Occlusion adjusting method and device

Abstract

The invention provides an occlusion adjustment method and device, and the method comprises the steps: obtaining a first digital model and a second digital model of the oral cavity of a user, and carrying out the model registration of the first digital model and the second digital model, and obtaining a spatial conversion relation, and adjusting the occlusion relationship of the first digital model based on the spatial conversion relationship to obtain a target digital model. In the embodiment of the specification, the original first digital model is registered based on the second digital model containing the occlusion state, and the real occlusion relationship represented by the second digital model is restored to the first digital model, so that the accuracy of the occlusion adjustment result can be effectively improved, and the consistency with the real occlusion relationship of the user is higher. Manual participation is not needed, the occlusion adjustment algorithm process is simplified, occlusion adjustment deviation caused by subjective factors is avoided, and the algorithm efficiency is improved. Meanwhile, the flexibility is higher, and diversified oral rehabilitation scenes are met.

Description

Technical Field

[0001] The present invention relates to the field of intelligent oral technology, and in particular to an occlusion adjustment method and device. Background Art

[0002] In oral clinical applications, the accuracy of the occlusal relationship of the upper and lower mandibular digital models is very important, as it directly affects the tooth arrangement plan and the final correction result. If the occlusal relationship of the upper and lower mandibular digital models is inconsistent with the user's actual occlusal relationship, it may lead to deviations in the expected correction effect. Therefore, in clinical practice, it is often necessary to optimize and adjust the occlusal relationship of the upper and lower mandibular models.

[0003] In the related technology, the occlusion adjustment of the upper and lower jaw models mainly relies on manual work, and the upper and lower jaw models are optimized and adjusted manually through third-party editing tools. This requires manual participation, and the technical threshold is very high. The occlusion adjustment results are greatly affected by human factors. Summary of the invention

[0004] In order to achieve automatic adjustment of the occlusal relationship of the upper and lower jaw models, reduce the impact of manual and third-party editing tools on the occlusal adjustment results, and thereby improve the efficiency and accuracy of the occlusal adjustment, the embodiments of this specification provide an occlusal adjustment method, device, electronic device, storage medium, and computer program product.

[0005] In a first aspect, an embodiment of the present specification provides a method for adjusting occlusion, comprising:

[0006] Acquire a first digital model and a second digital model of the user's oral cavity, wherein the first digital model represents a digital model obtained by digitally processing the user's oral cavity, and the second digital model represents a digital model obtained by collecting oral data of the user's oral cavity in a occlusal state;

[0007] Performing model registration on the first digital model and the second digital model to obtain a spatial conversion relationship for mapping the first digital model to the second digital model;

[0008] The occlusal relationship of the first digital model is adjusted based on the spatial conversion relationship to obtain a target digital model of the user's oral cavity.

[0009] In some embodiments, the first digital model and the second digital model are digital models of the tooth region of the user's mouth, and acquiring the first digital model and the second digital model of the user's mouth includes:

[0010] Digitally process the user's oral cavity to obtain an oral cavity model, and segment the tooth region of the oral cavity model to obtain the first digital model;

[0011] The user's oral cavity in the occlusal state is digitally processed to obtain an occlusal model, and the tooth area of ​​the occlusal model is segmented to obtain the second digital model.

[0012] In some implementations, performing model registration on the first digital model and the second digital model to obtain a spatial conversion relationship for mapping the first digital model to the second digital model includes:

[0013] Direction matching is performed on the main axis directions of the first digital model and the second digital model, and model registration is performed based on the first digital model and the second digital model after direction matching to obtain the space conversion relationship.

[0014] In some embodiments, the spatial conversion relationship includes a first conversion matrix; performing direction matching on the main axis directions of the first digital model and the second digital model, and performing model registration on the first digital model and the second digital model after the direction matching to obtain the spatial conversion relationship includes:

[0015] Acquire first point cloud data corresponding to the first digital model, and second point cloud data corresponding to the second digital model;

[0016] Performing spatial flipping on the principal axis of the first point cloud data to obtain candidate point cloud data in multiple principal axis directions;

[0017] Performing point cloud registration based on principal component analysis on each candidate point cloud data and the second point cloud data respectively, to obtain a similarity and a transformation matrix corresponding to each candidate point cloud data, wherein the similarity represents the similarity between the candidate point cloud data and the second point cloud data;

[0018] The candidate point cloud data with the greatest similarity is determined as the target candidate point cloud data, and the transformation matrix corresponding to the target candidate point cloud data is determined as the first transformation matrix.

[0019] In some implementations, spatially flipping the principal axis of the first point cloud data to obtain candidate point cloud data in multiple principal axis directions includes:

[0020] Processing the first point cloud data to obtain a first eigenvector of the centroid of the first point cloud data, wherein a direction of the first eigenvector represents a principal axis direction of the first point cloud data;

[0021] The first point cloud data is flipped based on the main axis direction represented by the first eigenvector to obtain candidate point cloud data in multiple main axis directions.

[0022] In some implementations, performing point cloud registration based on principal component analysis on each candidate point cloud data and the second point cloud data to obtain a similarity corresponding to each candidate point cloud data includes:

[0023] Performing point cloud registration on each candidate point cloud data and the second point cloud data respectively, and calculating an average distance between each candidate point cloud data and the second point cloud data, wherein the similarity includes the average distance;

[0024] The step of determining the candidate point cloud data with the greatest similarity as the target candidate point cloud data includes:

[0025] The candidate point cloud data with the smallest average distance is determined as the target candidate point cloud data.

[0026] In some implementations, adjusting the occlusal state of the first digital model based on the spatial transformation relationship to obtain a target digital model of the user's oral cavity includes:

[0027] The first point cloud data is spatially transformed according to the first transformation matrix to obtain the target digital model.

[0028] In some implementations, the spatial conversion relationship further includes a second conversion matrix, and the performing model registration on the first digital model and the second digital model to obtain a spatial conversion relationship for mapping the first digital model to the second digital model further includes:

[0029] Performing point cloud registration based on sparse iterative closest point on the target candidate point cloud data and the second point cloud data to obtain the second transformation matrix;

[0030] The adjusting the occlusal state of the first digital model based on the spatial conversion relationship to obtain a target digital model of the user's oral cavity includes:

[0031] Performing spatial position transformation on the first point cloud data according to the first transformation matrix to obtain third point cloud data;

[0032] The third point cloud data is spatially transformed according to the second transformation matrix to obtain the target digital model.

[0033] In some embodiments, the first digital model and the second digital model include any one of an upper jaw model, a lower jaw model, an upper and lower jaw model, and a tooth model of the user's oral cavity.

[0034] In a second aspect, an embodiment of the present specification provides an occlusal adjustment device, comprising:

[0035] A model acquisition module is configured to acquire a first digital model and a second digital model of the user's oral cavity, wherein the first digital model represents a digital model obtained by digitally processing the user's oral cavity, and the second digital model represents a digital model obtained by collecting oral data of the user's oral cavity in a occlusal state;

[0036] A model registration module is configured to perform model registration on the first digital model and the second digital model to obtain a spatial conversion relationship for mapping the first digital model to the second digital model;

[0037] The occlusion adjustment module is configured to adjust the occlusion relationship of the first digital model based on the space conversion relationship to obtain a target digital model of the user's oral cavity.

[0038] In some implementations, the model acquisition module is configured to:

[0039] Digitally process the user's oral cavity to obtain an oral cavity model, and segment the tooth region of the oral cavity model to obtain the first digital model;

[0040] The user's oral cavity in the occlusal state is digitally processed to obtain an occlusal model, and the tooth area of ​​the occlusal model is segmented to obtain the second digital model.

[0041] In some embodiments, the model registration module is configured to:

[0042] Direction matching is performed on the main axis directions of the first digital model and the second digital model, and model registration is performed based on the first digital model and the second digital model after direction matching to obtain the space conversion relationship.

[0043] In some embodiments, the model registration module is configured to:

[0044] Acquire first point cloud data corresponding to the first digital model, and second point cloud data corresponding to the second digital model;

[0045] Performing spatial flipping on the principal axis of the first point cloud data to obtain candidate point cloud data in multiple principal axis directions;

[0046] Performing point cloud registration based on principal component analysis on each candidate point cloud data and the second point cloud data respectively, to obtain a similarity and a transformation matrix corresponding to each candidate point cloud data, wherein the similarity represents the similarity between the candidate point cloud data and the second point cloud data;

[0047] The candidate point cloud data with the greatest similarity is determined as the target candidate point cloud data, and the transformation matrix corresponding to the target candidate point cloud data is determined as the first transformation matrix.

[0048] In some embodiments, the model registration module is configured to:

[0049] Processing the first point cloud data to obtain a first eigenvector of the centroid of the first point cloud data, wherein a direction of the first eigenvector represents a principal axis direction of the first point cloud data;

[0050] The first point cloud data is flipped based on the main axis direction represented by the first eigenvector to obtain candidate point cloud data in multiple main axis directions.

[0051] In some embodiments, the model registration module is configured to:

[0052] Performing point cloud registration on each candidate point cloud data and the second point cloud data respectively, and calculating an average distance between each candidate point cloud data and the second point cloud data, wherein the similarity includes the average distance;

[0053] The step of determining the candidate point cloud data with the greatest similarity as the target candidate point cloud data includes:

[0054] The candidate point cloud data with the smallest average distance is determined as the target candidate point cloud data.

[0055] In some embodiments, the bite adjustment module is configured to:

[0056] The first point cloud data is spatially transformed according to the first transformation matrix to obtain the target digital model.

[0057] In some embodiments, the model registration module is configured to:

[0058] The target candidate point cloud data and the second point cloud data are subjected to point cloud registration based on sparse iterative nearest point to obtain the second transformation matrix.

[0059] In some embodiments, the bite adjustment module is configured to:

[0060] Performing spatial position transformation on the first point cloud data according to the first transformation matrix to obtain third point cloud data;

[0061] The third point cloud data is spatially transformed according to the second transformation matrix to obtain the target digital model.

[0062] In some embodiments, the first digital model and the second digital model include any one of an upper jaw model, a lower jaw model, an upper and lower jaw model, and a tooth model of the user's oral cavity.

[0063] In a third aspect, an embodiment of the present specification provides an electronic device, including:

[0064] Processor; and

[0065] A memory stores computer instructions, wherein the computer instructions are used to enable a processor to execute the method described in any of the above embodiments.

[0066] In a fourth aspect, an embodiment of this specification provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the method described in any of the above embodiments.

[0067] In a fifth aspect, an embodiment of this specification provides a computer program product, which implements the method described in any of the above embodiments when running.

[0068] The occlusion adjustment method of the implementation mode of the present specification includes obtaining a first digital model and a second digital model of the user's oral cavity, performing model registration on the first digital model and the second digital model to obtain a spatial conversion relationship, and adjusting the occlusion relationship of the first digital model based on the spatial conversion relationship to obtain a target digital model. In the implementation mode of the present specification, the original first digital model is registered based on the second digital model including the occlusion state, and the occlusion adjustment of the first digital model is performed based on the spatial conversion relationship obtained by the registration, so that the real occlusion relationship represented by the second digital model is restored to the first digital model, which can effectively improve the accuracy of the occlusion adjustment result and make it more consistent with the real occlusion relationship of the user. In addition, there is no need for human intervention, which simplifies the occlusion adjustment algorithm process, avoids occlusion adjustment deviations caused by subjective factors, and improves the efficiency of the algorithm. At the same time, it is more flexible and meets the needs of various oral restoration scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is a flow chart of an occlusion adjustment method in some embodiments of the present specification.

[0070] Figure 2 is a flow chart of an occlusion adjustment method in some embodiments of the present specification.

[0071] Figure 3 It is a schematic diagram of the principle of the occlusion adjustment method in some embodiments of the present specification.

[0072] Figure 4 is a flow chart of an occlusion adjustment method in some embodiments of the present specification.

[0073] Figure 5 It is a schematic diagram of the principle of the occlusion adjustment method in some embodiments of the present specification.

[0074] Figure 6 is a flow chart of an occlusion adjustment method in some embodiments of the present specification.

[0075] Figure 7 is a flow chart of an occlusion adjustment method in some embodiments of the present specification.

[0076] Figure 8 is a flow chart of an occlusion adjustment method in some embodiments of the present specification.

[0077] Fig. 9 It is a schematic diagram of the principle of the occlusion adjustment method in some embodiments of the present specification.

[0078] Fig.10 It is a schematic diagram of the principle of the occlusion adjustment method in some embodiments of the present specification.

[0079] Fig.11 It is a structural block diagram of the occlusal adjustment device in some embodiments of this specification.

[0080] Fig.12 It is a structural block diagram of an electronic device in some implementation modes of this specification. DETAILED DESCRIPTION

[0081] The technical solution of this specification will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described implementation is a part of the implementation of this specification, not all of the implementation. Based on the implementation in this specification, all other implementations obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification. In addition, the technical features involved in the different implementations of this specification described below can be combined with each other as long as they do not conflict with each other.

[0082] With the development of intelligent oral treatment technology, oral restoration technology based on three-dimensional digital models has been widely used in actual clinical practice. Taking orthodontics as an example, three-dimensional modeling technologies such as oral scanning can be used to build digital models of the user's upper and lower jaws, and tooth arrangement and correction plans can be implemented based on the digital models of the upper and lower jaws.

[0083] In the field of orthodontics / restoration, the accuracy of the occlusal relationship of the upper and lower jaw models is very important. If the occlusal relationship of the upper and lower jaw digital models is inconsistent with the user's actual occlusal relationship, it may lead to deviations in the expected correction effect. For example, it may affect the comfort of the final designed restoration in the user's mouth, causing the restoration to fail to work properly.

[0084] In oral clinical applications, intraoral scanners are usually used to convert the user's real intraoral upper and lower jaw information into a three-dimensional digital model. The three-dimensional digital model mainly includes the shape of teeth and gums. At the same time, the intraoral scanner can directly obtain the occlusion relationship of the user's mouth. Generally, after scanning and obtaining the upper and lower jaw models, the mouth is kept in a natural occlusion state, and then the intraoral scanner is used to scan the entire jaw area in the occlusion state. Based on the overlapping relationship between the upper and lower jaws and the entire jaw, the upper and lower jaws are aligned to the current occlusion state to obtain an occlusion relationship that is closer to the real one.

[0085] However, due to the deformation errors in the scanning process (such as the model deformation errors of the upper and lower jaw data themselves, the deformation errors of the whole jaw data) and the influence of interferences such as bubble noise on the model, the occlusal relationship of the upper and lower jaw models obtained by scanning is not accurate enough. Moreover, in actual clinical practice, as the correction stage progresses, it is often necessary to provide oral digital models with different occlusal relationships according to clinical needs, which requires the occlusal relationship of the upper and lower jaw models to be adjusted. The operation process of adjusting the occlusal relationship of the upper and lower jaw models is called occlusal adjustment.

[0086] One possible implementation method is to make occlusal adjustments for the occlusal relationship of the upper and lower jaw models, which can rely on manual and third-party editing tools. For example, manual adjustments can be made based on the user's intraoral photos and the original upper and lower jaw models. Specifically, the operator refers to the user's occlusal photos in multiple directions, and first makes initial adjustments to the upper and lower jaw model files through operations such as translation and rotation on the third-party model editor software, and then makes fine adjustments based on the midline position, posterior tooth coverage, etc., to achieve the purpose of occlusion consistency with the photo. For example, the upper and lower jaw models can also be adjusted according to the occlusal adjustment algorithm. Specifically, the operator can pre-mark the tooth area of ​​the upper and lower jaw models, and then set the iteration parameters to iteratively calculate the distance between the maxillary and mandibular tooth pairs, and adjust the occlusal relationship until the calculated distance meets the preset conditions to obtain a more accurate occlusal result.

[0087] In the above-mentioned related technical solutions, the process of occlusal adjustment requires different degrees of manual intervention, requiring the operator to have extremely rich experience in oral restoration and the ability to perceive between two-dimensional photos and three-dimensional models. The adjustment results obtained by operators of different levels vary greatly. Even for the same operator on the same patient, the results of occlusal adjustment cannot be completely consistent in different scenarios and working conditions. In addition, manual setting of parameters also needs to rely on the operator's experience, and there is a lack of objective guidance on the parameter rules for complex cases, resulting in low efficiency of occlusal adjustment and high subjective dependence.

[0088] Furthermore, the above-mentioned occlusal adjustment process inevitably needs to rely on third-party model editing software. The operator needs to perform various operations on the model such as translation, rotation, and configuration parameters on the software, which requires the operator to be proficient in the editing software and increases the learning cost of the operator.

[0089] Based on the above problems, the embodiments of this specification provide an occlusion adjustment method, device, electronic device, storage medium and computer program product, aiming to provide an occlusion adjustment method that reduces dependence on manual experience and third-party editing software, and performs occlusion adjustment based on the alignment of the original model and the occlusion relationship model, which helps to improve the efficiency of occlusion adjustment and the accuracy of the adjustment results.

[0090] In some embodiments, this specification provides a method for occlusal adjustment, which can be processed by an electronic device. In the embodiments of this specification, the electronic device can be any type of device suitable for implementation, such as an intraoral scanner, a computer, a server, a mobile terminal, a wearable device, etc., and this specification does not limit this.

[0091] like Figure 1 As shown, in some embodiments, the occlusal adjustment method exemplified in this specification includes:

[0092] S110 , obtaining a first digital model and a second digital model of the user's oral cavity.

[0093] In the implementation manner of this specification, the oral cavity refers to the cavity in the user's mouth, which may include tissues or organs such as teeth, crowns, gums, jaws, etc. For example, when performing oral restoration on a user, it is necessary to convert the user's oral information into a three-dimensional digital model, and the three-dimensional digital model is the first digital model of the user's oral cavity described in this specification.

[0094] In some embodiments, the first digital model may be obtained by performing a three-dimensional digital scan of the user's oral cavity. The first digital model includes at least a tooth model of the user's oral cavity and may further include a model of the gums or other areas.

[0095] It is worth noting that in an actual oral restoration scenario, it may only be necessary to perform half-mouth occlusion restoration on the user's oral cavity, so that the first digital model may only include the maxillary model or the mandibular model that needs to be restored. It may also be necessary to perform full-mouth restoration on the user's oral cavity, so that the first digital model may include both the maxillary model and the mandibular model. Those skilled in the art will undoubtedly understand this, and this specification will not be specifically explained below unless necessary.

[0096] In some implementations, an oral cavity scanning device may be used to scan the user's oral cavity, thereby constructing a first digital model of the user's oral cavity.

[0097] In one example, when scanning the user's mouth, a two-dimensional color image and a three-dimensional point cloud data frame can be obtained for each scanning position, and then the two-dimensional color image is recognized based on a preset artificial intelligence model to obtain the recognition results corresponding to the pixel points on the two-dimensional color image. Then, for the same scanning position, the recognition results are mapped to the three-dimensional data points of the three-dimensional point cloud data frame. Finally, all the three-dimensional point cloud data frames are fused to obtain the first digital model of the user's mouth.

[0098] It can be understood that the above process is only an example of obtaining the first digital model in the implementation of this specification, and the process of obtaining the first digital model is not limited to the above method. The first digital model can also be constructed by other methods with reference to relevant technologies in this field, and this specification will not repeat them.

[0099] In the above embodiment, the occlusal relationship of the original first digital model is not directly adjusted. Instead, a second digital model of the user's mouth is further introduced. The second digital model can reflect the actual occlusal relationship of the user's mouth under a preset occlusal state. The occlusal relationship of the first digital model is adjusted based on the second digital model, and an adjustment result that is closer to the user's actual occlusal relationship can be obtained.

[0100] In the implementation manner of this specification, the second digital model refers to a three-dimensional digital model of the user's oral cavity in an occlusal state, and the occlusal state refers to any one or more occlusal states required in oral restoration. In general scenarios, the occlusal state can be the state when the upper and lower jaws of the user are naturally occluded. In some special scenarios, the occlusal relationship of the user in a specific occlusal posture may also be required, such as deep overbite, deep overjet, open bite, etc., so that the user bites according to the specific occlusal posture.

[0101] In the implementation manner of this specification, the second digital model may only include a digital model obtained in a certain occlusal state of the target oral cavity, so that based on the occlusal adjustment method of this specification, the occlusal relationship of the first digital model can be adjusted based on the second digital model. The second digital model may also include a digital model obtained in multiple occlusal states of the user's oral cavity, so that based on the occlusal adjustment method of this specification, the occlusal relationship of the first digital model can be adjusted based on the second digital model in each occlusal state, and the adjustment results of the first digital model in multiple occlusal states are obtained.

[0102] It should be noted that the occlusion adjustment method between the second digital model and the first digital model in each occlusal state can refer to each other. The occlusal adjustment method of this specification is described below using the second digital model in one occlusal state as an example.

[0103] In some embodiments, the process of obtaining the second digital model may include: allowing the user to bite a specific material (such as an impression) according to a preset occlusal state (such as a natural occlusal state), and the occlusal result can be obtained after the impression material is solidified. Then, the impression occlusal result is poured with a mold material (such as plaster) to fill the upper and lower jaw areas respectively, and the upper and lower jaw dentition models are obtained after the mold material is solidified. Finally, the dentition model is scanned by digital means (such as blue light scanning or other scanning methods) to obtain a second digital model containing a correct occlusal relationship.

[0104] Of course, it can be understood that the above process is only an example of obtaining the second digital model in the implementation of this specification, and the process of obtaining the second digital model is not limited to the above method. Those skilled in the art can also construct the second digital model of the user's oral cavity in other ways with reference to relevant technologies, such as oral CBCT (Cone beam Computer Tomography, oral and maxillofacial cone beam scanning), model reconstruction based on oral two-dimensional images, etc., which will not be repeated in this specification.

[0105] In the field of computer graphics, mesh models are usually used to represent the geometric form of three-dimensional objects. In the implementation of this specification, the model form corresponding to the first digital model and the second digital model can be a mesh model.

[0106] The mesh model is composed of a large number of discrete vertices, edges connecting two vertices, and mesh patches composed of multiple edges. In some embodiments, the mesh patches can be triangular patches, that is, the face of a triangle composed of three vertices is a mesh patch, so that the mesh model is composed of a continuous surface composed of a large number of triangular patches. In other embodiments, the mesh patches can be quadrilaterals, that is, the face of a quadrilateral composed of four vertices is a mesh patch, so that the mesh model is composed of a continuous surface composed of a large number of quadrilateral patches.

[0107] The point cloud data described below in this specification may be a data set consisting of all vertex-related information on a mesh model.

[0108] S120 , performing model registration on the first digital model and the second digital model to obtain a spatial conversion relationship for mapping the first digital model to the second digital model.

[0109] In combination with the above, it can be known that the goal of occlusal adjustment is to restore the correct occlusal relationship contained in the second digital model to the first digital model. That is, the occlusal relationship of the first digital model is adjusted according to the occlusal relationship of the second digital model, so that the upper and lower jaw occlusal relationship of the first digital model is closer to the user's real occlusal relationship.

[0110] In the implementation manner of this specification, after obtaining the first digital model and the second digital model, the mapping relationship between the points on the first digital model and the points on the second digital model can be determined, and the mapping relationship is the spatial conversion relationship. Therefore, the spatial position of the first digital model can be adjusted based on the spatial conversion relationship, and the occlusion relationship of the first digital model can be adjusted to be the same as or close to the actual occlusion relationship of the user.

[0111] In the implementation manner of this specification, the model alignment of two digital models refers to the alignment of the tooth area included in the model. It can be understood that the three-dimensional digital model obtained by digital scanning and other means, in addition to the tooth area, often also includes noise parts such as gums and bases. These noise parts may cause a decrease in accuracy during model alignment and affect the occlusal adjustment results.

[0112] In some embodiments, the first digital model and the second digital model may be digital models of the tooth region of the user's mouth. For example, the original oral model of the user's mouth may be obtained by digital scanning or other means, and then the tooth region of the oral model may be segmented to obtain the first digital model by retaining only the tooth region. The segmentation process of the second digital model is similar to the segmentation process of the second digital model. Figure 2 Provide explanation.

[0113] In some embodiments, combined with the above, it can be seen that the first digital model and the second digital model are both Mesh mesh models, and the corresponding model data is point cloud data. In the case of high mesh model accuracy, the amount of vertex data corresponding to the model is huge, which leads to a large amount of calculation in the model registration process, and is easy to cause local calculation convergence, fall into local minimization, and lead to unsatisfactory registration results.

[0114] In some embodiments of the present specification, the point cloud data of the first digital model and the second digital model may be downsampled before model registration, thereby reducing the number of vertices and reducing the computational overhead. Figure 4 Provide explanation.

[0115] It can be understood that the above-mentioned process of segmenting the tooth region model and downsampling the point cloud is optional and not necessary.

[0116] In some embodiments, a model registration algorithm based on point cloud data may be used to register the first digital model and the second digital model, such as a PCA (Principal Component Analysis) algorithm, a SICP (Sparse Iterative Closest Point) algorithm, etc., which is not limited in this specification.

[0117] Through the model registration process of the above example, the spatial conversion relationship between the first digital model and the second digital model can be obtained, and the spatial conversion relationship can be understood as the mapping relationship between points on the first digital model and points on the second digital model. Based on the spatial conversion relationship, any point on the first digital model can be mapped to the position space of the second digital model, so as to adjust the occlusal relationship of the first digital model.

[0118] In some embodiments, in the initial state, the spatial position and orientation of the first digital model and the second digital model may be greatly different. For example, the model may be flipped left and right, up and down, front and back, or a combination of flips. If the model is directly registered, the calculation process may fall into a local optimal solution, and the model cannot correctly converge to overlap.

[0119] In some embodiments of the present specification, in the process of registering the first digital model with the second digital model, the main axis direction of the first digital model can be calculated first, and then the first digital model can be flipped to be roughly the same as the main axis direction of the second digital model through model registration to complete the rough registration, and then the first digital model and the second digital model after the rough registration are finely registered, which effectively reduces the risk of registration failure and improves the accuracy of occlusal adjustment. Figure 6 and Figure 7 Provide explanation.

[0120] S130 . Adjust the occlusal relationship of the first digital model based on the spatial transformation relationship to obtain a target digital model of the user's oral cavity.

[0121] As can be seen from the above, the spatial conversion relationship represents the spatial mapping relationship between the first digital model and the second digital model, so when the first digital model is adjusted for occlusion, each point on the first digital model can be mapped and adjusted based on the spatial conversion relationship. For example, each point on the first digital model is spatially converted once according to the spatial conversion relationship to obtain a target digital model with the same occlusion state.

[0122] Based on the above, it can be seen that in the implementation mode of the present specification, the original first digital model is aligned based on the second digital model including the occlusal state, and the occlusal adjustment of the first digital model is performed based on the spatial transformation relationship obtained by the alignment, so that the actual occlusal relationship represented by the second digital model is restored to the first digital model, which can effectively improve the accuracy of the occlusal adjustment result and make it more consistent with the user's actual occlusal relationship.

[0123] In addition, in the implementation manner of the present specification, the operator only needs to generate the first digital model and the second digital model of the user's mouth as needed, and then input the two sets of models into the algorithm end to automatically complete the occlusion adjustment process. There is no need for manual editing, nor is there any need to manually adjust the relevant parameters of the occlusion adjustment process, thereby avoiding occlusion adjustment deviations due to subjective factors, reducing dependence on third-party model editing tools, simplifying the occlusion adjustment algorithm process, and improving algorithm efficiency.

[0124] Furthermore, in the embodiments of the present specification, the occlusion adjustment method is more flexible, and can be used to adjust the occlusion of a half-mouth digital model or a full-mouth digital model. At the same time, it can also adapt to adjustment scenarios of multiple occlusal states. For example, if the occlusal relationship of the first digital model in multiple occlusal states needs to be adjusted, it is only necessary to obtain the same first digital model, and then obtain multiple second digital models in different occlusal states, and repeat the above method process in sequence, so that the occlusion adjustment can be performed on the first digital model in multiple different occlusal states to meet the diverse oral restoration scenarios.

[0125] In combination with the foregoing, it can be known that in some embodiments, the first digital model and the second digital model described in this specification are both models obtained by segmenting the tooth area of ​​the oral model, that is, the first digital model and the second digital model only include the tooth area of ​​the user's oral cavity, so that in the model alignment process, only the tooth model is used for alignment, which avoids irrelevant noise interference and improves the model alignment accuracy. Figure 2 Provide explanation.

[0126] like Figure 2 As shown, in some embodiments, the occlusion adjustment method exemplified in this specification, the process of obtaining the first digital model and the second digital model includes:

[0127] S210, digitally process the user's oral cavity to obtain an oral cavity model, and segment the tooth area of ​​the oral cavity model to obtain a first digital model.

[0128] S220, digitally process the user's oral cavity in an occlusal state to obtain an occlusal model, and segment the tooth region of the occlusal model to obtain a second digital model.

[0129] In the implementation manner of this specification, firstly, the oral model and occlusal model of the user's mouth can be obtained through various digital processing methods. The oral model refers to a three-dimensional digital model obtained by digitally processing the intraoral information of the user's mouth, which may include models of tissues and organs such as teeth, gums, and cheeks. The occlusal model refers to a three-dimensional digital model of the user's mouth in one or more occlusal states, which may also include models of tissues and organs such as teeth, gums, and cheeks.

[0130] The methods for obtaining oral and occlusal models have been given above in this specification, and those skilled in the art can understand and fully implement them by referring to the above, for example, through intraoral scanning, oral CBCT, model reconstruction based on oral two-dimensional images, etc., and this specification will not go into details.

[0131] In the implementation manner of this specification, the purpose of model segmentation is to identify the tooth area and non-tooth area from the model, the non-tooth area is, for example, the gums, base, cheeks and other noise parts, so that only the model part of the tooth area is retained through model segmentation, and the model part of the non-tooth area is deleted.

[0132] It can be understood that there are many three-dimensional model segmentation algorithms, and the implementation methods of this specification do not impose too many restrictions on this. Any model segmentation algorithm can be used. Model segmentation algorithms include but are not limited to geometric topology, neural networks, deep learning and other methods.

[0133] In one implementation, a model segmentation algorithm based on deep learning is used as an example. First, a tooth segmentation network based on deep learning can be pre-built, the input of the tooth segmentation network is a three-dimensional model, and the output is a tooth region of the three-dimensional model. The tooth segmentation network can be, for example, a deep learning network based on an architecture such as 3D-CNNs, PointNet, or PointNet++.

[0134] In the network training stage, a large number of training data sets containing three-dimensional models of tooth regions can be collected by manual annotation, and the tooth segmentation network can be trained based on the training data sets until the network converges and the training is stopped. The network training process can adopt the traditional supervised training process, which can be understood and fully implemented by those skilled in the art with reference to relevant technologies, and will not be described in detail in this specification.

[0135] In the prediction stage, the oral model or the occlusal model can be input into the trained tooth segmentation network, so that the tooth segmentation network can predict and output the tooth area corresponding to the model. For example, the oral model is input into the tooth segmentation network to obtain a first digital model containing only the tooth area in the oral model. The occlusal model is input into the tooth segmentation network to obtain a second digital model containing only the tooth area in the occlusal model.

[0136] In one example, the oral cavity model takes the upper and lower jaw models of the user's oral cavity as an example. Figure 3 Figure 2 shows the visualization result of processing the oral model through the above tooth segmentation network. Figure 3 As shown, the colored area in the figure is the tooth area, and the gray area is the non-tooth area.

[0137] Continue to refer to Figure 3As shown, in this example, after the tooth region is segmented from the oral model, each tooth can be further segmented individually, for example Figure 3 Each tooth is represented by a different color. Of course, the model segmentation result can also be to only segment the tooth area and the non-tooth area, without the need to segment each tooth separately.

[0138] The above description uses the tooth region segmentation process of the oral model as an example. The segmentation process of the occlusal model is the same, and the above description may be referred to, and no further elaboration is given.

[0139] From the above, it can be seen that in the implementation mode of this specification, the first digital model and the second digital model are obtained by segmenting the oral model and the occlusal model, thereby removing the interference of non-tooth areas such as gums, noise, and base, providing an accurate data basis for subsequent model alignment, thereby improving the algorithm accuracy of occlusal adjustment.

[0140] Combined with the above, it can be seen that the first digital model and the second digital model are both Mesh mesh models, and the corresponding mesh vertex data is point cloud data. When the mesh model has high accuracy, the amount of vertex data corresponding to the model is huge, which leads to a large amount of calculation in the model registration process, and is easy to cause local calculation convergence and fall into local minimization, resulting in unsatisfactory registration results.

[0141] Therefore, in some embodiments of the present specification, the point cloud data of the first digital model and the second digital model can be downsampled before model registration. Figure 4 This process is described.

[0142] like Figure 4 As shown, in some embodiments, the occlusion adjustment method exemplified in this specification, based on the process of performing model registration on the first digital model and the second digital model, includes:

[0143] S410: Acquire first point cloud data corresponding to the first digital model and second point cloud data corresponding to the second digital model.

[0144] S420, downsampling the first point cloud data and the second point cloud data, and performing point cloud registration based on the downsampling first point cloud data and the second point cloud data to obtain a space transformation matrix.

[0145] In combination with the foregoing, it can be known that the data corresponding to the first digital model and the second digital model are point cloud data, so the point cloud data of the first digital model is defined as the first point cloud data, and the point cloud data corresponding to the second digital model is defined as the second point cloud data.

[0146] The purpose of point cloud downsampling is to reduce the number of vertices in the point cloud model. There are many algorithms for downsampling the first point cloud data and the second point cloud data, such as a grid-based point cloud downsampling algorithm, a Poisson equation-based point cloud downsampling algorithm, etc. This manual does not limit this.

[0147] Taking the grid-based point cloud downsampling algorithm as an example, the point cloud data can be spatially divided by a grid of a fixed size. The grid is a cube with a certain side length, or it can be other shapes, which are not limited here. It can be understood that by using the grid to spatially divide the point cloud data, for the grid with points, each grid will contain one or more points, and then the distance between each point in the grid and the center point of the grid can be calculated respectively, and then only the point with the smallest distance is retained, and the rest of the points in the grid are deleted. By performing the above operation on each grid, the point cloud downsampling process can be completed.

[0148] It can be understood that after the first point cloud data and the second point cloud data are processed through the above process, the number of vertices included in the point cloud data will be greatly reduced, thereby reducing the data volume and computing overhead. Figure 5 The visualization result after downsampling the first point cloud data is shown.

[0149] In the implementation mode of this specification, after the downsampling processing of the first point cloud data and the second point cloud data is completed, point cloud registration can be performed based on the first point cloud data and the second point cloud data after the data volume is reduced to obtain the spatial conversion relationship between the first digital model and the second digital model. Figure 6 The process of point cloud registration is explained.

[0150] like Figure 6 As shown, in some embodiments, the occlusal adjustment method exemplified in this specification, based on the process of performing model registration on the first digital model and the second digital model to obtain a spatial transformation relationship, includes:

[0151] S610: Acquire first point cloud data corresponding to the first digital model and second point cloud data corresponding to the second digital model.

[0152] In combination with the above, it can be known that in the implementation of this specification, the point cloud data of the first digital model is defined as the first point cloud data, and the point cloud data corresponding to the second digital model is defined as the second point cloud data. When performing point cloud registration, the first point cloud data can be used as the source point cloud data, and the second point cloud data can be used as the target point cloud data.

[0153] S620 , spatially flip the principal axis of the first point cloud data to obtain candidate point cloud data in multiple principal axis directions.

[0154] It can be understood that the principal axis direction of the point cloud refers to the direction of the principal eigenvector of the point cloud centroid. The principal axis direction of the point cloud can be calculated through the following process: first, the centroid of the point cloud can be calculated. The centroid is the average position of all points in the point cloud data. The purpose of calculating the centroid is to convert the point cloud into a coordinate system centered on the centroid. Then, for each point in the point cloud, the coordinates of the point relative to the centroid are calculated to construct a covariance matrix. The covariance matrix can reflect the correlation between each point in the point cloud data and the centroid. Then, the eigenvectors and eigenvalues ​​of multiple main directions of the point cloud data are calculated according to the covariance matrix. Finally, the eigenvalues ​​of these eigenvectors are sorted. The eigenvector with the largest eigenvalue represents the principal eigenvector of the point cloud data, and the direction of the principal eigenvector is the principal axis direction of the point cloud.

[0155] In the implementation manner of this specification, after the first point cloud data and the second point cloud data are acquired, the main feature vectors and the main axis directions of the first point cloud data and the second point cloud data can be calculated by the aforementioned method process.

[0156] It can be understood that when the main axis directions of the first point cloud data and the second point cloud data are consistent or very close, the spatial position relationship between the first point cloud data and the second point cloud data should also be consistent or very close. On the contrary, when the main axis directions of the first point cloud data and the second point cloud data are very different, such as left-right flipping, up-down flipping, front-back flipping, and combined flipping, the spatial position relationship between the first point cloud data and the second point cloud data is also very different. If the registration is performed directly, the calculation process may fall into a local optimal solution, and the model cannot correctly converge to overlap.

[0157] Based on this, in some embodiments of this specification, by flipping the main axis, the main axis of the first point cloud data traverses all directions to obtain candidate point cloud data in multiple main axis directions. Figure 7 Provide explanation.

[0158] like Figure 7 As shown, in some embodiments, the bite adjustment method exemplified in this specification, the process of obtaining candidate point cloud data in multiple main axis directions includes:

[0159] S621. Process the first point cloud data to obtain a first eigenvector of the centroid of the first point cloud data.

[0160] S622. Flip the first point cloud data based on the main axis direction represented by the first eigenvector to obtain candidate point cloud data under multiple main axis directions.

[0161] In combination with the foregoing, it can be known that the centroid of the first point cloud data can be calculated first, and then the coordinates of each point in the first point cloud data relative to the centroid can be calculated respectively and a covariance matrix can be constructed. Then, the principal eigenvector of the first point cloud data is calculated according to the covariance matrix. The principal eigenvector is also the first eigenvector described in this specification, and the direction of the first eigenvector is the main axis direction of the first point cloud data. The specific process of calculating the first eigenvector and the main axis direction can be understood by those skilled in the art in combination with the foregoing, and will not be described in detail here.

[0162] Specifically, the right-hand rule is a method used to determine the axis, which ensures the orientation consistency of the coordinate system. The specific principle is: when you need to determine the direction of a coordinate axis in a three-dimensional space, you can use your index finger and middle finger to point to the x-axis and y-axis directions respectively, then the direction pointed by the thumb is the z-axis direction.

[0163] According to the above right-hand rule, for a three-dimensional eigenvector, the direction of the third dimension can be obtained by cross product calculation after determining the direction of two dimensions. Therefore, there are only four possible combinations of directions of the first eigenvector, represented as [1,1], [1,-1], [-1,1], [-1,-1], 1 means the current axis remains unchanged, -1 means the current axis is flipped, and the axis of the third axis can be calculated by cross product without repeated calculation.

[0164] Therefore, in the implementation of this specification, based on the above four possibilities, the direction of the first eigenvector can be changed in sequence, that is, the main axis direction of the first point cloud data can be changed, so as to obtain candidate point cloud data corresponding to the four coordinate axis directions.

[0165] S630 , performing point cloud registration based on principal component analysis on each candidate point cloud data and the second point cloud data, respectively, to obtain a similarity and a transformation matrix corresponding to each candidate point cloud data.

[0166] The PCA algorithm is also called the principal component analysis algorithm, which is a statistical method. In some embodiments of the present disclosure, the PCA algorithm can be used to achieve registration between the candidate point cloud data and the second point cloud data.

[0167] Combined with the above, it can be seen that the candidate point cloud data refers to multiple point cloud data obtained by flipping the first point cloud data in the direction of the principal axis. The purpose of PCA alignment is to align each candidate point cloud data with the second point cloud data respectively, and then find the point cloud that is most similar to the second point cloud data, that is, the target candidate point cloud data.

[0168] Taking any candidate point cloud data as an example, when performing PCA registration on the candidate point cloud data and the second point cloud data, the principal components of the two point cloud data can be aligned first, and the principal component is the first eigenvector mentioned above. That is, the first eigenvector of the candidate point cloud data is aligned with the principal eigenvector of the second point cloud data to obtain the rotation matrix and the center of mass translation matrix between the two, and the transformation matrix between the two point clouds is obtained based on the rotation matrix and the center of mass translation matrix. At the same time, the average distance between the candidate point cloud data and the second point cloud data can be calculated, and the average distance is used to represent the similarity between the candidate point cloud data and the second point cloud data. Therefore, through this process, the similarity and transformation matrix corresponding to each candidate point cloud data can be calculated respectively.

[0169] There are many ways to calculate the average distance between two point clouds, which are not limited in this specification. The following is a method for calculating the average distance between the candidate point cloud data and the second point cloud data based on a KD tree (K-dimensional tree).

[0170] Taking the candidate point cloud data in a certain main axis direction as an example, we can first build a KD tree for the candidate point cloud data. The KD tree is a binary tree with each node representing a point. Then, for each point in the second point cloud data, we use the KD tree to perform a nearest neighbor search to find the distance to the closest point in the candidate point cloud data. After that, we add up the nearest neighbor distances of all query points and divide them by the total number of query points to get the average distance between the candidate point cloud data and the second point cloud data.

[0171] The above method can be used to calculate the average distance between each candidate point cloud data and the second point cloud data. The average distance represents the similarity between the candidate point cloud data and the second point cloud data. The smaller the average distance, the higher the similarity, that is, the closer the main axis direction of the candidate point cloud data is to the second point cloud data. Conversely, the larger the average distance, the lower the similarity, that is, the greater the difference between the main axis direction of the candidate point cloud data and the second point cloud data.

[0172] S640: Determine the candidate point cloud data with the greatest similarity as the target candidate point cloud data, and determine the transformation matrix corresponding to the target candidate point cloud data as the first transformation matrix.

[0173] As can be seen from the foregoing, after respectively calculating the similarity and transformation matrix corresponding to each candidate point cloud data, the candidate point cloud data with the greatest similarity can be determined as the target candidate point cloud data, and its corresponding transformation matrix is ​​the first transformation matrix.

[0174] In the implementation manner of this specification, the first transformation matrix represents the spatial transformation relationship between the first digital model and the second digital model. In some implementations, the first transformation matrix is ​​represented as PCA-Trans, and the first transformation matrix PCA-Trans represents the mapping relationship between points on the first point cloud data and the second point cloud data.

[0175] From the above, it can be seen that in the implementation of this specification, by utilizing the PCA point cloud registration method combined with the flipping of the main axis direction, the impact of the difference in the main axis direction between the first digital model and the second digital model can be effectively alleviated, thereby improving the model registration accuracy.

[0176] In some implementations, after the first transformation matrix PCA-Trans is obtained through the aforementioned method process, the first transformation matrix PCA-Trans can be directly used to perform spatial position transformation on the first point cloud data to obtain a target digital model.

[0177] In other embodiments, in order to further improve the registration accuracy, the PCA algorithm based on the principal axis direction adjustment can be used as a rough registration for the first point cloud data, and then further combined with the SICP algorithm as a point cloud fine registration to obtain a more accurate point cloud registration result. Figure 8 Provide explanation.

[0178] like Figure 8 As shown, in some embodiments, the spatial transformation relationship between the first digital model and the second digital model includes not only the first transformation matrix mentioned above, but also the second transformation matrix. The process of performing model registration on the first digital model and the second digital model includes:

[0179] S810 , performing point cloud registration based on sparse iterative nearest points on the target candidate point cloud data and the second point cloud data to obtain a second transformation matrix.

[0180] SICP algorithm refers to the sparse iterative closest point algorithm, which is mainly used to handle the registration problem of large-scale point cloud data, and realizes point cloud alignment by iteratively finding the best match between the source point cloud and the target point cloud. In the embodiment of the present disclosure, the SICP algorithm can be used to further align the target candidate point cloud data after the principal axis adjustment and PCA registration.

[0181] Specifically, in some embodiments, the adjustment of SICP registration can be set as follows: if the number of registrations is less than k1, the minimum distance d1 between each point in the target candidate point cloud data and the corresponding point in the target point cloud data is calculated. If d1>k2*L1, the point is considered to be an outlier and deleted, completing an update of the target candidate point cloud data until the number of registrations is greater than or equal to k1, completing the SICP registration process and obtaining the second transformation matrix, which can be expressed as SICP-Trans.

[0182] In an example, the parameter k1 is set to 3, the initial value of k2 is 6, and the value of k2 decreases by 2 after each iteration. L1 is Figure 4 The grid side length in the implementation. In addition, the variable parameters of SICP registration are norm p and number of iterations n. It can be understood that the above parameter values ​​can be adjusted according to the specific scene requirements and are not limited to the above examples in this specification.

[0183] In the implementation manner of this specification, through the above-mentioned SICP point cloud registration process, the target candidate point cloud data and the second point cloud data can be further finely registered to obtain the second transformation matrix SICP-Trans between the two.

[0184] After obtaining the first transformation matrix PCA-Trans and the second transformation matrix SICP-Trans through the above method, the occlusal relationship of the original model can be adjusted based on the first transformation matrix PCA-Trans and the second transformation matrix SICP-Trans.

[0185] S820. Perform spatial position transformation on the first point cloud data according to the first transformation matrix to obtain third point cloud data.

[0186] S830. Perform spatial position transformation on the third point cloud data according to the second transformation matrix to obtain a target digital model.

[0187] In some implementations of this specification, the spatial transformation relationship between the first digital model and the second digital model includes the aforementioned first transformation matrix PCA-Trans and the second transformation matrix SICP-Trans. As can be seen from the foregoing, the first transformation matrix PCA-Trans refers to a transformation matrix for coarse registration of the first digital model, and the second transformation matrix SICP-Trans refers to a transformation matrix for fine registration of the first digital model.

[0188] Therefore, in the implementation of the present specification, when the first digital model is adjusted for occlusion, the points on the first point cloud data of the first digital model can first be spatially transformed based on the first transformation matrix PCA-Trans to achieve rough registration of the first digital model and obtain the third point cloud data. Then, on this basis, the points on the third point cloud data are further spatially transformed based on the second transformation matrix SICP-Trans to achieve fine registration of the third point cloud data and obtain the target digital model after the final occlusion adjustment, which is a mouth model containing an occlusion relationship that is the same as or close to the user's actual occlusion relationship.

[0189] From the above, it can be seen that in the implementation of this specification, by adjusting the main axis direction of the first digital model, the directions of the first digital model and the second digital model are kept as consistent or close as possible without manual intervention, which, on the one hand, accelerates the model registration process and allows the calculation results to converge quickly, and on the other hand, improves the model registration accuracy, thereby improving the accuracy of the occlusal adjustment results. In addition, in the implementation of this specification, a model registration method based on the main axis direction adjustment PCA coarse registration combined with SICP fine registration is adopted to improve the accuracy of the model registration results, thereby improving the accuracy of the occlusal adjustment results.

[0190] In one example, see Fig. 9 As shown, Fig. 9 The visualization result of the model registration between the first digital model and the second digital model is shown. Fig. 9 The medium blue represents the first digital model, and the gray represents the second digital model. Fig. 9 It can be seen that the model registration in the implementation manner of this specification has good accuracy.

[0191] In one example, Fig.10 A comparison diagram of the effects of the bite adjustment method of this specification is shown. Fig.10 (a) is the visualization result of the occlusal relationship of the original model that has not been adjusted by the method in this manual. Fig.10 (b) is the visualization result of the occlusal relationship of the model after adjustment using the method in this manual. Fig.10 As can be seen from (a) and (b), the model has a more accurate occlusion relationship after adjustment using the method described in this specification, thereby obtaining an adjustment result that is closer to the user's actual occlusion relationship.

[0192] In some embodiments, this specification provides an occlusal adjustment device, which can be applied to an electronic device. In the embodiments of this specification, the electronic device can be any type of device suitable for implementation, such as an intraoral scanner, a computer, a server, a mobile terminal, a wearable device, etc., and this specification does not limit this.

[0193] like Fig.11 As shown, in some embodiments, the bite adjustment device of the present specification example includes:

[0194] The model acquisition module 10 is configured to acquire a first digital model and a second digital model of the user's oral cavity, wherein the first digital model represents a digital model obtained by digitally processing the user's oral cavity, and the second digital model represents a digital model obtained by collecting oral data of the user's oral cavity in an occlusal state;

[0195] A model registration module 20 is configured to perform model registration on the first digital model and the second digital model to obtain a spatial conversion relationship for mapping the first digital model to the second digital model;

[0196] The occlusion adjustment module 30 is configured to adjust the occlusion relationship of the first digital model based on the space conversion relationship to obtain a target digital model of the user's oral cavity.

[0197] In some implementations, the model acquisition module 10 is configured to:

[0198] Digitally process the user's oral cavity to obtain an oral cavity model, and segment the tooth region of the oral cavity model to obtain the first digital model;

[0199] The user's oral cavity in the occlusal state is digitally processed to obtain an occlusal model, and the tooth area of ​​the occlusal model is segmented to obtain the second digital model.

[0200] In some embodiments, the model registration module 20 is configured to:

[0201] Direction matching is performed on the main axis directions of the first digital model and the second digital model, and model registration is performed based on the first digital model and the second digital model after direction matching to obtain the space conversion relationship.

[0202] In some embodiments, the model registration module 20 is configured to:

[0203] Acquire first point cloud data corresponding to the first digital model, and second point cloud data corresponding to the second digital model;

[0204] Performing spatial flipping on the principal axis of the first point cloud data to obtain candidate point cloud data in multiple principal axis directions;

[0205] Performing point cloud registration based on principal component analysis on each candidate point cloud data and the second point cloud data respectively, to obtain a similarity and a transformation matrix corresponding to each candidate point cloud data, wherein the similarity represents the similarity between the candidate point cloud data and the second point cloud data;

[0206] The candidate point cloud data with the greatest similarity is determined as the target candidate point cloud data, and the transformation matrix corresponding to the target candidate point cloud data is determined as the first transformation matrix.

[0207] In some embodiments, the model registration module 20 is configured to:

[0208] Processing the first point cloud data to obtain a first eigenvector of the centroid of the first point cloud data, wherein a direction of the first eigenvector represents a principal axis direction of the first point cloud data;

[0209] The first point cloud data is flipped based on the main axis direction represented by the first eigenvector to obtain candidate point cloud data in multiple main axis directions.

[0210] In some embodiments, the model registration module 20 is configured to:

[0211] Performing point cloud registration on each candidate point cloud data and the second point cloud data respectively, and calculating an average distance between each candidate point cloud data and the second point cloud data, wherein the similarity includes the average distance;

[0212] The step of determining the candidate point cloud data with the greatest similarity as the target candidate point cloud data includes:

[0213] The candidate point cloud data with the smallest average distance is determined as the target candidate point cloud data.

[0214] In some embodiments, the bite adjustment module 30 is configured to:

[0215] The first point cloud data is spatially transformed according to the first transformation matrix to obtain the target digital model.

[0216] In some embodiments, the model registration module 20 is configured to:

[0217] The target candidate point cloud data and the second point cloud data are subjected to point cloud registration based on sparse iterative nearest point to obtain the second transformation matrix.

[0218] In some embodiments, the bite adjustment module 30 is configured to:

[0219] Performing spatial position transformation on the first point cloud data according to the first transformation matrix to obtain third point cloud data;

[0220] The third point cloud data is spatially transformed according to the second transformation matrix to obtain the target digital model.

[0221] In some embodiments, the first digital model and the second digital model include any one of an upper jaw model, a lower jaw model, an upper and lower jaw model, and a tooth model of the user's oral cavity.

[0222] In some implementations, this specification provides an electronic device, including:

[0223] Processor; and

[0224] A memory stores computer instructions, wherein the computer instructions are used to enable a processor to execute the method described in any of the above embodiments.

[0225] In some embodiments, the present specification provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the method described in any of the above embodiments.

[0226] In some implementations, this specification provides a computer program product, which implements the method described in any of the above implementations when running.

[0227] Specifically, Fig.12 A schematic diagram of the structure of an electronic device 600 suitable for implementing the method of this specification is shown. Fig.12 The electronic device shown can realize the corresponding functions of the above-mentioned processor and storage medium.

[0228] like Fig.12 As shown, the electronic device 600 includes a processor 601, which can perform various appropriate actions and processes according to the program stored in the memory 602 or the program loaded from the storage part 608 into the memory 602. In the memory 602, various programs and data required for the operation of the electronic device 600 are also stored. The processor 601 and the memory 602 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0229] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage section 608 as needed.

[0230] In particular, according to an embodiment of the present specification, the above method process can be implemented as a computer software program. For example, an embodiment of the present specification includes a computer program product, which includes a computer program tangibly contained on a machine-readable medium, and the computer program includes a program code for executing the above method. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 609, and / or installed from a removable medium 611.

[0231] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the embodiments. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. The obvious changes or modifications derived therefrom are still within the scope of protection created by this specification.

Claims

1. A method for adjusting occlusion, characterized in that: include: Acquire a first digital model and a second digital model of the user's oral cavity, wherein the first digital model represents a digital model obtained by digitally processing the user's oral cavity, and the second digital model represents a digital model obtained by collecting oral data of the user's oral cavity in a occlusal state; Performing model registration on the first digital model and the second digital model to obtain a spatial conversion relationship for mapping the first digital model to the second digital model; The occlusal relationship of the first digital model is adjusted based on the spatial conversion relationship to obtain a target digital model of the user's oral cavity.

2. The method according to claim 1, characterized in that The first digital model and the second digital model are digital models of the tooth area of ​​the user's mouth, and the step of obtaining the first digital model and the second digital model of the user's mouth includes: Digitally process the user's oral cavity to obtain an oral cavity model, and segment the tooth region of the oral cavity model to obtain the first digital model; The user's oral cavity in the occlusal state is digitally processed to obtain an occlusal model, and the tooth area of ​​the occlusal model is segmented to obtain the second digital model.

3. The method according to claim 1, characterized in that The performing model registration on the first digital model and the second digital model to obtain a spatial conversion relationship for mapping the first digital model to the second digital model includes: Direction matching is performed on the main axis directions of the first digital model and the second digital model, and model registration is performed based on the first digital model and the second digital model after direction matching to obtain the space conversion relationship.

4. The method according to claim 3, characterized in that The spatial conversion relationship includes a first conversion matrix; performing direction matching on the main axis directions of the first digital model and the second digital model, and performing model registration on the first digital model and the second digital model after the direction matching to obtain the spatial conversion relationship includes: Acquire first point cloud data corresponding to the first digital model, and second point cloud data corresponding to the second digital model; Performing spatial flipping on the principal axis of the first point cloud data to obtain candidate point cloud data in multiple principal axis directions; Performing point cloud registration based on principal component analysis on each candidate point cloud data and the second point cloud data respectively, to obtain a similarity and a transformation matrix corresponding to each candidate point cloud data, wherein the similarity represents the similarity between the candidate point cloud data and the second point cloud data; The candidate point cloud data with the greatest similarity is determined as the target candidate point cloud data, and the transformation matrix corresponding to the target candidate point cloud data is determined as the first transformation matrix.

5. The method according to claim 4, characterized in that The spatially flipping the principal axis of the first point cloud data to obtain candidate point cloud data in multiple principal axis directions includes: Processing the first point cloud data to obtain a first eigenvector of the centroid of the first point cloud data, wherein a direction of the first eigenvector represents a principal axis direction of the first point cloud data; The first point cloud data is flipped based on the main axis direction represented by the first eigenvector to obtain candidate point cloud data in multiple main axis directions.

6. The method according to claim 4, characterized in that The performing point cloud registration based on principal component analysis on each candidate point cloud data and the second point cloud data to obtain the similarity corresponding to each candidate point cloud data includes: Performing point cloud registration on each candidate point cloud data and the second point cloud data respectively, and calculating an average distance between each candidate point cloud data and the second point cloud data, wherein the similarity includes the average distance; The step of determining the candidate point cloud data with the greatest similarity as the target candidate point cloud data includes: The candidate point cloud data with the smallest average distance is determined as the target candidate point cloud data.

7. The method according to any one of claims 4 to 6, characterized in that: The adjusting the occlusal state of the first digital model based on the spatial conversion relationship to obtain a target digital model of the user's oral cavity includes: The first point cloud data is spatially transformed according to the first transformation matrix to obtain the target digital model.

8. The method according to any one of claims 4 to 6, characterized in that: The spatial conversion relationship further includes a second conversion matrix, and the model registration of the first digital model and the second digital model to obtain the spatial conversion relationship for mapping the first digital model to the second digital model further includes: Performing point cloud registration based on sparse iterative closest point on the target candidate point cloud data and the second point cloud data to obtain the second transformation matrix; The adjusting the occlusal state of the first digital model based on the spatial conversion relationship to obtain a target digital model of the user's oral cavity includes: Performing spatial position transformation on the first point cloud data according to the first transformation matrix to obtain third point cloud data; The third point cloud data is spatially transformed according to the second transformation matrix to obtain the target digital model.

9. The method according to claim 1, characterized in that: The first digital model and the second digital model include any one of an upper jaw model, a lower jaw model, an upper and lower jaw model, and a tooth model of the user's oral cavity.

10. An occlusal adjustment device, characterized in that: include: A model acquisition module is configured to acquire a first digital model and a second digital model of the user's oral cavity, wherein the first digital model represents a digital model obtained by digitally processing the user's oral cavity, and the second digital model represents a digital model obtained by collecting oral data of the user's oral cavity in a occlusal state; A model registration module is configured to perform model registration on the first digital model and the second digital model to obtain a spatial conversion relationship for mapping the first digital model to the second digital model; The occlusion adjustment module is configured to adjust the occlusion relationship of the first digital model based on the space conversion relationship to obtain a target digital model of the user's oral cavity.

11. An electronic device, characterized in that: include: processor; and A memory storing computer instructions, wherein the computer instructions are used to enable a processor to execute the method according to any one of claims 1 to 9.

12. A storage medium, characterized in that: Computer instructions are stored, and the computer instructions are used to make a computer execute the method according to any one of claims 1 to 9.

13. A computer program product, characterized in that The computer program product implements the method according to any one of claims 1 to 9 when running.

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