Dental model processing method, storage medium, and electronic device

Abstract

The application discloses a kind of dental model processing method, storage medium and electronic equipment.Therein, the method includes: obtaining the dental model to be processed, wherein the dental model to be processed is the three-dimensional digital model generated based on three-dimensional scanning data;Tooth is divided to the dental model to be processed, and the dental model after tooth division is obtained;The dental model after tooth division is cut, and the dental model after cutting is obtained;The dental model after cutting is added to the bottom, and the target dental model is obtained.The application solves the technical problem that the dental model processing method provided by the related art is based on gingival line identification method to cut the model, resulting in poor processing effect and low accuracy of dental model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital denture, in particular to a dental model processing method, a storage medium and an electronic device. BACKGROUND

[0002] At present, in the technical field of digital denture, the full-mouth scanning data of a customer is usually obtained by using an oral scanning technology, and then a full-process processing such as repair, bottoming and marking is performed, the processed model is exported to a local or pushed to a chair side, and is connected to other dental production systems, such as a pre-processing tool to obtain a slicing file by layout slicing, and finally a 3D printing is performed according to the slicing file to obtain a repaired dental model.

[0003] However, in the prior art, in the process of constructing a dental model, a gingival line recognition method is usually used to scan and cut the dental model (referred to as dental model), and the cutting method is difficult to accurately identify the position and edge of each tooth, thereby resulting in poor cutting effect and low accuracy of the processed dental model.

[0004] From the above analysis, it can be seen that the dental model processing method provided by the above related technology based on the gingival line recognition method for model cutting leads to poor processing effect and low accuracy of the dental model, and currently there is no effective solution to the problem. SUMMARY

[0005] The embodiments of the present application provide a dental model processing method, a storage medium and an electronic device to at least solve the technical problem of poor processing effect and low accuracy of the dental model caused by the dental model processing method provided by the related technology based on the gingival line recognition method for model cutting.

[0006] According to an aspect of an embodiment of the present application, a dental model processing method is provided, comprising:

[0007] obtaining a dental model to be processed, wherein the dental model to be processed is a three-dimensional digital model generated based on three-dimensional scanning data; dividing teeth of the dental model to be processed to obtain a dental model after tooth division; cutting the dental model after tooth division to obtain a dental model after cutting; and bottoming the dental model after cutting to obtain a target dental model.

[0008] Optionally, before the tooth model processing method further comprises: calculating a normal vector and a face area of any one triangular facet of the to-be-processed dental model to obtain a normal weight of the any one triangular facet; calculating a total area of the plurality of triangular facets and a sum of the normal weights of the plurality of triangular facets to obtain a total normal weight; calculating a unit vector of the target transformation direction and the total normal weight to obtain a target transformation matrix; and transforming the to-be-processed dental model according to the target transformation matrix to obtain the aligned dental model.

[0009] Optionally, the tooth segmentation of the to-be-processed dental model to obtain the segmented dental model comprises: performing tooth identification and marking on the aligned dental model based on a target recognition technology to obtain a marking result, wherein the marking result is used to determine a facet area corresponding to any one tooth; expanding any one facet area based on the marking result to obtain a plurality of tooth models; and sorting and aligning the plurality of tooth models to obtain the segmented dental model, wherein an arrangement order of the plurality of tooth models is determined by the marking result.

[0010] Optionally, the cutting of the segmented dental model to obtain the cut dental model comprises: determining a target enclosing area of the tooth model in the segmented dental model; performing distance detection on the triangular facets on the segmented dental model according to a preset distance threshold and the target enclosing area to determine redundant triangular facets in the segmented dental model, wherein the redundant triangular facets are triangular facets with a distance greater than the preset distance threshold from the target enclosing area; and removing the redundant triangular facets in the segmented dental model to obtain the cut dental model.

[0011] Optionally, the determination of the target enclosing area of the tooth model in the segmented dental model comprises: generating a corresponding convex hull according to a joint facet area of a plurality of adjacent tooth models in the segmented dental model, wherein the joint facet area is determined by three-dimensional grid cells of the plurality of adjacent tooth models; and splicing a plurality of convex hulls to obtain the target enclosing area of the segmented dental model.

[0012] Optionally, the distance detection on the triangular facets on the segmented dental model according to the preset distance threshold and the target enclosing area to determine the redundant triangular facets in the segmented dental model comprises: detecting a distance between a first edge point and a second edge point, wherein the first edge point is a lingual edge point or a labial edge point of the segmented dental model, and the second edge point is a corresponding point of the first edge point on the target enclosing area; comparing the preset distance threshold and the distance to obtain a comparison result, wherein the comparison result is used to determine whether the first edge point is an edge noise point; and in response to the comparison result determining that the first edge point is an edge noise point, determining a triangular facet on which the first edge point of the segmented dental model is located as a redundant triangular facet.

[0013] Optionally, the method further includes: generating a spherical region of each tooth model by taking the center of each tooth model as the center of a sphere and taking the point on the tooth model farthest from the center of the sphere as the radius; and combining the spherical regions of all tooth models to obtain a combined spherical region, and retaining the triangular facets of the post-segmentation dental model that belong to the combined spherical region.

[0014] Optionally, the method further includes: performing edge lifting detection on the post-trimming dental model to obtain an edge lifting detection result, wherein the edge lifting detection result is used to determine whether the post-trimming dental model has edge lifting, and the edge lifting refers to a triangular facet corresponding to a convex noise point in the post-trimming dental model; and in response to the edge lifting detection result indicating that the post-trimming dental model has edge lifting, removing the edge lifting in the post-trimming dental model to obtain a first trimmed model.

[0015] Optionally, the edge lifting detection on the post-trimming dental model includes: constructing a target data structure corresponding to the post-trimming dental model, wherein the target data structure is used to describe the association relationship between any point, any face, and any edge in the post-trimming dental model; determining an edge point in the post-trimming dental model according to the association relationship; performing height judgment on a facet region in which the edge point is located to obtain a judgment result, wherein the judgment result is used to determine whether the facet region in which the edge point is located is the highest point in a target direction; and in response to the judgment result indicating that the facet region in which the edge point is located is the highest point, determining a plurality of triangular facets connected to the edge point in the post-trimming dental model as the edge lifting.

[0016] Optionally, the adding of the bottom to the post-trimming dental model to obtain the target dental model includes: adding a virtual bottom to the post-trimming dental model to obtain an initial bottom-added model; determining a target edge of the initial bottom-added model according to a plurality of triangular facets of the initial bottom-added model; constructing an initial voxel model according to the target edge; performing boundary interpolation and smoothing processing on the initial voxel model to obtain a target voxel model; performing intersection detection on the triangular facets in a first candidate region and the triangular facets in a second candidate region, respectively, to obtain an intersection detection result, wherein the first candidate region is a candidate region in the initial bottom-added model, the second candidate region is a candidate region in the target voxel model, and the intersection detection result is used to determine an intersection candidate facet in the second candidate region; performing region division and region marking on the target voxel model based on the intersection candidate facet to obtain a marked dental model; and performing smoothing processing on the marked dental model to obtain the target dental model.

[0017] Optionally, the method further includes: performing flash trimming on the target dental model to obtain a second trimmed model, wherein the flash trimming is used to remove closed flash and open flash in the target dental model; and performing global repair on the second trimmed model to obtain a repaired target model.

[0018] Optionally, the dental model processing method further includes: calculating a wall thickness of any one three-dimensional mesh element in the target dental model by using a target projection method to obtain a calculation result; and performing region segmentation on the target dental model according to the calculation result by using a region segmentation algorithm to obtain the closed flash.

[0019] Optionally, the dental model processing method further includes: detecting any one triangular facet of the target dental model by using a ray detection method to obtain a detection result, wherein the detection result is used to determine a number of intersection points of the target ray and the any one triangular facet; and in response to the number of intersection points satisfying a preset condition, determining that the any one triangular facet is the open flash.

[0020] Optionally, the global repair at least includes: repairing an inverted normal of the second cropped model, removing a noise shell in the second cropped model, repairing a defect in the second cropped model, repairing a self-intersection shell in the second cropped model, merging an intersection shell of the second cropped model, and removing a shell nested in any one shell.

[0021] According to another aspect of the embodiments of the present application, a dental model processing device is also provided, which comprises:

[0022] The acquisition module is configured to acquire a dental model to be processed, wherein the dental model to be processed is a three-dimensional digital model generated based on three-dimensional scanning data; the segmentation module is configured to segment the dental model to be processed to obtain a segmented dental model; the cropping module is configured to crop the segmented dental model to obtain a cropped dental model; and the base adding module is configured to add a base to the cropped dental model to obtain a target dental model.

[0023] Optionally, before the segmented dental model to be processed is segmented, the dental model processing device further includes: a calculation module configured to calculate a normal vector and a facet area of any one triangular facet of the dental model to be processed to obtain a normal weight of the any one triangular facet; calculate a total facet area of a plurality of triangular facets and a sum of normal weights of the plurality of triangular facets to obtain a total normal weight; calculate a unit vector of a target transformation direction and the total normal weight to obtain a target transformation matrix; and transform the dental model to be processed according to the target transformation matrix to obtain a righted dental model.

[0024] Optionally, the tooth separating module is further configured to: separate the teeth of the to-be-processed dental model to obtain a dental model after tooth separation, including: performing tooth identification and marking on the aligned dental model based on a target identification technology to obtain a marking result, wherein the marking result is used to determine a facial region corresponding to any tooth; expanding any facial region based on the marking result to obtain a plurality of tooth models; and performing sorting and alignment processing on the plurality of tooth models to obtain the dental model after tooth separation, wherein the arrangement order of the plurality of tooth models is determined by the marking result.

[0025] Optionally, the cutting module is further configured to: cut the dental model after tooth separation to obtain a dental model after cutting, including: determining a target enclosing region of the tooth model in the dental model after tooth separation; performing distance detection on the triangular facets on the dental model after tooth separation according to a preset distance threshold and the target enclosing region to determine redundant triangular facets in the dental model after tooth separation, wherein the redundant triangular facets are triangular facets with a distance greater than the preset distance threshold from the target enclosing region; and removing the redundant triangular facets in the dental model after tooth separation to obtain the dental model after cutting.

[0026] Optionally, the cutting module is further configured to: determine the target enclosing region of the tooth model in the dental model after tooth separation, including: generating a corresponding convex hull according to a joint facial region of a plurality of adjacent tooth models in the dental model after tooth separation, wherein the joint facial region is determined by three-dimensional grid cells of the plurality of adjacent tooth models; and splicing a plurality of convex hulls to obtain the target enclosing region of the dental model after tooth separation.

[0027] Optionally, the cutting module is further configured to: perform distance detection on the triangular facets on the dental model after tooth separation according to a preset distance threshold and the target enclosing region to determine redundant triangular facets in the dental model after tooth separation, including: detecting a distance between a first edge point and a second edge point, wherein the first edge point is a lingual edge point or a labial edge point of the dental model after tooth separation, and the second edge point is a corresponding point of the first edge point on the target enclosing region; comparing the preset distance threshold and the distance to obtain a comparison result, wherein the comparison result is used to determine whether the first edge point is an edge noise point; and in response to the comparison result determining that the first edge point is an edge noise point, determining a triangular facet on which the first edge point is located on the dental model after tooth separation as a redundant triangular facet.

[0028] Optionally, the dental model processing apparatus further includes a generating module configured to: generate a spherical region of each tooth model by taking a center of each tooth model as a spherical center and a point on the tooth model with a maximum distance from the spherical center as a radius; and combine the spherical regions of all tooth models to obtain a joint spherical region, and retain triangular facets on the dental model after tooth separation that belong to the joint spherical region.

[0029] Optionally, the dental model processing apparatus further comprises: a rim clipping module configured to perform rim detection on the clipped dental model to obtain a rim detection result, wherein the rim detection result is used to determine whether the clipped dental model has a rim, and the rim is a triangular facet corresponding to a convex noise point in the clipped dental model; in response to the rim detection result indicating that the clipped dental model has a rim, the rim in the clipped dental model is removed to obtain a first clipped model.

[0030] Optionally, the rim clipping module is further configured to: perform rim detection on the clipped dental model to obtain a rim detection result, including: constructing a target data structure corresponding to the clipped dental model, wherein the target data structure is used to describe the association relationship between any one point, any one face, and any one edge in the clipped dental model; determining an edge point in the clipped dental model according to the association relationship; performing height judgment on a facet region where the edge point is located to obtain a judgment result, wherein the judgment result is used to determine whether the facet region where the edge point is located is the highest point in a target direction; in response to the judgment result indicating that the facet region where the edge point is located is the highest point, determining a plurality of triangular facets connected to the edge point in the clipped dental model as the rim.

[0031] Optionally, the bottom adding module is further configured to: add a bottom to the clipped dental model to obtain a target dental model, including: adding a virtual bottom to the clipped dental model to obtain an initial bottom-added model; determining a target edge of the initial bottom-added model according to a plurality of triangular facets of the initial bottom-added model; constructing an initial voxel model according to the target edge; performing boundary interpolation and smoothing processing on the initial voxel model to obtain a target voxel model; performing intersection detection on triangular facets in a first candidate region and triangular facets in a second candidate region respectively to obtain an intersection detection result, wherein the first candidate region is a candidate region in the initial bottom-added model, the second candidate region is a candidate region in the target voxel model, and the intersection detection result is used to determine an intersection candidate facet in the second candidate region; performing region division and region marking on the target voxel model based on the intersection candidate facet to obtain a marked dental model; performing smoothing processing on the marked dental model to obtain the target dental model.

[0032] Optionally, the dental model processing apparatus further comprises: a flash clipping module configured to perform flash clipping on the target dental model to obtain a second clipped model, wherein the flash clipping is used to remove closed flash and open flash in the target dental model; performing global repair on the second clipped model to obtain a repaired target model.

[0033] Optionally, the dental model processing device further includes: a first detection module, used to calculate the wall thickness of any three-dimensional mesh unit in the target dental model using the target projection method, and obtain the calculation result; and to perform region segmentation on the target dental model according to the calculation result using a region segmentation algorithm to obtain a closed flash.

[0034] Optionally, the dental model processing device further includes: a second detection module, used to detect any triangular facet of the target dental model using ray detection method to obtain detection results, wherein the detection results are used to determine the number of intersections between the target ray and any triangular facet; in response to the number of intersections meeting a preset condition, any triangular facet is determined to be an open flash.

[0035] Optionally, the above-mentioned trimming module is also used for: global repair including at least: repairing the inverted normal of the second trimming model, removing the noise shell in the second trimming model, repairing defects in the second trimming model, repairing the self-intersecting shell of the second trimming model, merging the intersecting shells of the second trimming model, and removing the shell nested inside any shell.

[0036] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to execute the dental model processing method of any of the preceding claims.

[0037] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the executable program, wherein the executable program executes the dental model processing method of any of the preceding embodiments.

[0038] In this embodiment of the invention, a dental model to be processed is first obtained, which is a three-dimensional digital model generated based on three-dimensional scanning data. Next, the dental model to be processed is segmented to obtain a segmented dental model. Then, the segmented dental model is trimmed to obtain a trimmed dental model. Finally, the trimmed dental model is rubbed to obtain the target dental model. By sequentially segmenting, trimming, and rubbing the dental model to be processed, the goal of accurately constructing a digital prosthesis is achieved. This realizes the technical effect of improving the trimming effect of the dental model and increasing the accuracy of the target dental model by using a method of segmenting before trimming. Furthermore, it solves the technical problem of poor processing effect and low accuracy of dental models caused by model trimming based on gingival line recognition methods provided by related technologies. Attached Figure Description

[0039] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0040] Figure 1 is a hardware structure block diagram of an optional mobile terminal for a dental model processing method according to an embodiment of the application;

[0041] Figure 2 is a flow chart of a dental model processing method according to an embodiment of the application;

[0042] Figure 3 is a schematic diagram of a dental model to be processed according to an embodiment of the application;

[0043] Figure 4 is a schematic diagram of a dental model to be processed according to an embodiment of the application;

[0044] Figure 5 is a schematic diagram of a dental model to be processed according to an embodiment of the application;

[0045] Figure 6 is a schematic diagram of a dental model to be processed according to an embodiment of the application;

[0046] Figure 7 is a schematic diagram of a dental model to be processed according to an embodiment of the application;

[0047] Figure 8 is a schematic diagram of a dental model to be processed according to an embodiment of the application;

[0048] Figure 9 is a schematic diagram of a dental model to be processed according to an embodiment of the application;

[0049] Figure 10 is a schematic diagram of a dental model to be processed according to an embodiment of the application;

[0050] Figure 11 is a schematic diagram of a dental model to be processed according to an embodiment of the application;

[0051] Figure 12 is a schematic diagram of a dental model to be processed according to an embodiment of the application;

[0052] Figure 13 is a schematic diagram of a dental model to be processed according to an embodiment of the application;

[0053] Figure 14 is a schematic diagram of a dental model to be processed according to an embodiment of the application;

[0054] Figure 15 is a schematic diagram of a bottom tooth model adding device according to an embodiment of the present application;

[0055] Figure 16 is a structural block diagram of a dental model processing device according to an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0057] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0058] According to an embodiment of the present application, a method embodiment of a dental model processing method is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0059] Figure 1 is a hardware structural block diagram of an optional mobile terminal for a dental model processing method according to an embodiment of the present application, such as Figure 1As shown, the mobile terminal 10 (or mobile device 10) can include one or more processors 102 (which can include, but are not limited to, processing devices such as a Microcontroller Unit (MCU) or a Field Programmable Gate Array (FPGA)), a memory 104 for storing data, and a transceiver 106 for communication functions. In addition, it can also include a display device 110, an input / output device 108 (i.e., I / O device), a Universal Serial Bus (USB) port (which can be included as one of the ports of a computer bus, not shown in the figure), a network interface (not shown in the figure), a power supply (not shown in the figure), and / or a camera (not shown in the figure). Those skilled in the art will understand that the mobile terminal 10 can include more or less components, or a different configuration of components, than those shown in the figure, depending in part on the implementation. Figure 1 The structure shown is merely illustrative and does not limit the structure of the mobile terminal 10 described above. For example, the mobile terminal 10 can include more or less components than those shown in the figure, or have a different configuration of components than those shown in the figure. Figure 1 Figure 1

[0060] It should be noted that the one or more processors 102 and / or other data processing circuitry described above can be embodied in whole or in part as software, hardware, firmware, or any combination thereof. In addition, the data processing circuitry can be a single independent processing module, or be incorporated in whole or in part into any one of the other elements of the mobile terminal 10 (or mobile device).

[0061] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage means corresponding to the dental model processing method of the embodiments of the present application. The processor 102 can execute various functional applications and data processing by running the software programs and modules stored in the memory 104, i.e., implement the dental model processing method described above. The memory 104 can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory disposed remotely with respect to the processor 102, which can be connected to the mobile terminal 10 through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0062] ​​The transmission device 106 is configured to receive or send data via a network. The network can include a wireless network provided by a communication provider of the mobile terminal 10. In one example, the transmission device 106 includes a network interface controller (NIC) configured to connect to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module configured to communicate with the Internet via a wireless network.

[0063] In the above operating environment, the embodiments of the present application provide a dental model processing method as shown in Figure 2 Figure 2 is a flowchart of a dental model processing method according to an embodiment of the present application, as shown in Figure 2

[0064] In step S201, a dental model to be processed is obtained, wherein the dental model to be processed is a three-dimensional digital model generated based on three-dimensional scanning data.

[0065] In step S202, the dental model to be processed is segmented to obtain a segmented dental model.

[0066] In step S203, the segmented dental model is trimmed to obtain a trimmed dental model.

[0067] In step S204, the trimmed dental model is bottomed to obtain a target dental model.

[0068] The dental model can be a three-dimensional digital model obtained by processing and converting three-dimensional scanning data. The three-dimensional scanning data can include, but is not limited to, the following information or data: shape and position of teeth, size and proportion of teeth, shape and position of gums, color and texture of teeth, overall structure and morphology of the oral cavity, information of soft tissue in the oral cavity (e.g., position and morphology of the tongue). Exemplarily, the three-dimensional scanning data can be obtained by scanning the oral cavity of a user by a three-dimensional scanning device, or by scanning a dental impression model. The present application does not limit the data acquisition method.

[0069] The segmentation operation can be used to determine the position of each tooth in the target oral cavity, the relative position and order of all teeth in the dental model to be processed. The trimming operation can be used to delete the redundant parts (e.g., the edge) other than the necessary parts (e.g., teeth and gums) based on the information of each tooth obtained by the segmentation operation, so as to facilitate subsequent construction of a denture matching the tooth structure in the target oral cavity. The bottoming operation is used to add a bottom to the trimmed dental model to facilitate 3D printing of the target dental model obtained after bottoming.​​

[0070] In this embodiment of the invention, a dental model to be processed is first obtained, which is a three-dimensional digital model generated based on three-dimensional scanning data. Next, the dental model to be processed is segmented to obtain a segmented dental model. Then, the segmented dental model is trimmed to obtain a trimmed dental model. Finally, the trimmed dental model is rubbed to obtain the target dental model. By sequentially segmenting, trimming, and rubbing the dental model to be processed, the goal of accurately constructing a digital prosthesis is achieved. This realizes the technical effect of improving the trimming effect of the dental model and increasing the accuracy of the target dental model by using a method of segmenting before trimming. Furthermore, it solves the technical problem of poor processing effect and low accuracy of dental models caused by model trimming based on gingival line recognition methods provided by related technologies.

[0071] The methods described in the embodiments of the present invention will be further described below.

[0072] In an optional embodiment, the dental model processing method further includes, before separating the teeth of the dental model to be processed:

[0073] Step S251: Calculate the normal vector and area of ​​any triangular facet of the dental model to be processed to obtain the normal weight of any triangular facet.

[0074] Step S252: Calculate the total area of ​​the multiple triangular facets and the sum of the normal weights of the multiple triangular facets to obtain the total normal weight.

[0075] Step S253: Calculate the total normal weight and the unit vector of the target transformation direction to obtain the target transformation matrix;

[0076] Step S254: Transform the dental model to be processed according to the target transformation matrix to obtain the straightened dental model.

[0077] In the technical solution provided by this invention, since the orientation of the imported dental model to be processed is uncertain, it is necessary to fix the placement posture of the dental model to be processed. Therefore, the tooth surface (e.g., Figure 3 (The thickened part of the line shown) is facing upwards. Specifically, the method could be: denoted as the normal vector of the i-th triangular facet of the dental model to be processed. i (x i ,y i ,z i The area of ​​a surface is denoted as area. i The normal weight of the i-th triangular facet is obtained by multiplying its normal vector by its area. iIt can be represented by the following formula (1):

[0078] w i =normal i ×area i Formula (1)

[0079] Next, the total number of faces of the dental model to be processed is denoted as N. Then, based on the sum of the normal weights of all triangular faces of the dental model to be processed... Total area of ​​the surface The total normal weight W is calculated (e.g.) Figure 3 The arrow shown is as shown in the following formula (2):

[0080]

[0081] Furthermore, assuming the target transformation is a rotation, the target transformation direction is the z-direction, and the unit vector in the z-direction is (0,0,1), the total weight of the normal and this unit vector are calculated to obtain the target rotation matrix. Then, based on this target rotation matrix, the dental model to be processed is rotated to obtain the following result: Figure 4 The dental model shown is rotated with the tooth surfaces facing upwards.

[0082] In one optional embodiment, in step S202, the dental model to be processed is segmented into teeth to obtain a segmented dental model including:

[0083] Step S221: Based on target recognition technology, the teeth of the straightened dental model are identified and marked to obtain the marking results, wherein the marking results are used to determine the face region corresponding to any tooth;

[0084] Step S222: Based on the marking results, any one of the face regions is expanded to obtain multiple tooth models;

[0085] Step S223: Sort and align multiple tooth models to obtain a dental model after tooth separation. The arrangement order of the multiple tooth models is determined by the marking results.

[0086] The aforementioned target recognition technology can be, but is not limited to, computer vision technology, deep learning technology, and machine learning technology. In the technical solution provided by this invention, the marking result after tooth recognition and marking using target recognition technology can be as follows: Figure 5 As shown, Figure 5 In the model, rectangular boxes are used to mark the surface areas of each tooth in the aligned dental model.

[0087] After the tooth recognition and marking, the recognition result is projected to the aligned dental model, the bottom points of the ray filtering model are used to expand the remaining top points of the model, and in the expansion process, the curvature value of-0.5 can be set as the expansion end condition, and the expansion area of the model can also be limited based on the mask inflation or the frame expansion method. It should be noted that, in order to avoid the expansion of the tooth to the gum part, the area outside the tooth can be marked in advance.

[0088] After the tooth expansion is completed, the teeth are sorted, and the specific method can be: first, the center of the single tooth model is projected to two dimensions, and the center point of the single tooth model is calculated, a plurality of vectors are constructed with the center point of each tooth model as the starting point and the gravity point as the key point to obtain a vector set, then, with any one vector as the starting vector, the vector closest to the vector in the counterclockwise direction is searched, the included angle of any two closest vectors is calculated, further, the two vectors with the largest included angle in the calculation result are taken as two end points, the midpoint of the two end points is calculated, and each tooth model is sorted with the midpoint to each tooth model center point as a reference. The expanded tooth model after sorting is subjected to overlap judgment and elimination processing, finally, the mesh area of each tooth is generated based on the recognition result to obtain the tooth separation result, and all teeth are spliced and re-aligned according to the order to obtain the tooth separation dental model as shown in FIG. 8. Figure 6

[0089] It should be noted that the tooth separation dental model can be horizontally aligned and adjusted, specifically: the oriented bounding box (OBB) of the tooth separation dental model is obtained, and the corresponding alignment direction is generated combined with the model triangle facet normal and the direction of the maximum face of the oriented bounding box, so that the maximum face of the oriented bounding box is aligned with the target axis (such as the z-axis).

[0090] In an optional embodiment, in step S203, the tooth separation dental model is cropped to obtain a cropped dental model, including:

[0091] Step S231, determining a target enclosing area of a tooth model in the tooth separation dental model;

[0092] Step S232, performing distance detection on the triangular facets on the tooth separation dental model according to a preset distance threshold and the target enclosing area, to determine redundant triangular facets in the tooth separation dental model, wherein the redundant triangular facets are triangular facets with a distance greater than the preset distance threshold from the target enclosing area;

[0093] Step S233, removing the redundant triangular facets in the tooth separation dental model to obtain the cropped dental model.

[0094] ​In step S231, determining the target enclosing region of the tooth model in the tooth-separated dental model comprises:

[0095] In step S2311, a corresponding convex hull is generated according to the joint patch region of the plurality of adjacent tooth models in the tooth-separated dental model, wherein the joint patch region is determined by the three-dimensional mesh cells of the plurality of adjacent tooth models.

[0096] In step S2312, the plurality of convex hulls are spliced to obtain the target enclosing region of the tooth-separated dental model.

[0097] In step S232, the triangular patches on the tooth-separated dental model are distance detected according to the preset distance threshold and the target enclosing region to determine the redundant triangular patches in the tooth-separated dental model, comprising:

[0098] In step S2321, the distance between a first edge point and a second edge point is detected, wherein the first edge point is a lingual edge point or a labial edge point of the tooth-separated dental model, and the second edge point is a corresponding point of the first edge point on the target enclosing region; the preset distance threshold and the distance are compared to obtain a comparison result, wherein the comparison result is used to determine whether the first edge point is an edge noise point.

[0099] In step S2322, in response to the comparison result determining that the first edge point is the edge noise point, the triangular patch on the tooth-separated dental model where the first edge point is located is determined as the redundant triangular patch.

[0100] In the technical solution provided by the present application, the convex hull enclosing box can be generated based on the mesh region of all teeth obtained after tooth separation, and the convex hull enclosing box top view is obtained, as shown in Figure 7 The thickened arch-shaped part is the lingual region, the point on the edge of the arch-shaped part is the lingual edge point, and the irregular part around the arch-shaped part is the labial region, and the point on the edge of the irregular part is the labial edge point.

[0101] Further, based on the mesh region of each tooth in the tooth-separated dental model, the mesh regions of a plurality of adjacent teeth are spliced together, and a corresponding convex hull enclosing box is generated according to the spliced mesh region, as shown in Figure 8 Further, the plurality of different convex hull enclosing boxes are spliced together in order to obtain the target enclosing region of all tooth models in the tooth-separated dental model, as shown in Figure 9 Further, the data structure of the point set on the overall enclosing region is constructed, and it should be noted that the data structure can be a K-d tree (K-dimensional tree), and the K-d tree can be used to quickly find the points on the overall enclosing region.

[0102] Further, the distance between any one lingual edge point or any one labial edge point in the tooth model after tooth separation and the corresponding point (or called mapping point) on the target enclosing area is detected, and when the distance is greater than a preset distance threshold, it is determined that the lingual edge point or the labial edge point corresponding to the distance is a noise point (or called redundant point) in the tooth model after tooth separation, and then the triangular facet in which the lingual edge point or the labial edge point in the tooth model after tooth separation is located (i.e. the redundant triangular facet) is removed, to obtain the tooth model after cutting.

[0103] In an optional embodiment, the tooth model processing method further includes:

[0104] In step S261, the center of gravity of each tooth model is taken as the center of a sphere, and the point on the tooth model with the maximum distance from the center of the sphere is taken as the radius, to generate the spherical region of each tooth model.

[0105] In step S262, the spherical regions of all tooth models are combined to obtain a combined spherical region, and the triangular facets on the tooth model after tooth separation that belong to the combined spherical region are retained.

[0106] In the technical solution provided by the application, in order to avoid the incomplete tooth recognition when the mark is recognized, which leads to the error cutting of the triangular facets in the tooth area in the cutting process, before the above cutting, the center of gravity of each tooth model in the tooth model after tooth separation is taken as the center of a sphere, and the point on the tooth model with the maximum distance from the center of the sphere is taken as the radius, to generate the spherical region of each tooth model, and then the spherical regions of all tooth models are combined to obtain a combined spherical region as shown in FIG. 8, and then the triangular facets on the tooth model after tooth separation that belong to the combined spherical region are retained. Figure 10

[0107] In an optional embodiment, the tooth model processing method further includes:

[0108] In step S271, the tooth model after cutting is subjected to edge lifting detection to obtain an edge lifting detection result, wherein the edge lifting detection result is used to determine whether the tooth model after cutting has edge lifting, and the edge lifting is the triangular facet corresponding to the convex noise point in the tooth model after cutting.

[0109] In step S272, in response to the determination that the tooth model after cutting has the edge lifting according to the edge lifting detection result, the edge lifting in the tooth model after cutting is removed to obtain a first cutting model.

[0110] In step S271, the tooth model after cutting is subjected to edge lifting detection to obtain an edge lifting detection result, wherein the edge lifting detection result is used to determine whether the tooth model after cutting has edge lifting, and the edge lifting is the triangular facet corresponding to the convex noise point in the tooth model after cutting.

[0111] ​Step S2711, constructing a target data structure corresponding to the cropped dental model, wherein the target data structure is used to describe the association relationship of any point, any face, and any edge in the cropped dental model.

[0112] Step S2712, determining an edge point in the cropped dental model according to the association relationship.

[0113] Step S2713, performing height judgment on the face region where the arbitrary edge point is located to obtain a judgment result, wherein the judgment result is used to determine whether the face region where the arbitrary edge point is located is the highest point in the target direction.

[0114] Step S2713, in response to the judgment result determining that the face region where the arbitrary edge point is located is the highest point, determining a plurality of triangular faces connected with the arbitrary edge point in the cropped dental model as the warped edge.

[0115] The target data structure can be a half-edge data structure. Based on the constructed target data structure corresponding to the cropped dental model, all edges of all triangular faces of the cropped dental model are traversed. When an edge belongs to a triangular face, it is confirmed that the edge is an edge of the cropped dental model, and the point on the edge is an edge point. Further, it is judged whether the triangular face region where each edge point is located belongs to the highest point of the cropped dental model in the target direction (for example, the z direction). When the condition is met, it is determined that the edge point is a point on the warped edge. All triangular faces connected with the point on the warped edge in the cropped dental model are removed to obtain a first cropped model (for example, as shown in FIG. 6). Figure 11

[0116] In an optional embodiment, in step S204, the cropped dental model is bottomed to obtain a target dental model, which includes:

[0117] Step S241, bottoming the cropped dental model to obtain a bottomed tooth model.

[0118] Step S242, performing a flash cutting on the bottomed tooth model to obtain a third cropped model, wherein the flash cutting is used to remove the flash in the bottomed tooth model.

[0119] Step S243, performing global repair on the third cropped model to obtain the target dental model.

[0120] In step S241, the cropped dental model is bottomed to obtain a bottomed tooth model, which includes:

[0121] ​Step S2411, a virtual bottom is added to the cropped dental model to obtain an initial bottom-added model;

[0122] Step S2412, a target edge of the initial bottom-added model is determined according to a plurality of triangular facets of the initial bottom-added model;

[0123] Step S2413, the initial voxel model is constructed according to the target edge;

[0124] Step S2414, boundary interpolation and smoothing processing are performed on the initial voxel model to obtain a target voxel model;

[0125] Step S2415, intersection detection is respectively performed on the triangular facets in the first candidate region and the triangular facets in the second candidate region to obtain an intersection detection result, wherein the first candidate region is a candidate region in the initial bottom-added model, the second candidate region is a candidate region in the target voxel model, and the intersection detection result is used to determine an intersection candidate facet in the second candidate region;

[0126] Step S2416, region division and region marking are performed on the target voxel model based on the intersection candidate facet to obtain a marked dental model;

[0127] Step S2416, smoothing processing is performed on the marked dental model to obtain a target dental model.

[0128] It is easy to understand that the cropped dental model does not have a bottom facet, and therefore, the cropped dental model cannot be directly used for 3D printing, and the cropped dental model needs to be bottom-added. The specific method can be: a plurality of virtual triangular facets are constructed at the bottom of the cropped dental model based on the bounding box, then the maximum outer edge (i.e., the target edge) of the grid of the original cropped dental model is found, a closed loop is constructed on the maximum outer edge boundary in combination with the constructed virtual triangular facets, the size of the voxel generation space (virtual bottom) is obtained, the original cropped dental model is combined with the virtual bottom, the size of the Layered Depth-Normal Image (LDNI) and other parameters are calculated based on the bounding volume of the model grid, the boundary interpolation points of the image are generated according to the image resolution and the edge points, and then the boundary internal region is filled to obtain an initial voxel model (as shown in Figure 12 ).

[0129] Further, based on the grid boundary surface of the original cropped dental model, the corresponding boundary surface at the boundary of the initial voxel model is removed, then the outermost circle boundary of the voxel model is found according to the boundary points of the initial voxel model after the boundary surface is removed, and boundary interpolation is performed on the outermost circle boundary according to the length of the boundary and the resolution (as shown in Figure 13The boundary-interpolated voxels are converted into a mesh, and the converted mesh model is preliminarily and integrally smoothed to obtain a target voxel model.

[0130] Further, the triangular facets on the target voxel model are subjected to intersection detection. Specifically, for the original cropped dental model, the boundary surface of the candidate region mesh is expanded inward by a preset number of times (for example, 15 times), and for the target voxel model, the bottom region is removed based on a height filtering method, and the intermediate filling region is removed based on an edge filtering method. Then, a specific data structure of the remaining region of the target voxel model is established, which can be a K-d tree structure based on a surface area heuristic (SAH). The edges of the original cropped dental model are used as rays, and when the collision length of the rays is less than or equal to the edge length in the K-d tree structure based on the SAH, it is considered that the two models intersect. Then, the grid points are marked according to the relationship between the grid and the points on the voxel model, and the corresponding points on the voxel model are found according to the unknown points on the grid to obtain the intersecting facets (as shown in Figure 14

[0131] After the intersecting facets are determined, the original cropped dental model and the target voxel model are subjected to region division and marking according to the intersecting facets. The specific method can be as follows: the surfaces outside the candidate region of the original cropped dental model are marked as external regions, and the intersecting regions are marked based on the surface normal and edge direction of the intersecting facets; the bottom region of the target voxel model is marked as an external region, the intermediate region is marked as an internal region, and the intersecting regions are marked based on the surface normal and edge direction of the intersecting facets to obtain a marked dental model. Further, the side edges and the bottom of the marked dental model are subjected to smoothing processing, and the bottom facets can be subjected to simplification processing to obtain a target dental model (as shown in Figure 15

[0132] In an optional embodiment, the above-mentioned dental model processing method further includes:

[0133] In step S281, the burr is cropped to obtain a second cropped model.

[0134] In step S282, the second cropped model is subjected to global repair to obtain a repaired target model.

[0135] ​​As an optional implementation, in order to further improve the accuracy of the printed dental model, the target dental model obtained after the bottoming can be subjected to burr trimming and global repair processing. Before the burr trimming, the target dental model needs to be subjected to burr detection. The burr detection process is shown in the following steps S2811 to S2814.

[0136] In an optional embodiment, the dental model processing method further includes:

[0137] Step S2811, calculating the wall thickness of any one three-dimensional grid unit in the target dental model by using a target projection method to obtain a calculation result.

[0138] Step S2812, performing region segmentation on the target dental model according to the calculation result by using a region segmentation algorithm to obtain a closed burr.

[0139] Since the target dental model can have burrs with small wall thickness and sudden changes in wall thickness, and the wall thickness of a normal dental model is greater than that of the burr and the wall thickness changes uniformly, the burr belongs to the noise region of the target dental model and needs to be detected and removed. The specific method can be: for a closed burr, the wall thickness of the target dental model can be calculated using point projection, ray projection, etc., and then the region segmentation algorithm can be used to segment the target dental model according to the wall thickness calculation result to segment out the closed burr, and then the closed burr is removed from the target dental model.

[0140] In an optional embodiment, the dental model processing method further includes:

[0141] Step S2813, detecting any one triangular facet of the target dental model by using a ray detection method to obtain a detection result, wherein the detection result is used to determine the number of intersection points of the target ray and the any one triangular facet.

[0142] Step S2814, in response to the number of intersection points satisfying a preset condition, determining that the any one triangular facet is an open burr.

[0143] For an open burr, since its wall thickness cannot be accurately detected or cannot be detected, the wall thickness of the open burr can be preset as a target value (for example, 0.1 mm). Then, for any one triangular facet, the center point of the triangle is offset by a preset distance in the direction opposite to the normal line, and the offset point is taken as the starting point and the direction opposite to the normal line as the direction to emit a ray. The number of intersection points of the ray and the target dental model is calculated. When the number of intersection points is even, it is determined that the triangular facet is an open burr, and the open burr is removed from the target dental model.

[0144] In an optional embodiment, in step S282, the global repairing at least includes: repairing the inverse normal of the second clipping model, removing the noise shell in the second clipping model, repairing the defects in the second clipping model, repairing the self-intersecting shell of the second clipping model, merging the intersecting shells of the second clipping model, and removing the shell nested inside any one shell.

[0145] The defects in the second clipping model can be small shells, holes and the like in the model. By performing global repairing on the second clipping model after removing the closed flash and the open flash, the model error is sufficiently reduced, thereby facilitating improvement of the adaptation of the denture based on the model to the oral cavity of the target user and improvement of the user experience.

[0146] The dental model processing method provided in the above embodiments of the application first constructs an initial dental model according to scanned oral data, and then performs alignment, tooth separation, preliminary clipping (i.e., removing the redundant triangular facets in the dental model after tooth separation), edge lifting clipping, bottom adding, flash clipping, global repairing and the like on the initial dental model, so as to achieve the following technical effects:

[0147] (1) In the tooth separation process, the positions of the teeth in the dental model are identified and marked based on computer vision and other artificial intelligence methods. The tooth positions determined by this method have high accuracy, so that the positions of the teeth in the dental model after tooth separation are more accurate.

[0148] (2) The dental model after tooth separation is sequentially subjected to multiple fine clipping operations such as preliminary clipping and edge lifting clipping, so as to remove the edge noise and the lifted edge on the model, and further improve the accuracy of the model.

[0149] (3) In the process of adding a bottom to the model, a corresponding voxel model is constructed, boundary processing, intersection detection, region marking, boundary interpolation and smoothing processing and the like are performed, so as to improve the adaptation of the bottom adding region to the original dental model and reduce the error of the intersection part of the bottom adding region and the original dental model, thereby helping to generate an accurate denture and improving the user's dental treatment experience.

[0150] (4) The target dental model obtained after bottom adding is subjected to flash clipping and global repairing. In the process of flash clipping, the closed flash and the open flash are effectively detected and clipped, and various flashes in the model are effectively removed. The global repairing operation repairs various defects of the second clipping model obtained after flash clipping, so as to further improve the accuracy of the printed denture.

[0151] In the present embodiment, a dental model processing apparatus is also provided for implementing the above-described embodiments and preferred embodiments, which have been described above and will not be repeated. As used below, a "module" is a combination of software and / or hardware that can implement a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

[0152] Figure 16 is a structural block diagram of a dental model processing apparatus according to an embodiment of the present application, as shown in Figure 16 , the apparatus comprises:

[0153] The acquisition module 1601 is configured to acquire a dental model to be processed, wherein the dental model to be processed is a three-dimensional digital model generated based on three-dimensional scanning data.

[0154] The tooth separation module 1602 is configured to separate teeth of the dental model to be processed to obtain a dental model after tooth separation.

[0155] The cropping module 1603 is configured to crop the dental model after tooth separation to obtain a dental model after cropping.

[0156] The bottom adding module 1604 is configured to add a bottom to the dental model after cropping to obtain a target dental model.

[0157] Optionally, before the dental model to be processed after tooth separation is separated, the dental model processing apparatus further comprises a calculation module 1605 (not shown in the figure) configured to calculate a normal vector and a face area of any one triangular facet of the dental model to be processed to obtain a normal weight of the any one triangular facet; calculate a total area of the plurality of triangular facets and a sum of the normal weights of the plurality of triangular facets to obtain a total normal weight; calculate the total normal weight and a unit vector of a target transformation direction to obtain a target transformation matrix; and transform the dental model to be processed based on the target transformation matrix to obtain a dental model after alignment.

[0158] Optionally, the tooth separation module 1602 is further configured to separate teeth of the dental model to be processed to obtain a dental model after tooth separation, including: performing tooth identification and marking on the dental model after alignment based on a target recognition technology to obtain a marking result, wherein the marking result is used to determine a facet area corresponding to any one tooth; expanding any one facet area based on the marking result to obtain a plurality of tooth models; and sorting and aligning the plurality of tooth models to obtain the dental model after tooth separation, wherein an arrangement order of the plurality of tooth models is determined by the marking result.

[0159] Optionally, the clipping module 1603 is further configured to clip the tooth model after segmentation to obtain a clipped dental model, including: determining a target bounding area of the tooth model in the dental model after segmentation; performing distance detection on the triangular facets on the dental model after segmentation according to the target bounding area and a preset distance threshold, to determine redundant triangular facets in the dental model after segmentation, wherein the redundant triangular facets are triangular facets with a distance greater than the preset distance threshold from the target bounding area; and removing the redundant triangular facets in the dental model after segmentation to obtain the clipped dental model.

[0160] Optionally, the clipping module 1603 is further configured to determine the target bounding area of the tooth model in the dental model after segmentation, including: generating corresponding convex hulls according to joint facet areas of the plurality of adjacent tooth models in the dental model after segmentation, wherein the joint facet areas are determined by three-dimensional grid cells of the plurality of adjacent tooth models; and splicing the plurality of convex hulls to obtain the target bounding area of the dental model after segmentation.

[0161] Optionally, the clipping module 1603 is further configured to perform distance detection on the triangular facets on the dental model after segmentation according to the target bounding area and a preset distance threshold, to determine redundant triangular facets in the dental model after segmentation, including: detecting a distance between a first edge point and a second edge point, wherein the first edge point is a lingual edge point or a labial edge point of the dental model after segmentation, and the second edge point is a corresponding point of the first edge point on the target bounding area; comparing the distance with the preset distance threshold to obtain a comparison result, wherein the comparison result is used to determine whether the first edge point is an edge noise point; and in response to the comparison result determining that the first edge point is an edge noise point, determining a triangular facet on which the first edge point is located on the dental model after segmentation as a redundant triangular facet.

[0162] Optionally, the dental model processing apparatus further includes a generating module 1606 (not shown in the figure) configured to generate a spherical area of each tooth model by taking a center of each tooth model as a spherical center and a point on the tooth model with a maximum distance from the spherical center as a radius, and to jointly the spherical areas of all tooth models to obtain a joint spherical area, and to retain triangular facets on the dental model after segmentation that belong to the joint spherical area.

[0163] Optionally, the dental model processing apparatus further includes a beading clipping module 1607 (not shown in the figure) configured to perform beading detection on the clipped dental model to obtain a beading detection result, wherein the beading detection result is used to determine whether the clipped dental model has beading, and the beading is a triangular facet corresponding to a convex noise point in the clipped dental model; and in response to the beading detection result determining that the clipped dental model has beading, removing the beading in the clipped dental model to obtain a first clipped model.

[0164] Optionally, the edge lifting cutting module 1607 is further configured to perform edge lifting detection on the cut dental model to obtain an edge lifting detection result, including: constructing a target data structure corresponding to the cut dental model, wherein the target data structure is used to describe the association relationship of any one point, any one face, or any one edge in the cut dental model; determining an edge point in the cut dental model according to the association relationship; performing height judgment on a face region where any one edge point is located to obtain a judgment result, wherein the judgment result is used to determine whether the face region where any one edge point is located is the highest point in a target direction; in response to the judgment result determining that the face region where any one edge point is located is the highest point, determining a plurality of triangular facets connected to any one edge point in the cut dental model as edge lifting.

[0165] Optionally, the bottom adding module 1604 is further configured to add a bottom to the cut dental model to obtain a target dental model, including: adding a virtual bottom to the cut dental model to obtain an initial bottom-added model; determining a target edge of the initial bottom-added model according to a plurality of triangular facets of the initial bottom-added model; constructing an initial voxel model according to the target edge; performing boundary interpolation and smoothing processing on the initial voxel model to obtain a target voxel model; performing intersection detection on triangular facets in a first candidate region and triangular facets in a second candidate region respectively to obtain an intersection detection result, wherein the first candidate region is a candidate region in the initial bottom-added model, the second candidate region is a candidate region in the target voxel model, and the intersection detection result is used to determine an intersection candidate facet in the second candidate region; performing region division and region marking on the target voxel model based on the intersection candidate facet to obtain a marked dental model; performing smoothing processing on the marked dental model to obtain the target dental model.

[0166] Optionally, the dental model processing apparatus further includes a flash cutting module 1608 (not shown in the figure) configured to perform flash cutting on the target dental model to obtain a second cut model, wherein the flash cutting is used to remove closed flash and open flash in the target dental model; performing global repair on the second cut model to obtain a repaired target model.

[0167] Optionally, the dental model processing apparatus further includes a first detection module 1609 (not shown in the figure) configured to calculate the wall thickness of any one three-dimensional grid unit in the target dental model using a target projection method to obtain a calculation result; and performing region segmentation on the target dental model according to the calculation result using a region segmentation algorithm to obtain closed flash.

[0168] Optionally, the dental model processing apparatus further comprises a second detection module 1610 (not shown in the figure) configured to detect any one of the triangular facets of the target dental model by using a ray detection method to obtain a detection result, wherein the detection result is used to determine the number of intersection points of the target ray and the any one of the triangular facets; and in response to the number of intersection points satisfying a preset condition, the any one of the triangular facets is determined as an open flash.

[0169] Optionally, the flash cutting module 1608 is further configured to perform global repair, including: repairing the reverse normal of the second cutting model, removing the noise shell in the second cutting model, repairing the defects in the second cutting model, repairing the self-intersecting shell in the second cutting model, merging the intersecting shells of the second cutting model, and removing the shell nested in any one of the shells.

[0170] It should be noted that the above modules can be implemented by software or hardware, and for the latter, the following implementation manners can be used, but are not limited thereto: the above modules are located in the same processor; or the above modules are located in different processors in any combination.

[0171] According to another aspect of the embodiments of the present application, a computer readable storage medium is also provided, which comprises a stored executable program, wherein the executable program controls the device where the computer readable storage medium is located to perform the dental model processing method of any one of the preceding aspects when the executable program is running.

[0172] Optionally, in the present embodiment, the storage medium can be configured to store a computer program for performing the following steps:

[0173] Step S1, obtaining a dental model to be processed, wherein the dental model to be processed is a three-dimensional digital model generated based on three-dimensional scanning data;

[0174] Step S2, performing tooth segmentation on the dental model to be processed to obtain a dental model after tooth segmentation;

[0175] Step S3, performing cutting on the dental model after tooth segmentation to obtain a dental model after cutting;

[0176] Step S4, performing bottoming on the dental model after cutting to obtain a target dental model.

[0177] Optionally, in the present embodiment, the storage medium can include but is not limited to: a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store computer programs.

[0178] According to a further aspect of the embodiments of the present application, an electronic device is also provided, comprising a memory storing an executable program; and a processor configured to execute the executable program, wherein the executable program performs the dental model processing method of any one of the preceding aspects when executed.

[0179] Optionally, in the embodiment, the processor is configured to perform the following steps by using the computer program:

[0180] In step S1, a dental model to be processed is obtained, wherein the dental model to be processed is a three-dimensional digital model generated based on three-dimensional scanning data.

[0181] In step S2, the dental model to be processed is segmented to obtain a segmented dental model.

[0182] In step S3, the segmented dental model is trimmed to obtain a trimmed dental model.

[0183] In step S4, the trimmed dental model is bottomed to obtain a target dental model.

[0184] Optionally, in the embodiment, the specific examples can refer to the examples described in the above embodiments and optional implementation manners, and the embodiment will not be described here.

[0185] The serial numbers of the above embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0186] In the above embodiments of the present application, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0187] In the several embodiments of the present application, it should be understood that the disclosed technology can be implemented in other ways. Of course, the unit embodiment described above is only schematic. For example, the division of the units can be a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection between units can be indirect coupling or communication connection through some interface, unit or module, and can be electrical or other forms.

[0188] The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment.

[0189] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0190] When the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application, essentially or the part that contributes to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0191] The above is only the preferred embodiment of the present application, and it should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A dental cast processing method characterized by, The method comprises the following steps: acquiring a to-be-processed dental model, wherein the to-be-processed dental model is a three-dimensional digital model generated based on three-dimensional scanning data; calculating a normal vector and a face area of any one triangular facet of the to-be-processed dental model to obtain a normal weight of the any one triangular facet; calculating a total area of a plurality of triangular facets and a sum of normal weights of the plurality of triangular facets to obtain a total normal weight; calculating the total normal weight and a unit vector of a target transformation direction to obtain a target transformation matrix; transforming the to-be-processed dental model according to the target transformation matrix to obtain a dental model after alignment; performing tooth identification and marking on the dental model after alignment based on a target recognition technology to obtain a marking result, wherein the marking result is used to determine a facet area corresponding to any one tooth; expanding any one facet area based on the marking result to obtain a plurality of tooth models; sorting and aligning the plurality of tooth models to obtain a dental model after tooth separation, wherein an arrangement order of the plurality of tooth models is determined by the marking result; determining a target enclosing area of a tooth model in the dental model after tooth separation; performing distance detection on triangular facets on the dental model after tooth separation according to a preset distance threshold and the target enclosing area to determine redundant triangular facets in the dental model after tooth separation, wherein the redundant triangular facets are triangular facets with a distance greater than the preset distance threshold from the target enclosing area; removing the redundant triangular facets in the dental model after tooth separation to obtain a dental model after cutting; adding a base to the dental model after cutting to obtain a target dental model.

2. The dental cast processing method according to claim 1, characterized by, The method further comprises the following steps: generating a corresponding convex hull according to a joint facet area of a plurality of adjacent tooth models in the dental model after tooth separation, wherein the joint facet area is determined by three-dimensional grid cells of the plurality of adjacent tooth models; splicing a plurality of convex hulls to obtain a target enclosing area of the dental model after tooth separation.

3. The dental cast processing method according to claim 1, characterized by, The method further comprises the following steps: detecting a distance between a first edge point and a second edge point, wherein the first edge point is a lingual edge point or a labial edge point of the dental model after tooth separation, and the second edge point is a corresponding point of the first edge point on the target enclosing area; comparing the preset distance threshold and the distance to obtain a comparison result, wherein the comparison result is used to determine whether the first edge point is an edge noise point; in response to the comparison result determining that the first edge point is the edge noise point, determining a triangular facet on which the first edge point is located on the dental model after tooth separation as the redundant triangular facet.

4. The dental cast processing method according to claim 1, characterized by, The method further comprises the following steps: generating a spherical surface area of each tooth model by taking a barycenter of each tooth model as a spherical center and a point on the tooth model with a maximum distance from the barycenter as a radius. The spherical surface regions of all the tooth models are combined to obtain a combined spherical surface region, and triangular facets belonging to the combined spherical surface region on the divided dental model are reserved.

5. The dental cast processing method according to claim 1, characterized by, The method further comprises: edge lifting detection is performed on the cropped dental model to obtain an edge lifting detection result, wherein the edge lifting detection result is used to determine whether the cropped dental model has edge lifting, and the edge lifting is a triangular facet corresponding to a convex noise point in the cropped dental model; in response to the edge lifting detection result determining that the cropped dental model has the edge lifting, the edge lifting in the cropped dental model is removed to obtain a first cropped model.

6. The dental cast processing method according to claim 5, characterized by The edge lifting detection on the cropped dental model to obtain the edge lifting detection result comprises: a target data structure corresponding to the cropped dental model is constructed, wherein the target data structure is used to describe the association relationship of any one point, any one face, or any one edge in the cropped dental model; an edge point in the cropped dental model is determined according to the association relationship; a height judgment is performed on a facet region where any one edge point is located to obtain a judgment result, wherein the judgment result is used to determine whether the facet region where the any one edge point is located is the highest point in a target direction; in response to the judgment result determining that the facet region where the any one edge point is located is the highest point, a plurality of triangular facets connected with the any one edge point in the cropped dental model are determined as the edge lifting.

7. The dental cast processing method according to claim 1, characterized by, The adding of a bottom to the cropped dental model to obtain the target dental model comprises: a virtual bottom is added to the cropped dental model to obtain an initial bottom-added model; a target edge of the initial bottom-added model is determined according to a plurality of triangular facets of the initial bottom-added model; an initial voxel model is constructed according to the target edge; a target voxel model is obtained by performing boundary interpolation and smoothing processing on the initial voxel model; intersection detection is respectively performed on triangular facets in a first candidate region and triangular facets in a second candidate region to obtain an intersection detection result, wherein the first candidate region is a candidate region in the initial bottom-added model, the second candidate region is a candidate region in the target voxel model, and the intersection detection result is used to determine an intersection candidate facet in the second candidate region; the target voxel model is regionally divided and regionally marked based on the intersection candidate facet to obtain a marked dental model; the marked dental model is smoothed to obtain the target dental model.

8. The dental cast processing method according to claim 7, characterized by, The method further comprises: edge lifting cropping is performed on the target dental model to obtain a second cropped model, wherein the edge lifting cropping is used to remove closed edge lifting and open edge lifting in the target dental model; global repair is performed on the second cropped model to obtain a repaired target model.

9. The dental cast processing method according to claim 8, characterized in that, The method further comprises: a wall thickness of any one three-dimensional grid unit in the target dental model is calculated using a target projection method to obtain a calculation result; the target dental model is regionally divided according to the calculation result using a region segmentation algorithm to obtain the closed edge lifting.

10. The dental cast processing method according to claim 8, characterized by, The method further comprises: The ray detection method is used to detect any one triangular facet of the target dental model to obtain a detection result, wherein the detection result is used to determine the number of intersection points of a target ray and the any one triangular facet; In response to the number of intersection points satisfying a preset condition, the any one triangular facet is determined as the open flash.

11. The dental cast processing method according to claim 8, characterized by, The global repair at least includes: repairing the reverse normal of the second clipping model, removing the noise shell in the second clipping model, repairing the defects in the second clipping model, repairing the self-intersecting shell of the second clipping model, merging the intersecting shells of the second clipping model, and removing the shell nested in the inside of any one shell.

12. A computer-readable storage medium, characterized in that, The computer readable storage medium includes a stored executable program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to execute the dental model processing method of any one of claims 1-11 when the executable program is running.

13. An electronic device, comprising: Comprise: A memory storing an executable program; A processor for running the executable program, wherein the executable program executes the dental model processing method of any one of claims 1-11 when the executable program is running.

Images