate information of each discrete point in the initial three-dimensional model, and converting the three-dimensional coordinate information of each discrete point into point cloud data; wherein the point cloud data includes a plurality of point cloud points;

[0066] Step S3: grouping each point cloud point in the point cloud data according to the attributes of each point cloud point in the point cloud data to obtain a plurality of point cloud point attribute groups; wherein the attributes include: density and point cloud point noise;

[0067] Step S4: for each point cloud point attribute group, a preset optimization algorithm is selected according to the attribute corresponding to the point cloud point attribute group to optimize the point cloud point attribute group to obtain optimized point cloud data, and an optimized three-dimensional model is generated according to the optimized point cloud data;

[0068] Step S5: fitting the optimized three-dimensional model and the initial three-dimensional model, determining the deviation compensation margin, and using the deviation compensation margin to perform deviation compensation on the initial three-dimensional model to obtain a deviation-optimized three-dimensional model of the subperiosteal implant to be optimized;

[0069] Step S6: manufacturing a subperiosteal implant based on the optimized three-dimensional model.

[0070] For step S1, optical scanner data of the location where the subperiosteal implant is to be placed in the patient who needs to manufacture the subperiosteal implant is obtained by a handheld optical scanning instrument. Preferably, the optical scanner data generally includes jaw three-dimensional morphological data, soft tissue three-dimensional morphological data, reference points and three-dimensional coordinate system data; wherein the reference points are used to integrate scanning data from different perspectives into the same three-dimensional coordinate system. An initial three-dimensional model of the subperiosteal implant to be optimized is generated based on the acquired optical scanner data; the initial three-dimensional model contains all the data that can be provided by the optical scanner data.

[0071] For step S2, the three-dimensional coordinate information of each discrete point is obtained from the initial three-dimensional model generated above, and the three-dimensional coordinate information of each discrete point is converted into the expression form of point cloud data. It should be noted that point cloud data is a collection of a series of points in three-dimensional space, which are called point cloud points. A number of point cloud points can represent the shape of the surface of an object.

[0072] Preferably, after converting the three-dimensional coordinate information of each discrete point in the initial three-dimensional model into point cloud data, the point cloud data is preprocessed, and the preprocessing process includes denoising, downsampling and normal vector calculation; wherein, denoising is because point cloud data usually contains noise. In addition to the noise carried by the point cloud points themselves, it may also be noise caused by errors in the optical scanner sensor or noise caused by errors generated during the scanning process of the optical scanner. For the noise in the point cloud data caused by the optical scanner, the noise in the point cloud data can be removed by filters such as Gaussian filters and bilateral filters. Downsampling processing can avoid point cloud data being too dense. By processing point cloud data through downsampling algorithms such as voxel grid downsampling algorithms, the amount of data can be reduced while retaining the main features of the point cloud data. The purpose of normal vector calculation is to generate a grid. By calculating the normal vector of each point cloud point, the local surface direction of the point cloud point can be indicated; the normal vector calculation can be estimated and calculated based on domain analysis.

[0073] For step S3, the point cloud points in the point cloud data are grouped according to the attributes of each point cloud point in the point cloud data to obtain a number of point cloud point attribute groups, wherein the attributes of the point cloud points include at least density and point cloud point noise, and preferably, the attributes may also include surface shape, surface complexity and voxel complexity, etc.; wherein the surface shape is usually distinguished between regular and irregular.

[0074] For step S4, for each point cloud point attribute grouping, a preset optimization algorithm is selected according to the attributes corresponding to the point cloud point attribute grouping to optimize the point cloud point attribute grouping, thereby obtaining optimized point cloud data. Among them, the preset optimization algorithms include: triangulation algorithm, α shape algorithm, surface reconstruction algorithm and layer-by-layer growth algorithm. Exemplarily, for each point cloud point with regular surface shape and high density, a triangulation algorithm is used for processing; the triangulation algorithm constructs a triangulation based on each point cloud point, connects each point cloud point to form a triangular structure, and then generates a triangular mesh; the triangulation generates a relatively uniform mesh by maximizing the angle from the point cloud point to the triangle. For each point cloud point data with high surface complexity and the need to eliminate point cloud point noise, the α shape algorithm is used; the α shape algorithm determines which point cloud points in each point cloud point data belong to the surface of the object by adjusting the parameter α value, thereby removing unnecessary internal connections; a lower α value can handle more complex geometric shapes. For each point cloud point with high noise or irregularity, a surface reconstruction algorithm is used; the surface reconstruction algorithm converts each point cloud point into a continuous mesh surface by solving the Poisson equation according to the normal vector information of each point cloud point; the surface reconstruction algorithm can process each point cloud point that meets the specified rules to generate a relatively smooth surface by globally processing the point cloud data. For each point cloud point with a voxel data structure, a layer-by-layer growth algorithm is used for processing; the layer-by-layer growth algorithm embeds each point cloud point into a voxel grid, and extracts a triangular grid from the voxel cube by scanning the voxel cube. Each voxel cube generates a corresponding triangle according to the distribution of the point cloud, and finally forms a mesh. After optimizing the point cloud point attribute grouping by selecting the corresponding preset optimization algorithm according to the above judgment rules, a number of corresponding optimized point cloud point attribute grouping data can be obtained, and then the optimized point cloud data can be obtained according to all the optimized point cloud point attribute grouping data.

[0075] In a preferred embodiment, the generating of an optimized three-dimensional model based on the optimized point cloud data includes: generating mesh data based on the optimized point cloud data; wherein the mesh data includes: a plurality of triangular meshes and a plurality of polygonal meshes; smoothing the mesh data to obtain mesh data after first data preprocessing; filling holes in the mesh data after first data preprocessing to obtain mesh data after second data preprocessing; extracting geometric feature data of each mesh in the mesh data after second data preprocessing, and simplifying the mesh data after second data preprocessing based on the geometric feature data to obtain mesh data after third data preprocessing; generating an optimized three-dimensional model based on the mesh data after third data preprocessing.

[0076] Specifically, mesh data including a plurality of triangular meshes and a plurality of polygonal meshes are generated according to the optimized point cloud data, and the mesh data is smoothed to eliminate overly sharp or irregular surfaces in the mesh data, and the mesh data after the first data preprocessing is obtained; the mesh data after the first data preprocessing is hole-filled based on the hole-filling algorithm to obtain the mesh data after the second data preprocessing, so as to fill the holes caused by data missing in the mesh data after the first data preprocessing. The geometric feature data of each mesh in the mesh data after the second data preprocessing is extracted, and the mesh data after the second data preprocessing is simplified according to the extracted geometric feature data to reduce the mesh complexity and file size of the mesh data after the second data preprocessing, and the mesh data after the third data preprocessing is obtained; the mesh data after the third data preprocessing simplified in this way can reduce the number of meshes while retaining the geometric features. The optimized three-dimensional model is generated according to the mesh data after the third data preprocessing. Preferably, the file format used when exporting the optimized three-dimensional model is a 3D file format such as OBJ, STL, PLY, etc.

[0077] For step S5, in a preferred embodiment, the fitting of the optimized three-dimensional model and the initial three-dimensional model to determine the deviation compensation margin includes: fitting the optimized three-dimensional model and the initial three-dimensional model to determine a size deviation cloud map; determining the deviation compensation margin according to the size deviation cloud map.

[0078] In another preferred embodiment, the fitting of the optimized three-dimensional model and the initial three-dimensional model to determine the size deviation cloud map includes: aligning and fitting the optimized three-dimensional model and the initial three-dimensional model to obtain an alignment result map of the optimized three-dimensional model and the initial three-dimensional model; extracting a plurality of error judgment data groups from the alignment result map; wherein each error judgment data group includes a three-dimensional coordinate data obtained from the optimized three-dimensional model and a three-dimensional coordinate data obtained from the initial three-dimensional model; a distance threshold between the three-dimensional coordinate data obtained from the optimized three-dimensional model and the three-dimensional coordinate data obtained from the initial three-dimensional model is less than a preset distance threshold; and a size deviation cloud map is generated based on the plurality of error judgment data groups and the initial three-dimensional model.

[0079] In another preferred embodiment, the method of generating a size deviation cloud map based on a plurality of error judgment data groups and an initial three-dimensional model includes: determining the size deviation amount corresponding to each error judgment data group based on each error judgment data group; mapping the size deviation amount corresponding to each error judgment data group to a color space through color mapping to obtain a color space mapping map; and superimposing the color space mapping map on the initial three-dimensional model to generate a size deviation cloud map.

[0080] Specifically, the initial three-dimensional model and the optimized three-dimensional model are fitted, and four points are marked on the initial three-dimensional model, namely point A, point B, point C and point D; four points are marked on the optimized three-dimensional model, namely point a, point b, point c and point d. After marking, the first deviation threshold between the two points is determined based on point A and point a. If the first deviation threshold is greater than the preset deviation threshold, the position of point a is adjusted until the first deviation threshold between point A and point a is not greater than the preset deviation threshold. Similarly, the second deviation threshold between the two points is determined based on point B and point b, the third deviation threshold between the two points is determined based on point C and point c, and the fourth deviation threshold between the two points is determined based on point D and point d. The second deviation threshold, the third deviation threshold and the fourth deviation threshold are calibrated with the preset deviation, and the positions of point b, point c and point d are adjusted according to the calibration results until the second deviation threshold, the third deviation threshold and the fourth deviation threshold are all no greater than the preset deviation threshold. At this time, the positions of point A, point B, point C and point D on the initial three-dimensional model and point a, point b, point c and point d on the optimized three-dimensional model roughly correspond, respectively.

[0081] Then, the surface registration algorithm is used to identify the four points marked on the initial 3D model and the four points marked on the optimized 3D model. The registration process of the surface registration algorithm includes:

[0082] Step S101: Align the initial 3D model and the optimized 3D model according to the 4 points marked on the initial 3D model and the 4 points marked on the optimized 3D model. Preferably, the alignment can also be achieved by manually marking feature points or using a rough geometric transformation.

[0083] Step S201: Taking the initial three-dimensional model as the reference model and the optimized three-dimensional model as the model to be registered, a point is selected from the initial three-dimensional model, and a nearest neighbor search algorithm, such as a KD-Tree algorithm, is used to accelerate the search for a point in the optimized three-dimensional model that is closest to the selected point in the initial three-dimensional model, and an error determination data set is constructed based on the selected point in the initial three-dimensional model and the nearest point in the optimized three-dimensional model that is closest to the selected point in the initial three-dimensional model. It should be noted that in order to avoid the point found when searching for a point in the optimized three-dimensional model that is closest to the selected point in the initial three-dimensional model, the distance threshold between the point obtained from the optimized three-dimensional model and the point obtained from the initial three-dimensional model is less than a preset distance threshold. The preset distance threshold is determined according to the nearest neighbor search algorithm.

[0084] Step S301: For each error determination data set, a rigid body transformation matrix is ​​calculated based on the data of two points in the error determination data set; wherein the rigid body transformation matrix includes a rigid body rotation matrix and a rigid body translation vector. The rigid body rotation matrix and the rigid body translation vector are calculated by the least squares method to obtain the size deviation of the error determination data set.

[0085] Step S401: transforming the initial three-dimensional model through a rigid body transformation matrix to further align it with the optimized three-dimensional model.

[0086] Step S501: If the preset convergence condition is met, execute step S601; if the preset convergence condition is not met, return to step S201 to continue calculating the size deviation of the next error judgment data group; wherein, the preset convergence condition includes: the average size deviation of each error judgment data group reaches the convergence error, or the size deviation of each error judgment data group has been calculated.

[0087] Step S601: Map the size deviation corresponding to each error determination data group to the color space through color mapping, identify different size deviations with different colors, and obtain a color space mapping diagram; superimpose the color space mapping diagram on the initial three-dimensional model to generate a size deviation cloud map. Preferably, the size deviation cloud map is exported as an image or 3D visualization file for easy reference.

[0088] Preferably, the deviation compensation margin of each point in the initial three-dimensional model is determined according to the color distribution in the size deviation cloud map, and then the initial three-dimensional model is deviation compensated according to the deviation compensation margin to obtain a deviation-optimized three-dimensional model of the subperiosteal implant to be optimized.

[0089] In step S6, a subperiosteal implant is manufactured according to the deviation-optimized three-dimensional model, and the subperiosteal implant is provided to medical staff for oral treatment of the patient.

[0090] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0091] like Figure 2 As shown, an embodiment of the present invention provides a subperiosteal implant manufacturing size deviation optimization device, including: an initial three-dimensional model construction module, an optimized three-dimensional model construction module and a deviation compensation optimization module;

[0092] The initial three-dimensional model building module is used to obtain optical scanner data of the patient's subperiosteal implant to be placed, and generate an initial three-dimensional model of the subperiosteal implant to be optimized based on the optical scanner data;

[0093] The optimized three-dimensional model construction module is used to obtain the three-dimensional coordinate information of each discrete point in the initial three-dimensional model, and convert the three-dimensional coordinate information of each discrete point into point cloud data; wherein the point cloud data includes a plurality of point cloud points; each point cloud point in the point cloud data is grouped according to the attributes of each point cloud point in the point cloud data to obtain a plurality of point cloud point attribute groups; wherein the attributes include: density and point cloud point noise; for each point cloud point attribute group, a preset optimization algorithm is selected according to the attributes corresponding to the point cloud point attribute group to optimize the point cloud point attribute group to obtain optimized point cloud data, and an optimized three-dimensional model is generated according to the optimized point cloud data;

[0094] The deviation compensation optimization module is used to fit the optimized three-dimensional model and the initial three-dimensional model, determine the deviation compensation margin, and use the deviation compensation margin to compensate the deviation of the initial three-dimensional model to obtain the deviation optimized three-dimensional model of the subperiosteal implant to be optimized; and manufacture the subperiosteal implant according to the deviation optimized three-dimensional model.

[0095] In a preferred embodiment, the step of fitting the optimized three-dimensional model and the initial three-dimensional model to determine the deviation compensation margin includes:

[0096] Fitting the optimized three-dimensional model and the initial three-dimensional model to determine a size deviation cloud map;

[0097] Determine the deviation compensation allowance based on the dimensional deviation cloud diagram.

[0098] In a preferred embodiment, generating an optimized three-dimensional model according to the optimized point cloud data includes:

[0099] Generate mesh data according to the optimized point cloud data; wherein the mesh data includes: a plurality of triangular meshes and a plurality of polygonal meshes;

[0100] Smoothing the grid data to obtain grid data after first data preprocessing;

[0101] Filling holes in the grid data after the first data preprocessing to obtain grid data after the second data preprocessing;

[0102] Extracting geometric feature data of each grid in the grid data after the second data preprocessing, and simplifying the grid data after the second data preprocessing according to the geometric feature data to obtain grid data after the third data preprocessing;

[0103] An optimized three-dimensional model is generated according to the mesh data preprocessed by the third data.

[0104] In a preferred embodiment, the step of fitting the optimized three-dimensional model and the initial three-dimensional model to determine a size deviation cloud map includes:

[0105] Aligning and fitting the optimized three-dimensional model and the initial three-dimensional model to obtain an alignment result graph of the optimized three-dimensional model and the initial three-dimensional model;

[0106] Extracting a plurality of error determination data groups from the alignment result graph; wherein each error determination data group includes a three-dimensional coordinate data obtained from the optimized three-dimensional model and a three-dimensional coordinate data obtained from the initial three-dimensional model; a distance threshold between the three-dimensional coordinate data obtained from the optimized three-dimensional model and the three-dimensional coordinate data obtained from the initial three-dimensional model is less than a preset distance threshold;

[0107] A dimensional deviation cloud diagram is generated based on a plurality of error determination data sets and an initial three-dimensional model.

[0108] In a preferred embodiment, the step of generating a dimension deviation cloud map based on a plurality of error determination data sets and an initial three-dimensional model includes:

[0109] Determine the dimensional deviation corresponding to each error determination data group according to each error determination data group;

[0110] Mapping the size deviation corresponding to each error determination data group to the color space through color mapping to obtain a color space mapping diagram;

[0111] The color space map is superimposed on the initial 3D model to generate a size deviation cloud map.

[0112] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0113] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0114] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimizing dimensional deviation in manufacturing subperiosteal implants, characterized in that: include: Acquiring optical scanner data of the patient's location where the subperiosteal implant is to be placed, and generating an initial three-dimensional model of the subperiosteal implant to be optimized based on the optical scanner data; Acquire the three-dimensional coordinate information of each discrete point in the initial three-dimensional model, and convert the three-dimensional coordinate information of each discrete point into point cloud data; wherein the point cloud data includes a plurality of point cloud points; Grouping each point cloud point in the point cloud data according to the attributes of each point cloud point in the point cloud data to obtain a number of point cloud point attribute groups; wherein the attributes include: density and point cloud point noise; For each point cloud point attribute grouping, a preset optimization algorithm is selected according to the attributes corresponding to the point cloud point attribute grouping to optimize the point cloud point attribute grouping to obtain optimized point cloud data, and an optimized three-dimensional model is generated according to the optimized point cloud data; Fitting the optimized three-dimensional model and the initial three-dimensional model, determining a deviation compensation margin, and using the deviation compensation margin to perform deviation compensation on the initial three-dimensional model to obtain a deviation-optimized three-dimensional model of the subperiosteal implant to be optimized; The subperiosteal implant is manufactured by optimizing the three-dimensional model according to the deviation.

2. A method for optimizing dimensional deviation of a subperiosteal implant according to claim 1, characterized in that: The step of fitting the optimized three-dimensional model and the initial three-dimensional model to determine the deviation compensation margin includes: Fitting the optimized three-dimensional model and the initial three-dimensional model to determine a size deviation cloud map; Determine the deviation compensation allowance based on the dimensional deviation cloud diagram.

3. A method for optimizing dimensional deviation of a subperiosteal implant according to claim 1, characterized in that: The step of generating an optimized three-dimensional model according to the optimized point cloud data comprises: Generate mesh data according to the optimized point cloud data; wherein the mesh data includes: a plurality of triangular meshes and a plurality of polygonal meshes; Smoothing the grid data to obtain grid data after first data preprocessing; Filling holes in the grid data after the first data preprocessing to obtain grid data after the second data preprocessing; Extracting geometric feature data of each grid in the grid data after the second data preprocessing, and simplifying the grid data after the second data preprocessing according to the geometric feature data to obtain grid data after the third data preprocessing; An optimized three-dimensional model is generated according to the mesh data preprocessed by the third data.

4. A method for optimizing dimensional deviation of a subperiosteal implant according to claim 2, characterized in that: The step of fitting the optimized three-dimensional model and the initial three-dimensional model to determine a size deviation cloud map includes: Aligning and fitting the optimized three-dimensional model and the initial three-dimensional model to obtain an alignment result graph of the optimized three-dimensional model and the initial three-dimensional model; Extracting a plurality of error determination data groups from the alignment result graph; wherein each error determination data group includes a three-dimensional coordinate data obtained from the optimized three-dimensional model and a three-dimensional coordinate data obtained from the initial three-dimensional model; a distance threshold between the three-dimensional coordinate data obtained from the optimized three-dimensional model and the three-dimensional coordinate data obtained from the initial three-dimensional model is less than a preset distance threshold; A dimensional deviation cloud diagram is generated based on a plurality of error determination data sets and an initial three-dimensional model.

5. A method for optimizing dimensional deviation of a subperiosteal implant according to claim 4, characterized in that: The step of generating a dimension deviation cloud map according to a plurality of error determination data sets and an initial three-dimensional model includes: Determine the dimensional deviation corresponding to each error determination data group according to each error determination data group; Mapping the size deviation corresponding to each error determination data group to the color space through color mapping to obtain a color space mapping diagram; The color space map is superimposed on the initial 3D model to generate a size deviation cloud map.

6. A device for optimizing dimensional deviation in manufacturing subperiosteal implants, characterized in that: include: An initial three-dimensional model building module, an optimized three-dimensional model building module, and a deviation compensation optimization module; The initial three-dimensional model building module is used to obtain optical scanner data of the patient's subperiosteal implant to be placed, and generate an initial three-dimensional model of the subperiosteal implant to be optimized based on the optical scanner data; The optimized three-dimensional model construction module is used to obtain the three-dimensional coordinate information of each discrete point in the initial three-dimensional model, and convert the three-dimensional coordinate information of each discrete point into point cloud data; wherein the point cloud data includes a plurality of point cloud points; each point cloud point in the point cloud data is grouped according to the attributes of each point cloud point in the point cloud data to obtain a plurality of point cloud point attribute groups; wherein the attributes include: density and point cloud point noise; for each point cloud point attribute group, a preset optimization algorithm is selected according to the attributes corresponding to the point cloud point attribute group to optimize the point cloud point attribute group to obtain optimized point cloud data, and an optimized three-dimensional model is generated according to the optimized point cloud data; The deviation compensation optimization module is used to fit the optimized three-dimensional model and the initial three-dimensional model, determine the deviation compensation margin, and use the deviation compensation margin to compensate the deviation of the initial three-dimensional model to obtain the deviation optimized three-dimensional model of the subperiosteal implant to be optimized; and manufacture the subperiosteal implant according to the deviation optimized three-dimensional model.

7. The device for optimizing dimension deviation of subperiosteal implant manufacturing according to claim 6, characterized in that: The step of fitting the optimized three-dimensional model and the initial three-dimensional model to determine the deviation compensation margin includes: Fitting the optimized three-dimensional model and the initial three-dimensional model to determine a size deviation cloud map; Determine the deviation compensation allowance based on the dimensional deviation cloud diagram.

8. The device for optimizing dimension deviation of subperiosteal implant manufacturing according to claim 6, characterized in that: The step of generating an optimized three-dimensional model according to the optimized point cloud data comprises: Generate mesh data according to the optimized point cloud data; wherein the mesh data includes: a plurality of triangular meshes and a plurality of polygonal meshes; Smoothing the grid data to obtain grid data after first data preprocessing; Filling holes in the grid data after the first data preprocessing to obtain grid data after the second data preprocessing; Extracting geometric feature data of each grid in the grid data after the second data preprocessing, and simplifying the grid data after the second data preprocessing according to the geometric feature data to obtain grid data after the third data preprocessing; An optimized three-dimensional model is generated according to the mesh data preprocessed by the third data.

9. The device for optimizing dimension deviation of subperiosteal implant manufacturing according to claim 7, characterized in that: The step of fitting the optimized three-dimensional model and the initial three-dimensional model to determine a size deviation cloud map includes: Aligning and fitting the optimized three-dimensional model and the initial three-dimensional model to obtain an alignment result graph of the optimized three-dimensional model and the initial three-dimensional model; Extracting a plurality of error determination data groups from the alignment result graph; wherein each error determination data group includes a three-dimensional coordinate data obtained from the optimized three-dimensional model and a three-dimensional coordinate data obtained from the initial three-dimensional model; a distance threshold between the three-dimensional coordinate data obtained from the optimized three-dimensional model and the three-dimensional coordinate data obtained from the initial three-dimensional model is less than a preset distance threshold; A dimensional deviation cloud diagram is generated based on a plurality of error determination data sets and an initial three-dimensional model.

10. The device for optimizing dimension deviation of subperiosteal implant manufacturing according to claim 9, characterized in that: The step of generating a dimension deviation cloud map according to a plurality of error determination data sets and an initial three-dimensional model includes: Determine the dimensional deviation corresponding to each error determination data group according to each error determination data group; Mapping the size deviation corresponding to each error determination data group to the color space through color mapping to obtain a color space mapping diagram; The color space map is superimposed on the initial 3D model to generate a size deviation cloud map.

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