A method and apparatus for optimizing dimensional deviations in the manufacturing of subperiosteal implants

Abstract

This invention discloses a method and apparatus for optimizing dimensional deviations in the manufacturing of subperiosteal implants. The method includes: acquiring optical scanner data at the location where the subperiosteal implant will be placed on the patient, and generating an initial three-dimensional model of the subperiosteal implant to be optimized; acquiring the three-dimensional coordinate information of the initial three-dimensional model, and converting the three-dimensional coordinate information into point cloud data; grouping the point cloud data according to the attributes of each point cloud point, obtaining several point cloud point attribute groups; for each point cloud point attribute group, selecting a preset optimization algorithm to optimize the point cloud point attribute group according to the attributes corresponding to the point cloud point attribute group, obtaining optimized point cloud data, and generating an optimized three-dimensional model based on the optimized point cloud data; fitting the optimized three-dimensional model and the initial three-dimensional model, determining the deviation compensation margin, and using the deviation compensation margin to compensate for the deviation in the initial three-dimensional model, obtaining a deviation-optimized three-dimensional model; and manufacturing the subperiosteal implant based on the deviation-optimized three-dimensional model.

Description

Technical Field

[0001] This invention relates to the field of subperiosteal implant technology, and more particularly to a method and apparatus for optimizing dimensional deviations in the manufacturing of subperiosteal implants. Background Technology

[0002] Subperiosteal implants are a specialized dental implant technique. Instead of being implanted inside the jawbone, they are placed between the periosteum and the bone surface. This method is primarily suitable for patients who cannot undergo traditional intraosseous implantation due to jawbone atrophy, osteoporosis, or other reasons. In dental treatment, the manufacturing dimensions of subperiosteal implants need to be customized based on the patient's oral condition. Current methods for manufacturing subperiosteal implants mainly involve medical staff using calipers to measure the implant dimensions. However, because implant shapes vary from patient to patient, this caliper-based measurement method introduces measurement errors in areas where the calipers cannot reach. This leads to a discrepancy between the final subperiosteal implant size and the required dimensions, resulting in a poor fit when placed between the periosteum and the bone surface. Therefore, there is an urgent need for a method to reduce dimensional deviations in subperiosteal implant manufacturing to improve the fit and stability of the manufactured implant, thereby enhancing patient treatment outcomes. Summary of the Invention

[0003] This invention provides a method and apparatus for optimizing the dimensional deviation of subperiosteal implant manufacturing, which can reduce the dimensional deviation of subperiosteal implant manufacturing, improve the fit and stability of the manufactured subperiosteal implant, and improve the treatment effect for patients.

[0004] An embodiment of the present invention provides a method for optimizing dimensional deviations in the manufacturing of subperiosteal implants, comprising:

[0005] Acquire optical scanner data at the site where the subperiosteal implant will be placed on the patient, and generate an initial three-dimensional model of the subperiosteal implant to be optimized based on the optical scanner data;

[0006] The three-dimensional coordinate information of each discrete point in the initial three-dimensional model is obtained, and the three-dimensional coordinate information of each discrete point is converted into point cloud data; wherein, the point cloud data includes a number of point cloud points;

[0007] Based on the attributes of each point in the point cloud data, the point cloud points are grouped to obtain several point cloud point attribute groups; wherein, the attributes include: density and point cloud point noise;

[0008] For each point cloud point attribute group, a preset optimization algorithm is selected based on the attribute corresponding to the point cloud point attribute group to optimize the point cloud point attribute group, thereby obtaining optimized point cloud data, and an optimized 3D model is generated based on the optimized point cloud data.

[0009] Fit the optimized three-dimensional model and the initial three-dimensional model to determine the deviation compensation margin, and use the deviation compensation margin to perform deviation compensation on the initial three-dimensional model to obtain the deviation-optimized three-dimensional model of the subperiosteal implant to be optimized.

[0010] Subperiosteal implants are manufactured based on the optimized 3D model of the deviation.

[0011] Further, the fitting of the optimized 3D model and the initial 3D model to determine the deviation compensation margin includes:

[0012] Fit the optimized 3D model and the initial 3D model to determine the size deviation cloud map;

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

[0014] Furthermore, the step of generating an optimized 3D model based on the optimized point cloud data includes:

[0015] Mesh data is generated based on the optimized point cloud data; wherein, the mesh data includes: several triangular meshes and several polygonal meshes;

[0016] The grid data is smoothed to obtain the first pre-processed grid data;

[0017] Hole filling is performed on the grid data after the first data preprocessing to obtain the grid data after the second data preprocessing;

[0018] Extract the geometric feature data of each grid in the grid data after the second data preprocessing, and simplify the grid data after the second data preprocessing based on the geometric feature data to obtain the grid data after the third data preprocessing;

[0019] An optimized 3D model is generated based on the preprocessed grid data from the third data source.

[0020] Further, the process of fitting the optimized 3D model and the initial 3D model to determine the dimensional deviation contour map includes:

[0021] Align and fit the optimized 3D model and the initial 3D model to obtain an alignment result diagram of the optimized 3D model and the initial 3D model;

[0022] Several error determination data groups are extracted from the alignment result image; wherein each error determination data group includes three-dimensional coordinate data obtained from the optimized three-dimensional model and three-dimensional coordinate data obtained from the initial three-dimensional model; the 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 dimension deviation cloud map is generated based on several error judgment data sets and the initial 3D model.

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

[0025] Determine the dimensional deviation corresponding to each error judgment data group based on each error judgment data group;

[0026] By mapping the size deviation corresponding to each error judgment data group to the color space, a color space mapping diagram is obtained.

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

[0028] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments;

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

[0030] The initial three-dimensional model construction module is used to acquire optical scanner data at the site where the patient's subperiosteal implant is to be placed, and to generate an initial three-dimensional model of the subperiosteal implant to be optimized based on the optical scanner data.

[0031] The optimized 3D model construction module is used to acquire the 3D coordinate information of each discrete point in the initial 3D model and convert the 3D coordinate information of each discrete point into point cloud data; wherein, the point cloud data includes a number of point cloud points; the point cloud points in the point cloud data are grouped according to the attributes of each point cloud point 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 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 3D model is generated based on 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 perform deviation compensation on 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] Further, the fitting of the optimized 3D model and the initial 3D model to determine the deviation compensation margin includes:

[0034] Fit the optimized 3D model and the initial 3D model to determine the size deviation cloud map;

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

[0036] Furthermore, the step of generating an optimized 3D model based on the optimized point cloud data includes:

[0037] Mesh data is generated based on the optimized point cloud data; wherein, the mesh data includes: several triangular meshes and several polygonal meshes;

[0038] The grid data is smoothed to obtain the first pre-processed grid data;

[0039] Hole filling is performed on the grid data after the first data preprocessing to obtain the grid data after the second data preprocessing;

[0040] Extract the geometric feature data of each grid in the grid data after the second data preprocessing, and simplify the grid data after the second data preprocessing based on the geometric feature data to obtain the grid data after the third data preprocessing;

[0041] An optimized 3D model is generated based on the preprocessed grid data from the third data source.

[0042] Further, the process of fitting the optimized 3D model and the initial 3D model to determine the dimensional deviation contour map includes:

[0043] Align and fit the optimized 3D model and the initial 3D model to obtain an alignment result diagram of the optimized 3D model and the initial 3D model;

[0044] Several error determination data groups are extracted from the alignment result image; wherein each error determination data group includes three-dimensional coordinate data obtained from the optimized three-dimensional model and three-dimensional coordinate data obtained from the initial three-dimensional model; the 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 dimension deviation cloud map is generated based on several error judgment data sets and the initial 3D model.

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

[0047] Determine the dimensional deviation corresponding to each error judgment data group based on each error judgment data group;

[0048] By mapping the size deviation corresponding to each error judgment data group to the color space, a color space mapping diagram is obtained.

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

[0050] The following benefits can be obtained by implementing the present invention:

[0051] This invention provides a method and apparatus for optimizing dimensional deviations in the manufacturing of subperiosteal implants. The method acquires optical scanner data at the site where the subperiosteal implant will be placed on the patient and generates an initial three-dimensional model of the subperiosteal implant to be optimized based on the optical scanner data. Acquiring optical scanner data avoids the large errors caused by manual caliper measurements and improves the accuracy of data measurement at the site where the subperiosteal implant will be placed.

[0052] Furthermore, the three-dimensional coordinate data of each discrete point in the initial three-dimensional model is acquired, and then the acquired three-dimensional coordinate data is converted into point cloud data. After attribute grouping processing of the point cloud data, optimized point cloud data is obtained, and then an optimized three-dimensional model is generated based on the optimized point cloud data. The optimized three-dimensional model and the initial three-dimensional model are fitted to determine the deviation compensation margin, and the deviation compensation margin is used to compensate for the deviation of the initial three-dimensional model to obtain the deviation-optimized three-dimensional model of the subperiosteal implant to be optimized. After obtaining the optimized three-dimensional model by further optimizing the model based on the initial three-dimensional model, the deviation compensation margin between the optimized three-dimensional model and the initial three-dimensional model is determined again. The deviation compensation margin is used to compensate for the deviation of the initial three-dimensional model to achieve secondary deviation optimization of the initial three-dimensional model, thereby further reducing the dimensional deviation problem in the initial three-dimensional model generated by the optical scanner data. The subperiosteal implant manufactured by the deviation-optimized three-dimensional model after secondary optimization can fit the subperiosteal implant to be placed better, thereby improving the fit and stability of the manufactured subperiosteal implant, and thus improving the treatment effect for patients. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating a method for optimizing dimensional deviations in the manufacture of subperiosteal implants according to an embodiment of the present invention.

[0054] Figure 2This is a schematic diagram of a device for optimizing the dimensional deviation in the manufacturing of subperiosteal implants according to an embodiment of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort 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 one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0058] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0059] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0060] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0061] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0062] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0063] like Figure 1 The image shows a method for optimizing dimensional deviations in the manufacturing of subperiosteal implants according to an embodiment of the present invention, comprising:

[0064] Step S1: Obtain optical scanner data at the site where the subperiosteal implant will be placed on the patient, and generate an initial three-dimensional model of the subperiosteal implant to be optimized based on the optical scanner data;

[0065] Step S2: 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 number of point cloud points;

[0066] Step S3: Group the point cloud points in the point cloud data according to their attributes to obtain several 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, select a preset optimization algorithm according to the attribute corresponding to the point cloud point attribute group to optimize the point cloud point attribute group, obtain the optimized point cloud data, and generate an optimized 3D model based on the optimized point cloud data.

[0068] Step S5: Fit the optimized three-dimensional model and the initial three-dimensional model, determine the deviation compensation margin, and use the deviation compensation margin to perform deviation compensation on the initial three-dimensional model to obtain the deviation optimized three-dimensional model of the subperiosteal implant to be optimized.

[0069] Step S6: Optimize the three-dimensional model based on the deviation to manufacture the subperiosteal implant.

[0070] For step S1, optical scanner data is acquired at the site where the subperiosteal implant will be placed on the patient using a handheld optical scanner. Preferably, this optical scanner data typically includes three-dimensional jawbone topography data, three-dimensional soft tissue topography data, reference points, and three-dimensional coordinate system data; wherein, the reference points are used to integrate scan 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; this initial three-dimensional model contains all the data that the optical scanner data can provide.

[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 form of point cloud data. It should be noted that point cloud data is a collection of points in three-dimensional space, these points are called point cloud points, and a number of point cloud points can represent the shape of the surface of an object.

[0072] Preferably, after converting the 3D coordinate information of each discrete point in the initial 3D model into point cloud data, the point cloud data is preprocessed. This preprocessing includes denoising, downsampling, and normal vector calculation. Denoising is performed because point cloud data typically contains noise. Besides the noise inherent in the point cloud points themselves, it may also be caused by errors from the optical scanner sensor or by errors generated during the scanning process. For noise in point cloud data caused by the optical scanner, filters such as Gaussian filters and bilateral filters can remove the noise. Downsampling avoids excessively dense point cloud data. By using downsampling algorithms, such as voxel mesh downsampling algorithms, the amount of data can be reduced while retaining the main features of the point cloud data. Normal vector calculation is used to generate a mesh. By calculating the normal vector of each point cloud point, the local surface orientation of the point cloud point can be indicated. Normal vector calculation can be estimated based on neighborhood analysis.

[0073] For step S3, the point cloud points in the point cloud data are grouped according to their attributes to obtain several point cloud point attribute groups. The attributes of the point cloud points include at least density and point cloud point noise. Preferably, the attributes may also include surface shape, surface complexity, and voxel complexity. Among them, the surface shape is usually distinguished as regular and irregular.

[0074] For step S4, for each point cloud attribute group, a preset optimization algorithm is selected based on the attribute corresponding to the point cloud attribute group to optimize the point cloud attribute group, thereby obtaining optimized point cloud data. The preset optimization algorithms include: triangulation algorithm, α-shape algorithm, surface reconstruction algorithm, and layer-by-layer growth algorithm. For example, for point cloud points with regular surface shapes and high density, the triangulation algorithm is used. The triangulation algorithm constructs triangulations based on each point cloud point, connecting the point cloud points to form triangular structures, thus generating a triangular mesh. Triangulation generates a relatively uniform mesh by maximizing the angle between the point cloud points and the triangles. For point cloud data with high surface complexity and requiring the removal of point cloud noise, the α-shape algorithm is used. The α-shape algorithm determines which point cloud points belong to the object surface by adjusting the parameter α value, thereby removing unnecessary internal connections; a lower α value can handle more complex geometric shapes. For point cloud points with high noise or irregularities, a surface reconstruction algorithm is employed. This algorithm transforms each point cloud point into a continuous mesh surface by solving the Poisson equation based on its normal vector information. By processing the point cloud data globally, the algorithm can generate relatively smooth surfaces for point cloud points that conform to defined rules. For point cloud points with a voxel data structure, a layer-by-layer growth algorithm is used. This algorithm embeds each point cloud point into a voxel mesh, extracts triangular meshes from the voxel cubes by scanning them, and generates corresponding triangles for each voxel cube based on the point cloud distribution, ultimately forming a mesh. After selecting the appropriate preset optimization algorithm based on the above judgment rules to optimize the point cloud point attribute groupings, several corresponding optimized point cloud point attribute groupings are obtained. Finally, the optimized point cloud data is obtained based on all the optimized point cloud point attribute groupings.

[0075] In a preferred embodiment, generating an optimized 3D model based on optimized point cloud data includes: generating mesh data based on optimized point cloud data; wherein the mesh data includes: several triangular meshes and several polygonal meshes; smoothing the mesh data to obtain first pre-processed mesh data; filling holes in the first pre-processed mesh data to obtain second pre-processed mesh data; extracting geometric feature data of each mesh in the second pre-processed mesh data, and simplifying the second pre-processed mesh data based on the geometric feature data to obtain third pre-processed mesh data; and generating an optimized 3D model based on the third pre-processed mesh data.

[0076] Specifically, based on the optimized point cloud data, a mesh data consisting of several triangular and polygonal meshes is generated. This mesh data is then smoothed to eliminate overly sharp or irregular surfaces, resulting in the first preprocessed mesh data. A hole-filling algorithm is then used to fill holes in the first preprocessed mesh data, resulting in the second preprocessed mesh data, which fills in holes caused by missing data in the first preprocessed mesh data. Geometric feature data of each mesh in the second preprocessed mesh data is extracted, and the mesh data is simplified based on these extracted features to reduce mesh complexity and file size, resulting in the third preprocessed mesh data. This simplified third preprocessed mesh data retains geometric features while reducing the number of meshes. An optimized 3D model is then generated based on the third preprocessed mesh data. Preferably, the optimized 3D model is exported in 3D file formats such as OBJ, STL, or PLY.

[0077] For step S5, in a preferred embodiment, fitting the optimized 3D model and the initial 3D model to determine the deviation compensation margin includes: fitting the optimized 3D model and the initial 3D model to determine the size deviation cloud map; and determining the deviation compensation margin based on the size deviation cloud map.

[0078] In another preferred embodiment, fitting the optimized 3D model and the initial 3D model to determine the size deviation cloud map includes: aligning and fitting the optimized 3D model and the initial 3D model to obtain an alignment result map of the optimized 3D model and the initial 3D model; extracting several error judgment data sets from the alignment result map; wherein each error judgment data set includes 3D coordinate data obtained from the optimized 3D model and 3D coordinate data obtained from the initial 3D model; the distance threshold between the 3D coordinate data obtained from the optimized 3D model and the 3D coordinate data obtained from the initial 3D model is less than a preset distance threshold; and generating a size deviation cloud map based on the several error judgment data sets and the initial 3D model.

[0079] In another preferred embodiment, generating a dimension deviation cloud map based on several error judgment data groups and an initial three-dimensional model includes: determining the dimension deviation amount corresponding to each error judgment data group based on each error judgment data group; mapping the dimension 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 onto the initial three-dimensional model to generate a dimension deviation cloud map.

[0080] Specifically, the initial 3D model and the optimized 3D model are fitted together. Four points are marked on the initial 3D model, namely point A, point B, point C and point D. Four points are marked on the optimized 3D model, namely point a, point b, point c and point d. After marking, the first deviation threshold between point A and point a is determined. 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 two points is determined based on points B and b, the third deviation threshold is determined based on points C and c, and the fourth deviation threshold is determined based on points D and d. The second, third, and fourth deviation thresholds are then checked against a preset deviation, and the positions of points b, c, and d are adjusted based on the check results until the second, third, and fourth deviation thresholds are all no greater than the preset deviation thresholds. At this point, the positions of points A, B, C, and D on the initial 3D model and points a, b, c, and d on the optimized 3D model roughly correspond to each other.

[0081] Then, a surface registration algorithm is used to identify four marked points on the initial 3D model and four marked points 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 based on the four points marked on the initial 3D model and the four points marked on the optimized 3D model. Preferably, the alignment can also be achieved by manually marking feature points or by using a coarse geometric transformation.

[0083] Step S201: Using the initial 3D model as the baseline model and the optimized 3D model as the model to be registered, select a point from the initial 3D model and use a nearest neighbor search algorithm, such as the KD-Tree algorithm, to accelerate the search for the nearest point in the optimized 3D model to the selected point in the initial 3D model. Construct an error judgment data set based on the selected point in the initial 3D model and the nearest point in the optimized 3D model to the selected point in the initial 3D model. It should be noted that, to avoid finding a point that is not the closest when searching for the nearest point in the optimized 3D model to the selected point in the initial 3D model, the distance threshold between the point obtained from the optimized 3D model and the point obtained from the initial 3D model is less than a preset distance threshold. This preset distance threshold is determined according to the nearest neighbor search algorithm.

[0084] Step S301: For each error judgment data set, calculate a rigid body transformation matrix based on the data from two points in the error judgment 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 rigid body translation vector are calculated using the least squares method to obtain the dimensional deviation of the error judgment data set.

[0085] Step S401: Transform the initial 3D model using a rigid body transformation matrix to further align it with the optimized 3D model.

[0086] Step S501: If the preset convergence condition is met, proceed to 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 dimensional deviation corresponding to each error judgment data group to a color space using color mapping, using different colors to identify different dimensional deviations, to obtain a color space mapping map; overlay the color space mapping map onto the initial 3D model to generate a dimensional deviation cloud map. Preferably, export the dimensional deviation cloud map as an image or 3D visualization file for easy viewing.

[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 deviation compensation is performed on the initial three-dimensional model according to the deviation compensation margin to obtain the deviation-optimized three-dimensional model of the subperiosteal implant to be optimized.

[0089] For step S6, the subperiosteal implant is manufactured based on the deviation optimization three-dimensional model, and the subperiosteal implant is provided to medical staff for use in the patient's oral treatment.

[0090] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0091] like Figure 2 As shown, an embodiment of the present invention provides a device for optimizing the dimensional deviation of subperiosteal implant manufacturing, 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 construction module is used to acquire optical scanner data at the site where the patient's subperiosteal implant is to be placed, and to generate an initial three-dimensional model of the subperiosteal implant to be optimized based on the optical scanner data.

[0093] The optimized 3D model construction module is used to acquire the 3D coordinate information of each discrete point in the initial 3D model and convert the 3D coordinate information of each discrete point into point cloud data; wherein, the point cloud data includes a number of point cloud points; the point cloud points in the point cloud data are grouped according to the attributes of each point cloud point 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 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 3D model is generated based on 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 perform deviation compensation on 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, fitting the optimized 3D model and the initial 3D model to determine the deviation compensation margin includes:

[0096] Fit the optimized 3D model and the initial 3D model to determine the size deviation cloud map;

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

[0098] In a preferred embodiment, generating an optimized 3D model based on the optimized point cloud data includes:

[0099] Mesh data is generated based on the optimized point cloud data; wherein, the mesh data includes: several triangular meshes and several polygonal meshes;

[0100] The grid data is smoothed to obtain the first pre-processed grid data;

[0101] Hole filling is performed on the grid data after the first data preprocessing to obtain the grid data after the second data preprocessing;

[0102] Extract the geometric feature data of each grid in the grid data after the second data preprocessing, and simplify the grid data after the second data preprocessing based on the geometric feature data to obtain the grid data after the third data preprocessing;

[0103] An optimized 3D model is generated based on the preprocessed grid data from the third data source.

[0104] In a preferred embodiment, fitting the optimized 3D model and the initial 3D model to determine the dimensional deviation contour map includes:

[0105] Align and fit the optimized 3D model and the initial 3D model to obtain an alignment result diagram of the optimized 3D model and the initial 3D model;

[0106] Several error determination data groups are extracted from the alignment result image; wherein each error determination data group includes three-dimensional coordinate data obtained from the optimized three-dimensional model and three-dimensional coordinate data obtained from the initial three-dimensional model; the 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 dimension deviation cloud map is generated based on several error judgment data sets and the initial 3D model.

[0108] In a preferred embodiment, generating a dimensional 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 judgment data group based on each error judgment data group;

[0110] By mapping the size deviation corresponding to each error judgment data group to the color space, a color space mapping diagram is obtained.

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

[0112] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

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

[0114] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and 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 deviations in the manufacturing of subperiosteal implants, characterized in that, include: Acquire optical scanner data at the site where the subperiosteal implant will be placed on the patient, and generate an initial three-dimensional model of the subperiosteal implant to be optimized based on the optical scanner data; The three-dimensional coordinate information of each discrete point in the initial three-dimensional model is obtained, and the three-dimensional coordinate information of each discrete point is converted into point cloud data; wherein, the point cloud data includes a number of point cloud points; Based on the attributes of each point in the point cloud data, the point cloud points are grouped to obtain several 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 based on the attribute corresponding to the point cloud point attribute group to optimize the point cloud point attribute group, thereby obtaining optimized point cloud data, and an optimized 3D model is generated based on the optimized point cloud data. Fit the optimized three-dimensional model and the initial three-dimensional model to determine the deviation compensation margin, and use the deviation compensation margin to perform deviation compensation on the initial three-dimensional model to obtain the deviation-optimized three-dimensional model of the subperiosteal implant to be optimized. Based on the deviation, the three-dimensional model is optimized to manufacture the subperiosteal implant; The step of fitting the optimized 3D model and the initial 3D model to determine the deviation compensation margin includes: Fit the optimized 3D model and the initial 3D model to determine the size deviation cloud map; Determine the deviation compensation margin based on the dimensional deviation contour map; The process of fitting the optimized 3D model and the initial 3D model to determine the size deviation contour map includes: Align and fit the optimized 3D model and the initial 3D model to obtain an alignment result diagram of the optimized 3D model and the initial 3D model; Several error determination data groups are extracted from the alignment result image; wherein each error determination data group includes three-dimensional coordinate data obtained from the optimized three-dimensional model and three-dimensional coordinate data obtained from the initial three-dimensional model; the 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 dimension deviation cloud map is generated based on several error judgment data sets and the initial 3D model.

2. The method for optimizing dimensional deviations in the manufacture of subperiosteal implants as described in claim 1, characterized in that, The step of generating an optimized 3D model based on the optimized point cloud data includes: Mesh data is generated based on the optimized point cloud data; wherein, the mesh data includes: several triangular meshes and several polygonal meshes; The grid data is smoothed to obtain the first pre-processed grid data; Hole filling is performed on the grid data after the first data preprocessing to obtain the grid data after the second data preprocessing; Extract the geometric feature data of each grid in the grid data after the second data preprocessing, and simplify the grid data after the second data preprocessing based on the geometric feature data to obtain the grid data after the third data preprocessing; An optimized 3D model is generated based on the preprocessed grid data from the third data source.

3. The method for optimizing dimensional deviations in the manufacture of subperiosteal implants as described in claim 1, characterized in that, The step of generating a dimension deviation cloud map based on several error judgment data sets and an initial 3D model includes: Determine the dimensional deviation corresponding to each error judgment data group based on each error judgment data group; By mapping the size deviation corresponding to each error judgment data group to the color space, a color space mapping diagram is obtained. The color space mapping is overlaid onto the initial 3D model to generate a size deviation cloud map.

4. A device for optimizing dimensional deviations in the manufacturing of subperiosteal implants, characterized in that, include: The module includes an initial 3D model building module, an optimized 3D model building module, and a deviation compensation optimization module. The initial three-dimensional model construction module is used to acquire optical scanner data at the site where the patient's subperiosteal implant is to be placed, and to generate an initial three-dimensional model of the subperiosteal implant to be optimized based on the optical scanner data. The optimized 3D model construction module is used to acquire the 3D coordinate information of each discrete point in the initial 3D model and convert the 3D coordinate information of each discrete point into point cloud data; wherein, the point cloud data includes a number of point cloud points; the point cloud points in the point cloud data are grouped according to the attributes of each point cloud point 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 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 3D model is generated based on 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 perform deviation compensation on the initial three-dimensional model to obtain the deviation-optimized three-dimensional model of the subperiosteal implant to be optimized; and to manufacture the subperiosteal implant according to the deviation-optimized three-dimensional model. The step of fitting the optimized 3D model and the initial 3D model to determine the deviation compensation margin includes: Fit the optimized 3D model and the initial 3D model to determine the size deviation cloud map; Determine the deviation compensation margin based on the dimensional deviation contour map; The process of fitting the optimized 3D model and the initial 3D model to determine the size deviation contour map includes: Align and fit the optimized 3D model and the initial 3D model to obtain an alignment result diagram of the optimized 3D model and the initial 3D model; Several error determination data groups are extracted from the alignment result image; wherein each error determination data group includes three-dimensional coordinate data obtained from the optimized three-dimensional model and three-dimensional coordinate data obtained from the initial three-dimensional model; the 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 dimension deviation cloud map is generated based on several error judgment data sets and the initial 3D model.

5. The subperiosteal implant manufacturing dimensional deviation optimization device as described in claim 4, characterized in that, The step of generating an optimized 3D model based on the optimized point cloud data includes: Mesh data is generated based on the optimized point cloud data; wherein, the mesh data includes: several triangular meshes and several polygonal meshes; The grid data is smoothed to obtain the first pre-processed grid data; Hole filling is performed on the grid data after the first data preprocessing to obtain the grid data after the second data preprocessing; Extract the geometric feature data of each grid in the grid data after the second data preprocessing, and simplify the grid data after the second data preprocessing based on the geometric feature data to obtain the grid data after the third data preprocessing; An optimized 3D model is generated based on the preprocessed grid data from the third data source.

6. The subperiosteal implant manufacturing dimensional deviation optimization device as described in claim 4, characterized in that, The step of generating a dimension deviation cloud map based on several error judgment data sets and an initial 3D model includes: Determine the dimensional deviation corresponding to each error judgment data group based on each error judgment data group; By mapping the size deviation corresponding to each error judgment data group to the color space, a color space mapping diagram is obtained. The color space mapping is overlaid onto the initial 3D model to generate a size deviation cloud map.

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