A model processing method and apparatus, and an electronic device

Abstract

The application provides a model processing method and device and electronic equipment. The method comprises the following steps: obtaining a face model and an oral cavity model, the oral cavity model comprising a plurality of oral cavity three-dimensional points; identifying a first target region in which the oral cavity is located in the face model, the first target region comprising a plurality of target three-dimensional points; generating an adjustment parameter according to texture information of the plurality of oral cavity three-dimensional points and the plurality of target three-dimensional points; adjusting the texture information of the plurality of oral cavity three-dimensional points according to the adjustment parameter to obtain an adjusted oral cavity model; and obtaining target data according to the face model and the adjusted oral cavity model. The present scheme can accurately solve the problem of inconsistent texture, improve the naturalness of the target data, and has a high degree of automation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-dimensional models, and in particular to a model processing method and device and electronic equipment. BACKGROUND

[0002] In the prior art, when a user has oral treatment needs such as planting, orthodontics, patching, repairing, aesthetics, etc., the user needs to be shown the final treatment effect, at which point the oral model and the facial model need to be aligned and integrated to generate target data, and the corresponding before-and-after treatment effect is shown to the user based on the target data. However, in practice, the oral model is generated by scanning the user's mouth, and the facial model is generated by scanning the user's face externally. Different scanning devices are used when scanning the mouth and the face, and differences in scanning devices and imaging methods as well as different environmental light in the mouth and outside lead to inconsistent textures of the oral model and the facial model, affecting the naturalness, aesthetics, and user satisfaction of the final target data. For example, the tooth model scanned in the mouth is dark and yellowish, and the tooth area in the facial model scanned from the face is relatively whitish and bright, and direct alignment and integration of the two kinds of data often has a large difference in color, and cannot completely restore the true appearance. Some people manually adjust the target data through post-processing software or image processing algorithms to improve the inconsistent texture; however, this method relies on the subjective judgment of the operator and cannot accurately solve the problem of inconsistent texture, making the target data unable to achieve natural transition and poor in aesthetics, and the degree of automation and processing efficiency is extremely low. SUMMARY

[0003] The purpose of the embodiments of the present application is to provide a model processing method, device and electronic equipment which can obtain target data with natural transition of the texture of the facial skin and the oral cavity (prosthesis, teeth or gums).

[0004] In a first aspect, the present application provides a model processing method, which comprises:

[0005] obtaining a facial model and an oral model, the oral model comprising a plurality of oral three-dimensional points;

[0006] identifying a first target region in the facial model where the oral cavity is located, the first target region comprising a plurality of target three-dimensional points;

[0007] generating an adjustment parameter according to the texture information of the plurality of oral three-dimensional points and the plurality of target three-dimensional points;

[0008] adjusting the texture information of the plurality of oral three-dimensional points according to the adjustment parameter to obtain an adjusted oral model;

[0009] obtaining target data according to the facial model and the adjusted oral model.

[0010] In a second aspect, the present application provides a model processing device, the processing device comprising:

[0011] an acquisition module configured to acquire a face model and an oral cavity model, the oral cavity model comprising a plurality of oral cavity three-dimensional points;

[0012] a recognition module configured to recognize a first target region in the face model where the oral cavity is located, the first target region comprising a plurality of target three-dimensional points;

[0013] a generation module configured to generate an adjustment parameter according to texture information of the plurality of oral cavity three-dimensional points and the plurality of target three-dimensional points;

[0014] an adjustment module configured to adjust the texture information of the plurality of oral cavity three-dimensional points according to the adjustment parameter to obtain an adjusted oral cavity model;

[0015] a data module configured to obtain target data according to the face model and the adjusted oral cavity model.

[0016] In a third aspect, the present application provides an electronic device, the electronic device comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the model processing method described above.

[0017] In the present application, the face model and the oral cavity model are acquired, and the first target region in the face model where the oral cavity is located is recognized, the adjustment parameter is generated using the texture information of the target three-dimensional points in the first target region and the oral cavity three-dimensional points in the oral cavity model, and then the texture information of the oral cavity three-dimensional points is adjusted according to the adjustment parameter, the face model is registered with the adjusted oral cavity model after the adjustment, and the target data is generated. Therefore, the present application can obtain the target data in which the texture of the face skin and the oral cavity (prosthesis, teeth, or gums) naturally transitions, and the present application adjusts the texture information of the corresponding three-dimensional points in the oral cavity model adaptively through the adjustment parameter, so that the texture information of the oral cavity three-dimensional points after the adjustment is consistent with the face model, and after the target data is generated according to the face model and the oral cavity model, the entire target data does not have texture differences due to the inconsistency of the texture of the oral cavity model and the face model, but achieves global texture consistency and natural transition effect, enhances the authenticity of the target data, and compared with different texture models, improves the overall aesthetic effect of the target data, has stronger natural beauty, and has better viewing experience, which is beneficial for subsequent doctor and patient communication of treatment plans. In addition, in the above-mentioned manner, the entire texture consistent adjustment process can be automatically completed, which does not depend on the subjective judgment of the operator, can accurately solve the problem of texture inconsistency, and has high automation degree and processing efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly introduced.

[0019] Figure 1 A structural schematic diagram of an electronic device provided by the present application is provided.

[0020] Figure 2 A flowchart of a model processing method provided by the present application is provided. Figure 1

[0021] Figure 3 A schematic diagram of an oral model provided by the present application is provided.

[0022] Figure 4 A simulation schematic diagram of a facial model provided by the present application is provided.

[0023] Figure 5 A simulation schematic diagram of a model generated by the prior art is provided.

[0024] Figure 6 A simulation schematic diagram of target data provided by the present application is provided.

[0025] Figure 7 A flowchart of a model processing method provided by the present application is provided. Figure 1

[0026] Figure 8 A schematic diagram of a model processing device provided by the present application is provided. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. Similar labels and letters in the following drawings represent similar items, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used for distinguishing description, and cannot be understood as indicating or implying relative importance.

[0028] As shown in Figure 1 , the present application provides an electronic device 1 comprising at least one processor 11 and a memory 12, Figure 1 ​​Taking a processor 11 as an example, the processor 11 and memory 12 are connected via a bus 10. The memory 12 stores instructions that can be executed by the processor 11. The instructions are executed by the processor 11 to enable the electronic device 1 to perform all or part of the model processing method in the embodiments described below. The electronic device can be understood, by way of example, as a 3D scanner (oral scanning device and / or facial scanning device), mobile phone, tablet computer, laptop computer, desktop computer, smart TV, cloud server, VR / AR device, etc. In some embodiments, the electronic device 1 can be integrated with the 3D scanner (oral scanning device and / or facial scanning device) or can be connected to the 3D scanner (oral scanning device and / or facial scanning device) via wired or wireless means.

[0029] The memory 12 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0030] like Figure 2 As shown, this application provides a model processing method, which can be executed by the aforementioned electronic device or by the cloud. The method includes the following steps:

[0031] Step S110: Obtain the facial model and oral cavity model. The oral cavity model includes multiple three-dimensional points of the oral cavity.

[0032] This involves acquiring facial and oral models of the target user undergoing dental treatment, including but not limited to restorations, veneers, orthodontics, and implants. The oral model includes at least multiple 3D points, each containing both coordinate and texture information. It is understood that the facial and oral models can be for the same target user, or for different target users.

[0033] In this step, the medical staff can use different scanning devices or the same scanning device to scan the target object's mouth and face to obtain the target object's mouth model and face model. As shown in FIG. 1A, it is the mouth model of the target object obtained by the medical staff through the mouth scanning device (intraoral scanner, extraoral scanner, CBCT scanner, CT scanner, X-ray scanner, tablet computer or mobile phone, etc.); as shown in FIG. 1B, it is the face model of the target object obtained by the medical staff through the face scanning device (for example, face scanner, tablet computer, mobile phone or AR glasses, etc.). The intraoral scanner, extraoral scanner and face scanner are used to scan the target object by using laser or structured light scanning technology, so as to quickly and accurately obtain the corresponding model of the target object in a non-contact manner. The mouth scanning device and the face scanning device can be separately arranged or integrated together. In some embodiments, the mouth model can be obtained by CBCT, CT, X-ray scanning device, that is, the intraoral scanning of the target object can be performed by using computer tomography or magnetic resonance imaging scanning technology to obtain detailed mouth information of the target object. In some embodiments, the mouth model can also be a template tooth model, and the mouth model can also be obtained by the electronic device through simulation algorithm or can be obtained by modifying and adjusting the existing tooth model by the electronic device. In some embodiments, the face model can also be a template face model, and the face model can also be obtained by the electronic device through simulation algorithm or can be obtained by modifying and adjusting the existing face model by the electronic device. Figure 3 Figure 4 In some embodiments, the electronic device and the mouth scanning device and / or the face scanning device can be connected in a wired or wireless manner, and the electronic device can obtain the face model and the mouth model during or after the scanning process. Specifically, one or more scanning devices can send the face model and / or the mouth model to the electronic device, or the electronic device can actively obtain the face model and / or the mouth model from the scanning device, or the electronic device can receive pictures in the scanning process of the scanning device and perform real-time three-dimensional reconstruction to obtain the face model and / or the mouth model, or the electronic device can receive pictures in the scanning process of the mouth scanning device and / or the face scanning device and perform three-dimensional reconstruction to obtain the face model and / or the mouth model after the scanning is completed.

[0034] In some embodiments, the electronic device and the mouth scanning device and / or the face scanning device can be connected in a wired or wireless manner, and the electronic device can obtain the face model and the mouth model during or after the scanning process. Specifically, one or more scanning devices can send the face model and / or the mouth model to the electronic device, or the electronic device can actively obtain the face model and / or the mouth model from the scanning device, or the electronic device can receive pictures in the scanning process of the scanning device and perform real-time three-dimensional reconstruction to obtain the face model and / or the mouth model, or the electronic device can receive pictures in the scanning process of the mouth scanning device and / or the face scanning device and perform three-dimensional reconstruction to obtain the face model and / or the mouth model after the scanning is completed.

[0035] Step S120: identifying a first target region where the mouth is located in the face model, and the first target region includes a plurality of target three-dimensional points.

[0036] Each target three-dimensional point includes at least coordinate information and texture information of the target three-dimensional point.

[0037] ​In some embodiments, an identification model can be stored in advance in the electronic device, and the identification model is used to identify the first target region where the oral cavity is located from the face model. Optionally, the electronic device can obtain the identification model from other devices or the cloud; or the electronic device can train the identification model using deep learning technology and store the identification model after training. Wherein, when training the identification model, a training set and a test set can be obtained in advance, the training set and the test set both include a plurality of face models, the first target region where the oral cavity is located is labeled on each face model in the training set, and each face model is preprocessed such as denoising. Further, a neural network model of a corresponding type is selected as the identification model, and the training set is used to train the identification model, and the test set is used to verify the identification result of the identification model after training. If the verification result shows that the identification model can accurately identify the first target region, the training of the identification model is completed. Of course, it can be understood that other training methods known in the prior art can be used to train the identification model, and the present application does not limit this.

[0038] In some embodiments, the identification model can be a deep learning image recognition model or a semantic recognition model. The identification model can also be a deep learning image segmentation model or a semantic segmentation model, and after identifying the first target region where the oral cavity is located in the face model, the first target region can be labeled or segmented. In some embodiments, this step can include lip line extraction, and the first target region is identified according to the extracted lip line.

[0039] Of course, it can be understood that in some embodiments, after this step, step S121 of removing the first target region from the face model after identification is included.

[0040] Wherein, when removing the first target region in the face model, the source data of the first target region can be deleted, or the first target region is deleted from the intermediate product or the reconstructed model so that the display interface does not display the first target region, or the first target region is separated and hidden and not displayed on the interactive interface, or only semi-transparent display is performed, etc.

[0041] Step S130: generating an adjustment parameter according to the texture information of the plurality of oral cavity three-dimensional points and the plurality of target three-dimensional points.

[0042] Wherein, the adjustment parameter includes a parameter for adjusting the texture information of each oral cavity three-dimensional point, which can make the texture information of the face model and the oral cavity model closer; the texture information at least includes color / color information.

[0043] In some embodiments, before the step, the method further comprises a step S131 of registering the face model and the oral model to determine the correspondence between the plurality of oral three-dimensional points in the oral model and the plurality of target three-dimensional points in the first target region. Specifically, the face model and the oral model can be registered in a common coordinate system by using the common region of the face model and the oral model, so as to determine the one-to-one correspondence or the best fitting correspondence between the plurality of oral three-dimensional points in the oral model and the plurality of target three-dimensional points in the first target region. Based on the correspondence, the adjustment parameters for generating the texture information of the corresponding oral three-dimensional point from the texture information of any target three-dimensional point can be determined.

[0044] It should be noted that the step S131 can occur after the step S120 or before the step S120 or synchronously with the step S120. Alternatively, after the step S131, the face model and the oral model can be fused or merged to obtain fused data. When the fusion or merging is performed, it can be selected whether to remove the first target region from the face model.

[0045] In some embodiments, if the texture information is represented in the RGB space, i.e., represented according to the numerical values of the R channel, the G channel and the B channel, the adjustment parameters can be generated according to the R channel, the G channel and the B channel values of the oral three-dimensional points and according to the R channel, the G channel and the B channel values of the target three-dimensional points. For example, the R channel values of the oral three-dimensional points and the target three-dimensional points are subjected to ratio operation to obtain a first adjustment parameter for adjusting the R channel value of the oral three-dimensional point, the G channel values of the oral three-dimensional points and the target three-dimensional points are subjected to ratio operation to obtain a second adjustment parameter for adjusting the G channel value of the oral three-dimensional point, and the B channel values of the oral three-dimensional points and the target three-dimensional points are subjected to ratio operation to obtain a third adjustment parameter for adjusting the B channel value of the oral three-dimensional point.

[0046] Step S140: adjusting the texture information of the plurality of oral three-dimensional points according to the adjustment parameters to obtain an adjusted oral model.

[0047] In some embodiments, the adjustment can be a product operation of the adjustment parameters and the initial texture information (texture information before adjustment) of each oral three-dimensional point. If the texture information is represented in the RGB space, the first adjustment parameter and the initial R channel value of the oral three-dimensional point can be subjected to product operation, the second adjustment parameter and the initial G channel value of the oral three-dimensional point can be subjected to product operation, and the third adjustment parameter and the initial B channel value of the oral three-dimensional point can be subjected to product operation, so as to adjust the texture information of the oral three-dimensional point. After the texture information of all the oral three-dimensional points is adjusted, the adjusted oral model can be obtained.

[0048] Step S150: Obtain target data based on the facial model and the adjusted oral cavity model.

[0049] In some embodiments, the facial model and the adjusted oral cavity model can be overlaid in this step to obtain, as shown below. Figure 6 The target data is shown in the simulation diagram obtained without scanning. In some embodiments, the facial model and the adjusted oral cavity model are fused or merged, so that the adjusted oral cavity model is stitched to the first target region. After stitching, the result is as shown in the diagram. Figure 6 The target data is shown. In some embodiments, the step of removing the first target region from the facial model is not performed in this step, or step S121 is not executed, and the adjusted oral cavity model is directly used to replace the first target data in the facial model, thus obtaining the target data as shown. Figure 6 The target data is shown.

[0050] In some embodiments, if step S121 is not performed and the first target region is not removed, the specific steps for fusing or merging the facial model and the adjusted oral cavity model are as follows: First, using the common region of the facial model and the adjusted oral cavity model, the two are unified under a common coordinate system to achieve alignment and registration. Then, the first target region in the facial model is removed (deleted from the source data, or deleted from the intermediate product or the reconstructed model, or simply hidden from the interactive interface or semi-transparently displayed, etc.). The adjusted oral cavity model is then stitched to the first target region. After stitching, the target model target data is obtained.

[0051] In some embodiments, if step S121 removed the first target region and step S131 has already registered the facial model and the oral cavity model, then a registration and removal step is not required in this step. This step includes: directly changing the texture information of the oral cavity model while maintaining the registration and stitching of the oral cavity model and the facial model; or directly replacing the oral cavity model with the adjusted oral cavity model; or first registering the oral cavity model with the adjusted oral cavity model to obtain the position transformation matrix between the oral cavity model and the adjusted oral cavity model; based on the position transformation matrix obtained by registering the facial model and the oral cavity model and the position transformation matrix between the oral cavity model and the adjusted oral cavity model, the facial model and the adjusted oral cavity model can be fused and registered to obtain the following result. Figure 6 The target data is shown.

[0052] like Figure 5 (The simulation diagram obtained without scanning) shows the target data generated by existing technology without texture adjustment of the oral cavity model. According to... Figure 5 and Figure 6 As can be seen, after adjusting the texture of the oral cavity model using the method described in this application,Figure 6 The model is significantly better than Figure 5 The model has better texture consistency, achieving a natural transition effect, enhancing the realism of the target data, improving the overall aesthetic effect and natural beauty of the target data, providing a better viewing experience, and facilitating subsequent communication between doctors and patients regarding treatment plans.

[0053] In some embodiments, step S150 may be followed by post-processing actions such as adjusting the lighting and shadows of the target data. These post-processing actions further enhance the aesthetic appeal of the target data.

[0054] As can be seen, this application can obtain target data showing a natural transition in texture between facial skin and the oral cavity (restoration, teeth, or gums). Furthermore, this application adaptively adjusts the texture information of corresponding 3D points in the oral cavity model by adjusting parameters, ensuring that the texture information of the oral cavity's 3D points remains consistent with the facial model after adjustment. After registering the facial and oral cavity models to generate target data, the entire target data will not exhibit texture differences due to inconsistencies in texture between the oral and facial models; instead, it achieves global texture consistency, resulting in a natural transition and enhancing the realism of the target data. Compared to models with different textures, it improves the overall aesthetic effect of the target data, enhancing its natural beauty and providing a better viewing experience, which is beneficial for subsequent communication between doctors and patients regarding treatment plans. In addition, in the above method, the entire texture consistency adjustment process can be completed automatically, without relying on the operator's subjective judgment. It can accurately solve the problem of texture inconsistency, exhibiting a high degree of automation and processing efficiency with minimal human intervention.

[0055] It is understood that the electronic device may be equipped with proprietary software to execute the steps in the above embodiments. The execution order of the above steps is not limited.

[0056] In one embodiment, such as Figure 7 As shown, this application provides a model processing method, which can be executed by the aforementioned electronic device or by the cloud. The method includes the following steps:

[0057] Step S210: Obtain the facial model and oral cavity model. The oral cavity model includes multiple 3D points of the oral cavity. For details, please refer to step S110 above, which will not be repeated here.

[0058] Step S220: Identify the first target region in the facial model where the oral cavity is located. The first target region includes multiple target 3D points. See step S120 above for details, which will not be repeated here.

[0059] Step S231: Convert the facial model and oral cavity model from RGB space to HSV space to obtain the color information of the oral cavity 3D points and target 3D points.

[0060] In the RGB space, the texture information is represented by R channel value, G channel value and B channel value. In the HSV space, the texture information is represented by brightness information, saturation information and hue information. HSV (Hue, Saturation, Value) is a color model consistent with human visual perception. It can decompose color information into hue (color category, 0°-360° circular distribution), saturation (color purity, 0%-100%) and brightness (brightness, 0%-100%) three independent parameters, which can improve the subsequent texture consistency processing effect.

[0061] The color information includes one or more of the brightness information, the saturation information and the hue information.

[0062] In an embodiment, by converting the texture information of each oral three-dimensional point and target three-dimensional point from the RGB space to the HSV space to obtain the color information, subsequent processing is performed on the brightness information, the saturation information and the hue information, so that the decoupling processing of brightness, chroma and saturation can be realized, the adjustment of hue, saturation and brightness can be realized, the texture consistency effect or the adjustment precision can be enhanced, the color information of the face and the skin and the oral cavity (prosthesis, teeth or gums) is more consistent, and more natural target data is obtained.

[0063] Step S232: performing brightness compression processing on the plurality of oral three-dimensional points and the plurality of target three-dimensional points.

[0064] This step performs brightness compression processing on each oral three-dimensional point and target three-dimensional point to make the brightness of the face model and the oral cavity model more uniform, reduce the over-bright and over-dark areas, improve the brightness display effect of the face model and the oral cavity model, and at the same time, reduce the influence of excessive and dark areas on the brightness adjustment of the oral three-dimensional points, and improve the brightness adjustment precision of the subsequent oral three-dimensional points. In some embodiments, this step is optional, and step S232 can not be performed.

[0065] Exemplarily, the brightness compression of each oral three-dimensional point and target three-dimensional point can be performed by the following Reinhard tone mapping formula (1):

[0066] (1)

[0067] When the brightness compression is performed on the oral three-dimensional points, V is the initial brightness of each oral three-dimensional point, which is contained in the oral three-dimensional points, is the brightness of each oral three-dimensional point after brightness compression. When the brightness compression is performed on the target three-dimensional points, V is the initial brightness of each target three-dimensional point, which is contained in the target three-dimensional points, a brightness of each target three-dimensional point after brightness compression.

[0068] Step S233: generating an adjustment parameter according to the average color information of the plurality of oral three-dimensional points and the plurality of target three-dimensional points.

[0069] In some embodiments, the generating of the adjustment parameter can be through an average processing. Specifically, when the color information comprises brightness information, the adjustment parameter comprises a brightness adjustment parameter, which can be generated according to the average brightness information of the plurality of oral three-dimensional points and the average brightness information of the plurality of target three-dimensional points; when the color information comprises saturation information, the adjustment parameter comprises a saturation adjustment parameter, which can be generated according to the average saturation information of the plurality of oral three-dimensional points and the average saturation information of the plurality of target three-dimensional points; when the color information comprises hue information, the adjustment parameter comprises a hue adjustment parameter, which can be generated according to the average hue information of the plurality of oral three-dimensional points and the average hue information of the plurality of target three-dimensional points. Exemplarily, the average color information of the plurality of oral three-dimensional points and the average color information of the plurality of target three-dimensional points can be subjected to a ratio operation to generate the adjustment parameter corresponding to the color information.

[0070] Step S241: adjusting the color information of the plurality of oral three-dimensional points according to the adjustment parameter to obtain an adjusted oral model.

[0071] Specifically, when the adjustment parameter comprises the brightness adjustment parameter, the brightness information of each oral three-dimensional point can be adjusted according to the brightness adjustment parameter; when the adjustment parameter comprises the saturation adjustment parameter, the saturation information of each oral three-dimensional point can be adjusted according to the saturation adjustment parameter; when the adjustment parameter comprises the hue adjustment parameter, the hue information of each oral three-dimensional point can be adjusted according to the hue adjustment parameter.

[0072] Step S242: converting the adjusted oral model and the facial model from the HSV space back to the RGB space.

[0073] In this step, the texture information of each oral three-dimensional point in the adjusted oral model and each target three-dimensional point in the facial model can be converted from the HSV space to the RGB space.

[0074] Step S250: obtaining target data according to the facial model and the adjusted oral model. For details, refer to the above step S150, which will not be described here.

[0075] It can be seen that in the above embodiment, by converting the texture information of the face model and the oral cavity model to the HSV space, the brightness information, the saturation information and the hue information of the texture information are decoupled, and when adjusting the texture information of the oral cavity three-dimensional points, the brightness information, the saturation information and the hue information of the oral cavity three-dimensional points can be adjusted respectively, in this way, the accuracy of color adjustment is improved, the color of the oral cavity model can be more consistent with the color of the face model, or the color of the oral cavity model can be closer to the color of the face model to realize natural transition, and then the aesthetic appearance of the target data is improved.

[0076] In an embodiment, the oral cavity model includes a second target region, the color information includes brightness information, and the adjustment parameter includes a target parameter for brightness adjustment of the plurality of oral cavity three-dimensional points in the second target region. At this time, the step 233 of generating the adjustment parameter according to the average color information of the plurality of oral cavity three-dimensional points and the plurality of target three-dimensional points further includes the following steps:

[0077] Step S310: Perform ratio operation on the average brightness of the plurality of oral cavity three-dimensional points in the second target region and the average brightness of the plurality of target three-dimensional points to determine the target parameter.

[0078] This step can be calculated by using the following formula (2):

[0079] (2)

[0080] Wherein, V1 is the target parameter, V face is the average brightness of the plurality of target three-dimensional points, and V teeth is the average brightness of the plurality of oral cavity three-dimensional points in the second target region.

[0081] Wherein, when determining the target parameter, according to the position of the second target region, the following cases can be divided:

[0082] (1) The second target region is the entire region of the oral cavity model.

[0083] At this time, the average brightness of the plurality of oral cavity three-dimensional points in the entire oral cavity model is calculated, and the average brightness of the plurality of target three-dimensional points is calculated, and after the calculation, the ratio operation is performed on the two average brightnesses to obtain the target parameter; the target parameter can adjust the brightness information of all oral cavity three-dimensional points in the entire oral cavity model. When calculating the average brightness of the plurality of oral cavity three-dimensional points in the entire oral cavity model, the ratio operation can be performed on the sum of the brightness of all oral cavity three-dimensional points and the number of oral cavity three-dimensional points. When calculating the average brightness of the plurality of target three-dimensional points, the ratio operation can be performed on the sum of the brightness of all target three-dimensional points and the number of target three-dimensional points.

[0084] (2) The second target region is the upper jaw region of the oral cavity model.

[0085] At this time, the average brightness of the plurality of oral cavity three-dimensional points in the maxillary region can be calculated, and the average brightness of the plurality of target three-dimensional points can be calculated. After the calculation is completed, the two average brightnesses are subjected to ratio operation to obtain a target parameter. The target parameter can adjust the brightness information of all oral cavity three-dimensional points in the maxillary region of the oral cavity model. When calculating the average brightness of the plurality of oral cavity three-dimensional points in the maxillary region, the ratio operation can be performed on the sum of the brightness of all oral cavity three-dimensional points in the maxillary region and the number of oral cavity three-dimensional points in the maxillary region.

[0086] Of course, it can be understood that the plurality of target three-dimensional points can be all target three-dimensional points in the first target region. Alternatively, in step S220, the maxillary region in the first target region can be identified, and at this time, the plurality of target three-dimensional points can be all target three-dimensional points in the maxillary region in the first target region.

[0087] (3) The second target region is the mandibular region of the oral cavity model.

[0088] At this time, the average brightness of the plurality of oral cavity three-dimensional points in the mandibular region can be calculated, and the average brightness of the plurality of target three-dimensional points can be calculated. After the calculation is completed, the two average brightnesses are subjected to ratio operation to obtain a target parameter. The target parameter can adjust the brightness information of all oral cavity three-dimensional points in the mandibular region of the oral cavity model. When calculating the average brightness of the plurality of oral cavity three-dimensional points in the mandibular region, the ratio operation can be performed on the sum of the brightness of all oral cavity three-dimensional points in the mandibular region and the number of oral cavity three-dimensional points in the mandibular region.

[0089] Of course, it can be understood that the plurality of target three-dimensional points can be all target three-dimensional points in the first target region. Alternatively, in step S220, the mandibular region in the first target region can be identified, and at this time, the plurality of target three-dimensional points can be all target three-dimensional points in the mandibular region in the first target region.

[0090] It should be noted that the second target region can distinguish between teeth and gums, or can not distinguish between teeth and gums. Teeth and gums can be calculated together or separately. The identification of the second target region can be based on the storage file name of the maxillary model or the mandibular model referenced by the oral cavity model, or can be obtained by identifying the model. The identification model can be a deep learning image recognition model or a semantic recognition model. The identification model can also be a deep learning image segmentation model or a semantic segmentation model. After identifying the second target region of the oral cavity model, the second target region can be labeled.

[0091] In some embodiments, in the above step, the effective oral cavity three-dimensional points can be selected from the second target region, and when calculating the average brightness of the plurality of oral cavity three-dimensional points, the ratio operation is performed on the sum of the brightness of all the effective oral cavity three-dimensional points and the number of the effective oral cavity three-dimensional points. The electronic device can store a first brightness threshold interval, and the brightness of the effective oral cavity three-dimensional points is within the first brightness threshold interval. In the above step, the effective target three-dimensional points can be selected from the corresponding region, and when calculating the average brightness of the plurality of target three-dimensional points, the ratio operation is performed on the sum of the brightness of all the effective target three-dimensional points and the number of the effective target three-dimensional points. The electronic device can store a second brightness threshold interval, and the brightness of the effective target three-dimensional points is within the second brightness threshold interval. In some embodiments, the first brightness threshold interval and the second brightness threshold interval can be input by a user.

[0092] In the above manner, the effective target three-dimensional points and / or the effective oral cavity three-dimensional points are selected for calculating the average brightness, which eliminates the influence of interference factors such as black pixel three-dimensional points, and improves the calculation accuracy of the average brightness.

[0093] In an embodiment, the step S241 of adjusting the color information of the plurality of oral cavity three-dimensional points according to the adjustment parameter to obtain the adjusted oral cavity model further includes the following steps:

[0094] Step S320: performing product operation on the target parameter and the initial brightness of the oral cavity three-dimensional point to determine the adjusted brightness of the oral cavity three-dimensional point.

[0095] After the target parameter is calculated in the above step S310, the initial brightness of the oral cavity three-dimensional point and the target parameter corresponding to the oral cavity three-dimensional point are subjected to product operation in this step, and the adjusted brightness of the oral cavity three-dimensional point is obtained after the operation. The brightness of each oral cavity three-dimensional point is adjusted in the above manner until the brightness adjustment of all oral cavity three-dimensional points is completed.

[0096] In another embodiment, the step S241 of adjusting the color information of the plurality of oral cavity three-dimensional points according to the adjustment parameter to obtain the adjusted oral cavity model further includes the following steps:

[0097] Step S330: adjusting the brightness information of the oral cavity three-dimensional point according to the target parameter and the weight information.

[0098] After the target parameter is calculated through the step S310, this step can adjust the brightness information of the oral cavity three-dimensional point according to the target parameter and the weight information. Specifically, the initial brightness of the oral cavity three-dimensional point, the target parameter corresponding to the oral cavity three-dimensional point, and the weight information can be multiplied to adjust the brightness of the oral cavity three-dimensional point, and the adjusted brightness of the oral cavity three-dimensional point is obtained. The weight information and the target parameter corresponding to the oral cavity three-dimensional point can be multiplied first, and then the result of the multiplication is multiplied with the initial brightness of the oral cavity three-dimensional point.

[0099] The weight information can have the following cases:

[0100] (1) The weight information can be manually set according to the preference of the target object, so that the brightness value of the oral cavity three-dimensional point can meet the habit of the target object, and the beautifying effect is achieved. For example, when the target object likes to be brighter, the weight information can be set to be larger, and when the target object likes to be darker, the weight information can be set to be smaller. For example, the weight information can be 0.7-0.9. Optionally, the weight information can be 0.7, 0.75, 0.8, 0.85, or 0.9.

[0101] (2) The weight information can be set according to the age of the target object; through this way, the brightness of the oral cavity three-dimensional point is matched with the age of the target object, and then the brightness of the oral cavity three-dimensional point is more matched with the brightness of the facial model, the coordination and beauty of the target data are improved, and the target data realizes natural transition. For example, when the target object is older, the weight information can be set to be smaller, and when the target object is younger, the weight information can be set to be larger.

[0102] (3) The weight information can be set according to the region where the target object is located; through this way, the brightness of the oral cavity three-dimensional point is matched with the region where the target object is located, and then the brightness of the oral cavity three-dimensional point is more matched with the brightness of the facial model, the coordination and beauty of the target data are improved, and the target data realizes natural transition.

[0103] (4) The weight information can be set according to the background brightness of the facial model; through this way, the brightness of the oral cavity three-dimensional point is matched with the background brightness of the facial model, and then the brightness of the oral cavity three-dimensional point is more matched with the brightness of the facial model, the coordination and beauty of the target data are improved, and the target data realizes natural transition. For example, when the background brightness of the facial model is higher, the weight information can be set to be larger, and when the background brightness of the facial model is lower, the weight information can be set to be smaller.

[0104] Of course, it can be understood that the weight information can remain unchanged throughout the entire calculation process. Alternatively, the first weight information is used when adjusting the brightness of the oral cavity three-dimensional points in the maxillary region, and the second weight information is used when adjusting the brightness of the oral cavity three-dimensional points in the mandibular region; the first weight information is greater than the second weight information. In real life, the brightness of the maxillary region is higher than that of the mandibular region. Therefore, in this embodiment, the brightness of the maxillary region is adjusted by using a larger weight information, so that the brightness distribution of the adjusted oral cavity model can be closer to the real situation, and the aesthetic appearance is improved.

[0105] In an embodiment, after the target parameter is calculated by using the method (2) in the above step S310, the brightness of the oral cavity three-dimensional points in the maxillary region can be adjusted by using the method in the above step S320 or step S330. After the adjustment is completed, the following steps can also be performed:

[0106] Step S340: The average brightness of the plurality of oral cavity three-dimensional points in the maxillary region after brightness adjustment and the average brightness of the plurality of oral cavity three-dimensional points in the mandibular region are subjected to ratio operation to determine the mandibular parameter.

[0107] The adjustment parameter further includes the above-mentioned mandibular parameter, and the mandibular parameter is used to adjust the brightness of the plurality of oral cavity three-dimensional points in the mandibular region.

[0108] After the brightness of the oral cavity three-dimensional points in the maxillary region is adjusted, the average brightness of the plurality of oral cavity three-dimensional points in the maxillary region can be obtained by ratio operation of the sum of the brightness values of the plurality of oral cavity three-dimensional points in the maxillary region and the number of the oral cavity three-dimensional points in the maxillary region. Further, this step can be calculated by using the following formula (3):

[0109] (3)

[0110] V2 is the mandibular parameter, V 上 is the average brightness of the plurality of oral cavity three-dimensional points in the maxillary region after brightness adjustment, and V 下 is the average brightness of the plurality of oral cavity three-dimensional points in the mandibular region.

[0111] After the mandibular parameter is obtained, the brightness of the oral cavity three-dimensional points in the mandibular region is adjusted by using the mandibular parameter, and the specific adjustment method is basically the same as the above-mentioned step S320 or step S330, except that the target parameter in the above-mentioned step is replaced by the mandibular parameter, which will not be described here.

[0112] In the above-mentioned method, the brightness of the mandibular region is adjusted by using the adjusted maxillary region, so that the brightness coordination and aesthetic appearance of the maxillary region and the mandibular region of the adjusted oral cavity model are stronger, and the overall over-adjustment can be avoided.

[0113] From the above, it can be seen that, in the present application, the brightness of the oral model is adjusted to match the brightness of the facial model, so that the brightness of the target data can be naturally transitioned and kept consistent, and the brightness mutation is not easy to occur.

[0114] In an embodiment, the color information includes saturation information and hue information, and the adjustment parameter includes a saturation adjustment parameter. At this time, the step 233 of generating the adjustment parameter according to the average color information of the plurality of oral three-dimensional points and the plurality of target three-dimensional points further includes the following steps:

[0115] Step S410: performing ratio operation on the average saturation of the plurality of oral three-dimensional points and the average saturation of the plurality of target three-dimensional points to determine the saturation adjustment parameter.

[0116] This step can calculate the average saturation of the plurality of oral three-dimensional points and the average saturation of the plurality of target three-dimensional points. After the calculation, the ratio operation is performed on the two average saturations by using the following formula (4) to obtain the saturation adjustment parameter, which is used to adjust the saturation of the oral three-dimensional points. When calculating the average saturation of the plurality of oral three-dimensional points, the ratio operation can be performed on the saturation cumulative sum of the plurality of oral three-dimensional points and the number of the oral three-dimensional points. When calculating the average saturation of the plurality of target three-dimensional points, the ratio operation can be performed on the saturation cumulative sum of the plurality of target three-dimensional points and the number of the target three-dimensional points.

[0117] (4)

[0118] Wherein, S(1) is the saturation adjustment parameter, is the average saturation of the plurality of target three-dimensional points, is the average saturation of the plurality of oral three-dimensional points.

[0119] In an embodiment, after the step S410, and the step S241 of adjusting the color information of the plurality of oral three-dimensional points according to the adjustment parameter to obtain the adjusted oral model, the method further includes the following steps:

[0120] Step S421: determining the adjustment direction according to the ratio of the average saturation of the plurality of oral three-dimensional points and the average saturation of the plurality of target three-dimensional points.

[0121] In this step, the adjustment direction is determined according to the ratio of the average saturation of the plurality of oral three-dimensional points and the average saturation of the plurality of target three-dimensional points. For example, if the ratio of the average saturation is greater than 1, it can be seen from the above formula (4) that the average saturation of the oral three-dimensional points is less than the average saturation of the target three-dimensional points. At this time, in order to eliminate the saturation difference, the adjustment direction should be to increase the saturation of the oral three-dimensional points. If the ratio of the average saturation is less than 1, it can be seen from the above formula (4) that the average saturation of the oral three-dimensional points is greater than the average saturation of the target three-dimensional points. At this time, in order to eliminate the saturation difference, the adjustment direction should be to decrease the saturation of the oral three-dimensional points.

[0122] Step S422: Determine the adjusted saturation of the oral three-dimensional points according to the adjustment direction and the saturation adjustment parameter.

[0123] In this step, the saturation of each oral three-dimensional point can be adjusted according to the adjustment direction and the saturation adjustment parameter calculated in the above step S410. After the adjustment is completed, the adjusted saturation of the oral three-dimensional points is obtained. When the saturation adjustment of all oral three-dimensional points is completed, the saturation adjustment process of the oral model is completed.

[0124] Specifically, the saturation of the oral three-dimensional points can be adjusted by the following formula (5).

[0125] (5)

[0126] Wherein, S is the adjusted saturation of the oral three-dimensional points, S o is the initial saturation of the oral three-dimensional points, which is contained in the oral three-dimensional points, A is the ratio of the average saturation of the plurality of oral three-dimensional points and the average saturation of the plurality of target three-dimensional points, sign(A-1) is the adjustment direction, and S(1) is the saturation adjustment parameter.

[0127] As can be seen from the above formula, when A is greater than 1, sign(A-1) is greater than 0, the average saturation of the oral three-dimensional points is less than the average saturation of the target three-dimensional points, and the adjustment direction should be to increase the saturation of the oral three-dimensional points. The specific increase value is sign(A-1)*S o *(1), which can effectively eliminate the saturation difference between the oral model and the facial model. When A is less than 1, sign(A-1) is less than 0, the average saturation of the oral three-dimensional points is greater than the average saturation of the target three-dimensional points, and the adjustment direction should be to decrease the saturation of the oral three-dimensional points. The specific increase value is sign(A-1)*S o *(1), because sign(A-1) is less than 0, the above increase value is a negative number at this time, which can realize the decrease of the saturation of the oral three-dimensional points and effectively eliminate the saturation difference between the oral model and the facial model.

[0128] Specifically, the following formula (6) can be used to adjust the saturation of the oral cavity three-dimensional points.

[0129] (6)

[0130] wherein S is the adjusted saturation of the oral cavity three-dimensional points, S o is the initial saturation of the oral cavity three-dimensional points, A is the ratio of the average saturation of the plurality of oral cavity three-dimensional points to the average saturation of the plurality of target three-dimensional points, sign(A-1) is the adjustment direction, and S(1) is the saturation adjustment parameter.

[0131] In the above manner, the clamp function is used to constrain the saturation value of the adjusted oral cavity three-dimensional points within the range of [0, 1], preventing color overflow / saturation overflow.

[0132] In an embodiment, the color information includes hue information and saturation information, and the adjustment parameter includes a saturation adjustment parameter. In this case, the step 233 of generating the adjustment parameter according to the average color information of the plurality of oral cavity three-dimensional points and the plurality of target three-dimensional points further includes the following steps:

[0133] Step S431: Perform a ratio operation on the average saturation of the plurality of oral cavity three-dimensional points and the average saturation of the plurality of target three-dimensional points to determine the scaling factor.

[0134] In this step, when performing the ratio operation, the average saturation of the target three-dimensional points is the numerator.

[0135] Step S432: Identify the point set to which each oral cavity three-dimensional point belongs according to the initial hue of each oral cavity three-dimensional point.

[0136] The plurality of oral cavity three-dimensional points form a first point set whose hue is in a first threshold interval, a second point set whose hue is in a second threshold interval, and a third point set whose hue is in a third threshold interval; the second threshold interval is greater than the first threshold interval and less than the third threshold interval. Here, the second threshold interval being greater than the first threshold interval and less than the third threshold interval means that the minimum threshold of the second threshold interval is greater than the maximum threshold of the first threshold interval, and the maximum threshold of the second threshold interval is less than the minimum threshold of the third threshold interval. In this step, the saturation adjustment parameter of the oral cavity three-dimensional point can be determined according to the initial hue of each oral cavity three-dimensional point. Since each threshold interval is actually a different color interval, the hue has the property of color, so the oral cavity three-dimensional point can be accurately determined to be in which point set, and based on this preliminary identification of the semantic information of the oral cavity three-dimensional point, such as being in the tooth, gum, tooth area close to the gum, tooth biting surface, tooth cheek side, tooth tongue side, etc., the saturation adjustment parameter of the oral cavity three-dimensional point is determined.

[0137] Step S433: According to the point set in which each oral cavity three-dimensional point is located and the scale factor, the saturation adjustment parameter of the corresponding oral cavity three-dimensional point is determined.

[0138] Specifically, if the oral cavity three-dimensional point is located in the first point set, the saturation adjustment parameter of the oral cavity three-dimensional point is calculated according to the initial hue of the oral cavity three-dimensional point in the first point set, the range of the first threshold interval, and the scale factor. Exemplarily, the saturation adjustment parameter of the oral cavity three-dimensional point can be calculated by the following formula (7).

[0139] (7)

[0140] Wherein, S(1) is the saturation adjustment parameter, X2 is the maximum threshold value of the first threshold interval, X1 is the minimum threshold value of the first threshold interval, H0 is the initial hue of the oral cavity three-dimensional point, is the scale factor.

[0141] If the oral cavity three-dimensional point is located in the second point set, the scale factor is taken as the saturation adjustment parameter of the oral cavity three-dimensional point.

[0142] If the oral cavity three-dimensional point is located in the third point set, the saturation adjustment parameter of the oral cavity three-dimensional point is calculated according to the initial hue of the oral cavity three-dimensional point in the third point set, the range of the third threshold interval, and the scale factor. Exemplarily, the saturation adjustment parameter of the oral cavity three-dimensional point can be calculated by the following formula (8).

[0143] (8)

[0144] Wherein, S(1) is the saturation adjustment parameter, X4 is the maximum threshold value of the third threshold interval, X3 is the minimum threshold value of the third threshold interval, H0 is the initial hue of the oral cavity three-dimensional point, is the scale factor. The first point set is the transition point set, the second point set is the core point set, and the third point set is the decay point set.

[0145] After the saturation adjustment parameter of each oral cavity three-dimensional point is calculated in the above manner, the saturation of the oral cavity three-dimensional point can be adjusted. The specific adjustment manner is detailed in the above steps S421 and S422, which will not be repeated here.

[0146] It can be seen that, in the embodiment, the saturation adjustment parameter of the oral cavity three-dimensional point is calculated according to the point set to which each oral cavity three-dimensional point belongs, and the principle of the segmented linear function is used to calculate the adjustment parameter of the oral cavity three-dimensional point. The oral cavity three-dimensional points in the second point set are mostly distributed in the middle region of the oral cavity model, at this time, the scale factor is directly taken as the saturation adjustment parameter of the oral cavity three-dimensional points in the second point set, the saturation of the oral cavity three-dimensional points in the second point set is greatly adjusted, the saturation of the middle region of the oral cavity model can be matched with the saturation of the face model, and then the saturation consistency of the target data is improved. The oral cavity three-dimensional points in the first point set and the third point set are mostly distributed in the edge region of the oral cavity model; at this time, the saturation adjustment parameter of the oral cavity three-dimensional points in the first point set presents linear variation, and the saturation adjustment parameter of the oral cavity three-dimensional points in the third point set presents linear variation, the saturation of the oral cavity three-dimensional points in the first point set and the third point set is linearly and gradually adjusted, the saturation of the edge region of the oral cavity model is smoothed, the saturation mutation is prevented, at the same time, the saturation of the edge region of the oral cavity model can be smoothly transitioned with the saturation of the middle region of the oral cavity model, the color block feeling is avoided, the overall saturation of the oral cavity model is naturally transitioned, and the saturation of the oral cavity model is closer to the real saturation of the oral cavity, and the naturalness, authenticity and aesthetic property are better.

[0147] The oral cavity model can include a tooth region and a gum region, the hue of the tooth region is a yellow hue, and the hue of the gum region is a red hue. The saturation of the yellow hue region and the red hue region can be adjusted respectively during the saturation adjustment, which will be described below.

[0148] (1) Saturation adjustment of the yellow hue region:

[0149] Method one:

[0150] Step S510: The average saturation of the oral cavity three-dimensional points in the yellow hue region of the oral cavity model and the average saturation of the target three-dimensional points in the yellow hue region of the face model are subjected to ratio operation to obtain a saturation adjustment parameter.

[0151] In this step, the oral cavity model can have a first target threshold interval. Whether the oral cavity three-dimensional point is located in the yellow hue region can be determined by judging whether the saturation of the oral cavity three-dimensional point is located in the first target threshold interval. If the saturation of the oral cavity three-dimensional point is located in the first target threshold interval, the oral cavity three-dimensional point is located in the yellow hue region. All oral cavity three-dimensional points located in the yellow hue region are identified by the above method, and the average saturation of each oral cavity three-dimensional point is calculated. The average saturation of the oral cavity three-dimensional point is the ratio of the sum of the saturations of the oral cavity three-dimensional points located in the yellow hue region to the number of the oral cavity three-dimensional points located in the yellow hue region.

[0152] Similarly, the face model can have a second target threshold interval, and whether the target three-dimensional point is located in the yellow hue region can be determined by judging whether the target three-dimensional point is located in the second target threshold interval. If the saturation of the target three-dimensional point is located in the second target threshold interval, the target three-dimensional point is located in the yellow hue region. In this way, all target three-dimensional points located in the yellow hue region are identified, and the average saturation of each target three-dimensional point is calculated. The average saturation of the target three-dimensional point is the ratio of the sum of the saturations of the target three-dimensional points located in the yellow hue region to the number of the target three-dimensional points located in the yellow hue region.

[0153] After the two average saturations are calculated, the two average saturations are subjected to a ratio operation to obtain a saturation adjustment parameter corresponding to the yellow hue region in the oral cavity model. In the ratio operation, the average saturation of the target three-dimensional point is located in the numerator.

[0154] It can be understood that the first target threshold interval and the second target threshold interval can be designed as needed, but the designed target threshold interval should ensure that the oral cavity three-dimensional points and the target three-dimensional points located in the yellow hue region are all screened out. For example, the first target threshold interval can be [0.04, 0.30], and when the first target threshold interval is set to the above threshold, the hue circle angle is about 15°-106°, and the hue is in the yellow-orange-brown interval, which effectively covers the natural color range of the oral cavity, avoids misjudgment of the gum or shadow area, and accurately screens out the oral cavity three-dimensional points located in the yellow hue region. For example, the second target threshold interval can be (0, 0.17], and when the second target threshold interval is set to the above threshold, it can effectively filter the over-bright, over-dark, or over-saturated regions, and accurately screen out the target three-dimensional points located in the yellow hue region.

[0155] It can be understood that the examples of the first target threshold interval and the second target threshold interval only include the saturation threshold interval, and in actual use, one or more of the saturation threshold interval, the brightness threshold interval, and the hue threshold interval can also be included. At this time, when screening the three-dimensional points located in the yellow hue region, it is necessary to judge whether the saturation of the corresponding three-dimensional point is located in the saturation threshold interval, whether the brightness of the corresponding three-dimensional point is located in the brightness threshold interval, and whether the hue of the corresponding three-dimensional point is located in the hue threshold interval. For example, at this time, the saturation threshold interval in the second target threshold interval can be (0, 0.17], and the brightness threshold interval in the second target threshold interval can be [0.43, 1).

[0156] Step S520: Determine the adjustment direction according to the ratio of the average saturation of the oral cavity three-dimensional points in the yellow hue region of the oral cavity model to the average saturation of the target three-dimensional points in the yellow hue region of the face model.

[0157] Step S530: According to the adjustment direction and the saturation adjustment parameter, the adjusted saturation of the yellow hue region in the oral cavity model is determined.

[0158] For details, see the above step S422, which will not be repeated here. The difference between the above step S422 is that each parameter in formula (5) is replaced by the corresponding parameter of the yellow hue region.

[0159] Method two:

[0160] Step S610: The average saturation of the yellow hue region in the oral cavity model and the average saturation of the target three-dimensional point in the yellow hue region of the face model are subjected to ratio operation to determine the proportion factor. The specific principle of the ratio operation in this step is described in detail in the above step S510.

[0161] Step S620: According to the initial hue of each oral cavity three-dimensional point in the yellow hue region of the oral cavity model, identify the point set to which each oral cavity three-dimensional point in the yellow hue region of the oral cavity model belongs.

[0162] In this step, the oral cavity model has a first yellow threshold interval, a second yellow threshold interval and a third yellow threshold interval. When the initial hue of the oral cavity three-dimensional point located in the yellow hue region is located in the above first yellow threshold interval, it indicates that it is located in the first point set. When the initial hue of the oral cavity three-dimensional point located in the yellow hue region is located in the above second yellow threshold interval, it indicates that it is located in the second point set. When the initial hue of the oral cavity three-dimensional point located in the yellow hue region is located in the above third yellow threshold interval, it indicates that it is located in the third point set. It can be understood that the above yellow threshold interval can be designed as needed. For example, the first yellow threshold interval can be [0.04, 0.13], the second yellow threshold interval can be (0.13, 0.21], and the third yellow threshold interval can be (0.21, 0.30].

[0163] Step S630: According to the point set where each oral cavity three-dimensional point in the yellow hue region of the oral cavity model is located and the proportion factor, the saturation adjustment parameter of the corresponding oral cavity three-dimensional point is determined.

[0164] In this step, the saturation adjustment parameter of the oral cavity three-dimensional point can be determined according to the following piecewise function:

[0165]

[0166] Wherein, is the proportion factor, is the saturation adjustment parameter corresponding to the yellow hue region, H0 is the initial hue of the oral cavity three-dimensional point, the above yellow-orange transition region is the first point set, the core yellow region is the second point set, and the orange-brown decay region is the third point set; is a first yellow threshold interval, is a second yellow threshold interval, is a third yellow threshold interval.

[0167] As can be seen from the above piecewise function, in this step, if the oral cavity three-dimensional point is located in the first point set, the saturation adjustment parameter of the oral cavity three-dimensional point is calculated according to the initial hue of the oral cavity three-dimensional point in the first point set, the range of the first threshold interval and the proportion factor. If the oral cavity three-dimensional point is located in the second point set, the proportion factor is taken as the saturation adjustment parameter of the oral cavity three-dimensional point. If the oral cavity three-dimensional point is located in the third point set, the saturation adjustment parameter of the oral cavity three-dimensional point is calculated according to the initial hue of the oral cavity three-dimensional point in the third point set, the range of the third threshold interval and the proportion factor.

[0168] Step S640: According to the adjustment direction and the saturation adjustment parameter, the adjusted saturation of each oral cavity three-dimensional point in the yellow hue region of the oral cavity model is determined. For details, see the above step S422, which will not be repeated here.

[0169] (1) Saturation adjustment for the red hue region:

[0170] Method one:

[0171] Step S710: The average saturation of the oral cavity three-dimensional points in the red hue region of the oral cavity model and the average saturation of the target three-dimensional points in the red hue region of the face model are subjected to ratio operation to obtain a saturation adjustment parameter.

[0172] In this step, the oral cavity model can have a third target threshold interval, which can be used to determine whether the oral cavity three-dimensional point is located in the red hue region by judging whether the saturation of the oral cavity three-dimensional point is located in the above-mentioned third target threshold interval. If the saturation of the oral cavity three-dimensional point is located in the above-mentioned third target threshold interval, the oral cavity three-dimensional point is located in the red hue region. All oral cavity three-dimensional points located in the red hue region are identified by the above-mentioned method, and the average saturation of each oral cavity three-dimensional point is calculated. The average saturation of the oral cavity three-dimensional point is the ratio of the sum of the saturations of the oral cavity three-dimensional points located in the red hue region to the number of the oral cavity three-dimensional points located in the red hue region.

[0173] Similarly, the face model can have a fourth target threshold interval. Whether the target three-dimensional point is located in the red hue region can be determined by judging whether the target three-dimensional point is located in the fourth target threshold interval. If the saturation of the target three-dimensional point is located in the fourth target threshold interval, the target three-dimensional point is located in the red hue region. In this way, all target three-dimensional points located in the red hue region are identified, and the average saturation of each target three-dimensional point is calculated. The average saturation of the target three-dimensional point is the ratio of the sum of the saturations of the target three-dimensional points located in the red hue region to the number of the target three-dimensional points located in the red hue region.

[0174] After the two average saturations are calculated, the two average saturations are subjected to a ratio operation to obtain a saturation adjustment parameter corresponding to the red hue region in the oral cavity model. The average saturation of the target three-dimensional point is located in the numerator during the ratio operation.

[0175] It can be understood that the third target threshold interval and the fourth target threshold interval can be designed as needed, but the designed target threshold interval should ensure that the oral cavity three-dimensional points and the target three-dimensional points located in the red hue region are all screened out. For example, the third target threshold interval can be [0.9, 1.0)∪[0, 0.05]. When the third target threshold interval is set to the above threshold, the hue circle angle is about 324°~360° and 0°~18°, and the hue is red-orange and red-purple interval, effectively covering the gum area, which can accurately screen out the oral cavity three-dimensional points located in the red hue region. For example, the fourth target threshold interval can be [0.17, 1.0]. When the fourth target threshold interval is set to the above threshold, it can effectively filter the over-bright, over-dark or over-saturated regions, and accurately screen out the target three-dimensional points located in the red hue region.

[0176] It can be understood that the above-mentioned third target threshold interval and fourth target threshold interval only include the saturation threshold interval, and in actual use, one or more of the saturation threshold interval, the brightness threshold interval and the hue threshold interval can also be included, at this time, when the three-dimensional points located in the red hue region are screened, it is necessary to judge whether the saturation of the corresponding three-dimensional point is located in the saturation threshold interval, whether the brightness of the corresponding three-dimensional point is located in the brightness threshold interval, and whether the hue of the corresponding three-dimensional point is located in the hue threshold interval. For example, at this time, the saturation threshold interval in the fourth threshold interval can be [0.17, 1.0], the brightness threshold interval in the fourth threshold interval can be [0.18, 1.0], and the hue threshold interval of the fourth threshold interval can be [0, 0.06)∪[0.86, 1.0]. That is, at this time, the fourth threshold interval is {(H ∈ [0, 0.06)∧ S ∈ [0.17, 1.0]∧ V ∈ [0.18, 1.0]),(H ∈ [0.86, 1.0]∧ S ∈ [0.17, 1.0]∧ V ∈ [0.18, 1.0])} ; Wherein, H is hue, S is saturation, and V is brightness.

[0177] Step S720: determining the adjustment direction according to the ratio of the average saturation of the three-dimensional points of the red hue region in the oral model and the average saturation of the target three-dimensional points of the red hue region in the face model.

[0178] Step S730: determining the adjusted saturation of the three-dimensional points of the red hue region in the oral model according to the adjustment direction and the saturation adjustment parameter.

[0179] For details, see the above step S422, which will not be repeated here. The difference between the above step S422 is that each parameter in formula (5) is replaced by the corresponding related parameter of the red hue region.

[0180] Method two:

[0181] Step S810: performing ratio operation on the average saturation of the three-dimensional points of the red hue region in the oral model and the average saturation of the target three-dimensional points of the red hue region in the face model to determine the proportion factor. The specific principle of the ratio operation in this step is described in detail in the above step S710.

[0182] Step S820: identifying the point set to which each three-dimensional point of the red hue region in the oral model belongs according to the initial hue of each three-dimensional point of the red hue region in the oral model.

[0183] In this step, the oral cavity model has a first red threshold interval, a second red threshold interval and a third red threshold interval, and the initial hue of the oral cavity three-dimensional point located in the red hue region is located in the first red threshold interval, which indicates that it is located in the first point set, the initial hue of the oral cavity three-dimensional point located in the red hue region is located in the second red threshold interval, which indicates that it is located in the second point set, and the initial hue of the oral cavity three-dimensional point located in the red hue region is located in the third red threshold interval, which indicates that it is located in the third point set. It can be understood that the above-mentioned red threshold interval can be designed by yourself as needed. For example, the first red threshold interval can be [0.9, 0.95], the second red threshold interval can be (0.95, 1.0)∪[0, 0.02], and the third red threshold interval can be (0.02, 0.05].

[0184] Step S830: According to the point set and the scaling factor of each oral cavity three-dimensional point in the red hue region of the oral cavity model, the saturation adjustment parameter of the corresponding oral cavity three-dimensional point is determined.

[0185] In this step, the saturation adjustment parameter of the oral cavity three-dimensional point can be determined according to the following piecewise function:

[0186]

[0187] wherein, is a scaling factor, is a saturation adjustment parameter corresponding to the red hue region, H0 is the initial hue of the oral cavity three-dimensional point, the orange-red transition region is the first point set, the core red region is the second point set, and the red-purple attenuation region is the third point set; is a first red threshold interval, is a second red threshold interval, is a third red threshold interval.

[0188] As can be seen from the above piecewise function, in this step, if the oral cavity three-dimensional point is located in the first point set, the saturation adjustment parameter of the oral cavity three-dimensional point is calculated according to the initial hue of the oral cavity three-dimensional point in the first point set, the difference of the first threshold interval and the scaling factor. If the oral cavity three-dimensional point is located in the second point set, the scaling factor is taken as the saturation adjustment parameter of the oral cavity three-dimensional point. If the oral cavity three-dimensional point is located in the third point set, the saturation adjustment parameter of the oral cavity three-dimensional point is calculated according to the initial hue of the oral cavity three-dimensional point in the third point set, the difference of the third threshold interval and the scaling factor.

[0189] Step S840: According to the adjustment direction and the saturation adjustment parameter, the adjusted saturation of each oral cavity three-dimensional point in the red hue region of the oral cavity model is determined. For details, see the above step S422, which will not be repeated here.

[0190] It can be seen from the above that, by adjusting the saturation of the oral model to match the saturation of the facial model, the saturation of the target data can be naturally transitioned and kept consistent in brightness, and saturation mutation is less likely to occur.

[0191] In an embodiment, the saturation of the upper jaw region and the lower jaw region of the oral model can also be adjusted respectively, in which case only the corresponding parameters in the above steps are replaced by the saturation parameters of the upper jaw region and the lower jaw region, and details are not repeated here.

[0192] It can be understood that, in the above embodiments, the three hue regions in the yellow hue region and the three hue regions in the red hue region are adjusted, and in practice, the yellow hue region and the red hue region can include other regions in addition to the above regions, but other regions have less effect on saturation consistency, so the saturation of other regions is not adjusted. It can be understood, of course, that other regions can also be adjusted, and the adjustment method is as described in the above embodiments, and details are not repeated here.

[0193] In an embodiment, the color information includes hue information, and the adjustment parameter includes a hue adjustment parameter, in which case the step 233 of generating the adjustment parameter according to the average color information of the plurality of oral three-dimensional points and the plurality of target three-dimensional points further includes the following steps:

[0194] Step S910: performing difference operation on the average hue of the plurality of oral three-dimensional points and the plurality of target three-dimensional points to determine the hue adjustment parameter.

[0195] In this step, the average hue of the plurality of target three-dimensional points can be subtracted from the average hue of the plurality of oral three-dimensional points to obtain the hue adjustment parameter.

[0196] Step S920: performing sum operation on the hue adjustment parameter and the initial hue of the oral three-dimensional points to determine the adjusted hue of the oral three-dimensional points.

[0197] In this step, after the hue of each oral three-dimensional point is adjusted in the above manner, the hue adjustment process of the oral model is completed.

[0198] In an embodiment, when adjusting the hue, the red hue region can be adjusted; at this time, the average hue of the plurality of target three-dimensional points in the red hue region in the face model can be subtracted from the average hue of the plurality of oral three-dimensional points in the red hue region in the oral model to obtain a hue adjustment parameter. Further, the hue adjustment parameter and the initial hue of the oral three-dimensional points in the red hue region in the oral model can be summed to obtain the adjusted hue of the oral three-dimensional points. When the hue adjustment parameter is greater than 0, it indicates that the red hue of the oral three-dimensional points is too strong, and when the oral three-dimensional points and the hue adjustment parameter are summed, the intensity of the red hue can be reduced. When the hue adjustment parameter is less than 0, it indicates that the red hue of the oral three-dimensional points is weak, and when the oral three-dimensional points and the hue adjustment parameter are summed, the intensity of the red hue can be increased.

[0199] In an embodiment, the hue of the upper jaw region and the lower jaw region of the oral model can also be adjusted respectively, at this time, only the corresponding parameters in the above steps are replaced by the hue parameters of the upper jaw region and the lower jaw region, which will not be described here.

[0200] As can be seen from the above, in the present application, the hue of the oral model is adjusted to match the hue of the face model, so that the hue of the target data can be naturally transitioned and the brightness is consistent, and the hue mutation is not easy to occur. In addition, in the present application, the brightness, saturation and hue are decoupled, and the brightness, hue and saturation are adjusted separately, which can accurately adjust the color of the oral model, and make the color of the oral model more matched with the color of the face model.

[0201] The present application provides a more scientific and automatic texture consistency method, which simplifies the adjustment process of texture consistency, has higher overall efficiency, and makes the target data more close to the real aesthetic requirements after adjustment, has stronger natural beauty, and improves the treatment experience of users.

[0202] The present application also provides a computer readable storage medium, which stores a computer program, and the computer program can be executed by a processor to perform the above model processing method.

[0203] As Figure 8As shown, the present application further provides a model processing apparatus 1000, comprising: an acquisition module 1100, configured to acquire a face model and an oral cavity model, the oral cavity model comprising a plurality of oral cavity three-dimensional points; an identification module 1200, configured to identify a first target region where the oral cavity is located in the face model, the first target region comprising a plurality of target three-dimensional points; a generation module 1300, configured to generate an adjustment parameter according to texture information of the plurality of oral cavity three-dimensional points and the plurality of target three-dimensional points; an adjustment module 1400, configured to adjust the texture information of the plurality of oral cavity three-dimensional points according to the adjustment parameter to obtain an adjusted oral cavity model; and a data module 1500, configured to obtain target data according to the face model and the adjusted oral cavity model.

[0204] In an embodiment, the generation module 1300 is further configured to: convert the face model and the oral cavity model from an RGB space to an HSV space to obtain color information of the plurality of oral cavity three-dimensional points and the plurality of target three-dimensional points; wherein the color information comprises one or more of brightness information, saturation information and hue information; perform brightness compression processing on the plurality of oral cavity three-dimensional points and the plurality of target three-dimensional points; and generate the adjustment parameter according to average color information of the plurality of oral cavity three-dimensional points and the plurality of target three-dimensional points.

[0205] In an embodiment, the adjustment module 1400 is further configured to: adjust the color information of the plurality of oral cavity three-dimensional points according to the adjustment parameter to obtain the adjusted oral cavity model; and convert the adjusted oral cavity model and the face model from the HSV space back to the RGB space.

[0206] In an embodiment, the oral cavity model comprises a second target region, the color information comprises brightness information, and the adjustment parameter comprises a target parameter for adjusting the brightness of the plurality of oral cavity three-dimensional points in the second target region; the generation module 1300 is further configured to: perform ratio operation on average brightness of the plurality of oral cavity three-dimensional points in the second target region and average brightness of the plurality of target three-dimensional points to determine the target parameter.

[0207] In an embodiment, the second target region is a maxillary region, the adjustment parameter further comprises a mandibular parameter for adjusting the brightness of the plurality of oral cavity three-dimensional points in a mandibular region, and the model processing apparatus 1000 further comprises a determination module 1600, configured to perform ratio operation on average brightness of the plurality of oral cavity three-dimensional points in the brightness-adjusted maxillary region and average brightness of the plurality of oral cavity three-dimensional points in the mandibular region to determine the mandibular parameter.

[0208] In an embodiment, the adjustment module 1400 is further configured to: perform product operation on the adjustment parameter and initial brightness of the oral cavity three-dimensional points to determine the adjusted brightness of the oral cavity three-dimensional points.

[0209] In an embodiment, the adjustment module 1400 is further configured to: adjust the brightness information of the oral cavity three-dimensional points according to the adjustment parameter and weight information.

[0210] In an embodiment, the adjusting module 1400 is further configured to: multiply the adjustment parameter, the weight information and the initial brightness of the oral cavity three-dimensional point to determine the adjusted brightness of the oral cavity three-dimensional point.

[0211] In an embodiment, the color information comprises saturation information, and the adjustment parameter comprises a saturation adjustment parameter, and the generating module 1300 is further configured to: perform ratio operation on the average saturation of the plurality of oral cavity three-dimensional points and the average saturation of the plurality of target three-dimensional points to determine the saturation adjustment parameter.

[0212] In an embodiment, the color information comprises hue information and saturation information, and the adjustment parameter comprises a saturation adjustment parameter, and the generating module 1300 is further configured to: perform ratio operation on the average saturation of the plurality of oral cavity three-dimensional points and the average saturation of the plurality of target three-dimensional points to determine a scaling factor; identify a point set to which each oral cavity three-dimensional point belongs according to the initial hue of each oral cavity three-dimensional point; wherein the plurality of oral cavity three-dimensional points form a first point set whose hue is in a first threshold interval, a second point set whose hue is in a second threshold interval, and a third point set whose hue is in a third threshold interval; the second threshold interval is greater than the first threshold interval and less than the third threshold interval; and determine the saturation adjustment parameter of each oral cavity three-dimensional point according to the point set in which the oral cavity three-dimensional point is located and the scaling factor.

[0213] In an embodiment, the generating module 1300 is further configured to: take the scaling factor as the saturation adjustment parameter of the oral cavity three-dimensional points in the second point set; determine the saturation adjustment parameter of the oral cavity three-dimensional points in the first point set according to the initial hue of the oral cavity three-dimensional points in the first point set, the range of the first threshold interval and the scaling factor; and determine the saturation adjustment parameter of the oral cavity three-dimensional points in the third point set according to the initial hue of the oral cavity three-dimensional points in the third point set, the range of the third threshold interval and the scaling factor.

[0214] In an embodiment, the generating module 1300 is further configured to determine the saturation adjustment parameter according to the following formula:

[0215]

[0216] wherein S(1) is the saturation adjustment parameter, is the first threshold interval, is the second threshold interval, is the third threshold interval, H0 is the initial hue of the oral cavity three-dimensional point, is the scaling factor.

[0217] In an embodiment, the adjusting module 1400 is further configured to determine an adjustment direction according to a ratio of the average saturation of the plurality of oral cavity three-dimensional points and the average saturation of the plurality of target three-dimensional points; and determine the adjusted saturation of the oral cavity three-dimensional point according to the adjustment direction and the saturation adjustment parameter.

[0218] In an embodiment, the adjusting module 1400 is further configured to adjust the saturation of the oral cavity three-dimensional point according to the following formula:

[0219]

[0220] wherein S is the adjusted saturation of the oral cavity three-dimensional point, S is the initial saturation of the oral cavity three-dimensional point, A is the ratio of the average saturation of the plurality of oral cavity three-dimensional points and the average saturation of the plurality of target three-dimensional points, sign(A-1) is the adjustment direction, and S(1) is the saturation adjustment parameter. o

[0221] In an embodiment, the color information comprises hue information, and the adjustment parameter comprises a hue adjustment parameter; and the generating module 1300 is further configured to determine the hue adjustment parameter by performing a difference operation on the average hue of the plurality of oral cavity three-dimensional points and the average hue of the plurality of target three-dimensional points.

[0222] In an embodiment, the adjusting module 1400 is further configured to determine the adjusted hue of the oral cavity three-dimensional point by performing a sum operation on the hue adjustment parameter and the initial hue of the oral cavity three-dimensional point.

[0223] The implementation process of the functions and roles of each module in the above device is described in detail in the implementation process of the corresponding steps in the above model processing method, which will not be described here.

[0224] In several embodiments provided by the present application, the disclosed device and method can also be implemented by other means. The device embodiments described above are only schematic; for example, the flowcharts and block diagrams in the accompanying drawings show possible implementation architectures, functions, and operations of the device, method, and computer program product according to the embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment, or a portion of code, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks can occur in a different order than that shown in the figure. For example, two consecutive blocks can actually be executed substantially in parallel, or they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions. ​

[0225] In addition, each functional module in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0226] If the functions are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

Claims

1. A model processing method characterized by, The method comprises: obtaining a face model and an oral cavity model, wherein the oral cavity model comprises a plurality of oral cavity three-dimensional points; identifying a first target region in the face model where the oral cavity is located, wherein the first target region comprises a plurality of target three-dimensional points; generating an adjustment parameter according to texture information of the plurality of oral cavity three-dimensional points and the plurality of target three-dimensional points; adjusting the texture information of the plurality of oral cavity three-dimensional points according to the adjustment parameter to obtain an adjusted oral cavity model; obtaining target data according to the face model and the adjusted oral cavity model.

2. The model processing method of claim 1, wherein, The method comprises: converting the face model and the oral cavity model from an RGB space to an HSV space to obtain color information of the oral cavity three-dimensional points and the target three-dimensional points, wherein the color information comprises one or more of brightness information, saturation information and hue information; performing brightness compression processing on the plurality of oral cavity three-dimensional points and the plurality of target three-dimensional points; generating the adjustment parameter according to average color information of the plurality of oral cavity three-dimensional points and the plurality of target three-dimensional points; The method comprises: adjusting the color information of the plurality of oral cavity three-dimensional points according to the adjustment parameter to obtain the adjusted oral cavity model; converting the adjusted oral cavity model and the face model from the HSV space back to the RGB space.

3. The model processing method according to claim 2, characterized by, The oral cavity model comprises a second target region, the color information comprises brightness information, and the adjustment parameter comprises a target parameter for adjusting the brightness of the plurality of oral cavity three-dimensional points in the second target region. The method comprises: performing ratio operation on average brightness of the plurality of oral cavity three-dimensional points in the second target region and average brightness of the plurality of target three-dimensional points to determine the target parameter.

4. The model processing method according to claim 3, characterized by, The second target region is a maxillary region, the adjustment parameter further comprises a mandibular parameter for adjusting the brightness of the plurality of oral cavity three-dimensional points in a mandibular region, and the method further comprises: performing ratio operation on average brightness of the plurality of oral cavity three-dimensional points in the maxillary region after brightness adjustment and average brightness of the plurality of oral cavity three-dimensional points in the mandibular region to determine the mandibular parameter.

5. The model processing method according to claim 3 or 4, characterized by, The method comprises: adjusting the brightness information of the plurality of oral cavity three-dimensional points according to the adjustment parameter and weight information.

6. The model processing method according to claim 5, characterized by, The method comprises: performing product operation on the adjustment parameter, the weight information and initial brightness of the oral cavity three-dimensional points to determine the adjusted brightness of the oral cavity three-dimensional points.

7. The model processing method according to claim 2, characterized by, The color information comprises saturation information, the adjustment parameter comprises a saturation adjustment parameter, and the method comprises: performing ratio operation on average saturation of the plurality of oral cavity three-dimensional points and average saturation of the plurality of target three-dimensional points to determine the saturation adjustment parameter. The average saturation of the plurality of oral three-dimensional points and the average saturation of the plurality of target three-dimensional points are subjected to ratio operation to determine the saturation adjustment parameter.

8. The model processing method according to claim 2, characterized by, The color information includes hue information and saturation information, the adjustment parameter includes a saturation adjustment parameter, and the adjustment parameter is generated according to average color information of the plurality of oral three-dimensional points and the plurality of target three-dimensional points. The average saturation of the plurality of oral three-dimensional points and the average saturation of the plurality of target three-dimensional points are subjected to ratio operation to determine a proportion factor. According to the initial hue of each oral three-dimensional point, a point set to which each oral three-dimensional point belongs is identified; wherein the plurality of oral three-dimensional points form a first point set with hue in a first threshold interval, a second point set with hue in a second threshold interval, and a third point set with hue in a third threshold interval; the second threshold interval is greater than the first threshold interval and less than the third threshold interval. According to the point set in which each oral three-dimensional point is located and the proportion factor, the saturation adjustment parameter of the corresponding oral three-dimensional point is determined.

9. The model processing method according to claim 8, characterized by, The proportion factor is used as the saturation adjustment parameter of the oral three-dimensional point in the second point set. According to the initial hue of the oral three-dimensional point in the first point set, the range of the first threshold interval, and the proportion factor, the saturation adjustment parameter of the oral three-dimensional point in the first point set is determined. According to the initial hue of the oral three-dimensional point in the third point set, the range of the third threshold interval, and the proportion factor, the saturation adjustment parameter of the oral three-dimensional point in the third point set is determined. The saturation adjustment parameter is determined according to the following formula:

10. The model processing method of claim 9, wherein, The color information of the plurality of oral three-dimensional points is adjusted according to the adjustment parameter, including: wherein S(1) is a saturation adjustment parameter, is the first threshold interval, is the second threshold interval, is the third threshold interval, and H0 is an initial hue of the three-dimensional point of the oral cavity. is the scaling factor.

11. The model processing method according to any one of claims 7-10, characterized by, According to the ratio of the average saturation of the plurality of oral three-dimensional points and the average saturation of the plurality of target three-dimensional points, an adjustment direction is determined. According to the adjustment direction and the saturation adjustment parameter, the adjusted saturation of the oral three-dimensional point is determined. The adjusted saturation of the oral three-dimensional point is determined according to the adjustment direction and the saturation adjustment parameter, specifically using the following formula:

12. The model processing method of claim 11, wherein, The color information includes hue information, the adjustment parameter includes a hue adjustment parameter, and the adjustment parameter is generated according to average color information of the plurality of oral three-dimensional points and the plurality of target three-dimensional points. Wherein, S is the adjusted saturation of the oral cavity three-dimensional point, S o is the initial saturation of the oral cavity three-dimensional point, A is the ratio of the average saturation of the plurality of oral cavity three-dimensional points and the average saturation of the plurality of target three-dimensional points, sign(A-1) is the adjustment direction, and S(1) is the saturation adjustment parameter.

13. The model processing method of claim 2, wherein The average hue of the plurality of oral three-dimensional points and the plurality of target three-dimensional points is subjected to difference operation to determine the hue adjustment parameter. The device includes:

14. A model processing apparatus characterized by comprising: An acquisition module is configured to acquire a face model and an oral model, wherein the oral model includes a plurality of oral three-dimensional points; An identification module is configured to identify a first target area in which an oral cavity is located in the face model, wherein the first target area includes a plurality of target three-dimensional points; A generation module is configured to generate an adjustment parameter according to texture information of the plurality of oral three-dimensional points and the plurality of target three-dimensional points. ​ An adjusting module is configured to adjust texture information of the plurality of oral three-dimensional points according to the adjusting parameter to obtain an adjusted oral model. A data module is configured to obtain target data according to the face model and the adjusted oral model.

15. An electronic device, comprising: The electronic device includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the model processing method of any one of claims 1-13.

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