CN115457196A - Occlusion adjustment method, device, equipment and medium - Google Patents
Occlusion adjustment method, device, equipment and medium Download PDFInfo
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- CN115457196A CN115457196A CN202210982001.5A CN202210982001A CN115457196A CN 115457196 A CN115457196 A CN 115457196A CN 202210982001 A CN202210982001 A CN 202210982001A CN 115457196 A CN115457196 A CN 115457196A
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
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Abstract
The disclosed embodiments relate to a bite adjustment method, apparatus, device, and medium, wherein the method includes: acquiring a first three-dimensional grid model and a second three-dimensional grid model corresponding to an upper jaw and a lower jaw of a target oral cavity; each vertex in the first three-dimensional mesh model and the second three-dimensional mesh model comprises mark information, a first tooth area and a second tooth area which correspond to the first three-dimensional mesh model and the second three-dimensional mesh model respectively are determined based on the mark information of each vertex, and occlusion adjustment is carried out based on the first tooth area and the second tooth area to obtain the occlusion relation of the first three-dimensional mesh model and the second three-dimensional mesh model. By adopting the technical scheme, in the occlusion adjustment process, the tooth areas corresponding to the upper jaw and the lower jaw are determined by the mark information of the vertexes in the three-dimensional mesh model corresponding to the upper jaw and the lower jaw to perform occlusion adjustment, so that the reliability of the occlusion adjustment result is improved, and the adjusted occlusion positions of the upper jaw and the lower jaw are further ensured to be more correct and true.
Description
Technical Field
The present disclosure relates to the field of intelligent oral treatment technologies, and in particular, to a method, an apparatus, a device, and a medium for occlusion adjustment.
Background
At present, when an intraoral scanner is used for oral cavity digital model taking, the correct biting position of the upper jaw and the lower jaw of a patient is generally obtained by scanning occlusion.
However, in practical applications, the upper and lower jaw occlusion positions are likely to be mistaken for bite holes, bites, or skewness due to the influence of deformation errors of the upper and lower jaw data or occlusion data, occlusion matching algorithm errors, and miscellaneous data such as soft tissues in the mouth.
Therefore, for the digital intraoral model and the occlusal position thereof obtained by the intraoral scanner, proper occlusal adjustment or optimization is often required to make the occlusal position of the upper and lower jaws more correct and true.
Disclosure of Invention
To solve the above technical problems, or at least partially solve the above technical problems, the present disclosure provides an occlusion adjustment method, apparatus, device, and medium.
The embodiment of the disclosure provides a bite adjustment method, which includes:
acquiring a first three-dimensional grid model and a second three-dimensional grid model corresponding to an upper jaw and a lower jaw of a target oral cavity; wherein each vertex in the first three-dimensional mesh model and the second three-dimensional mesh model comprises labeling information;
determining a first tooth area and a second tooth area respectively corresponding to the first three-dimensional mesh model and the second three-dimensional mesh model based on the marking information of each vertex;
and carrying out occlusion adjustment on the basis of the first tooth area and the second tooth area to obtain the occlusion relation of the first three-dimensional grid model and the second three-dimensional grid model.
The disclosed embodiment also provides an occlusion adjustment device, the device comprising:
the first acquisition module is used for acquiring a first three-dimensional grid model corresponding to the upper jaw of the target oral cavity; wherein each vertex in the first three-dimensional mesh model comprises label information;
the second acquisition module is used for acquiring a second three-dimensional grid model corresponding to the lower jaw of the target oral cavity; wherein each vertex in the second three-dimensional mesh model comprises label information;
a first determining module, configured to determine a first tooth region corresponding to the first three-dimensional mesh model based on the labeling information of each vertex;
a second determining module, configured to determine a second tooth region corresponding to the second three-dimensional mesh model based on the label information of each vertex;
and the adjusting module is used for carrying out occlusion adjustment on the basis of the first tooth area and the second tooth area to obtain the occlusion relation of the first three-dimensional grid model and the second three-dimensional grid model.
An embodiment of the present disclosure further provides an electronic device, which includes: a processor; a memory for storing the processor-executable instructions; the processor is used for reading the executable instructions from the memory and executing the instructions to realize the occlusion adjustment method provided by the embodiment of the disclosure.
The embodiment of the disclosure also provides a computer-readable storage medium, which stores a computer program for executing the bite adjustment method provided by the embodiment of the disclosure.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages: according to the occlusion adjustment scheme provided by the embodiment of the disclosure, a first three-dimensional grid model and a second three-dimensional grid model corresponding to an upper jaw and a lower jaw of a target oral cavity are obtained; each vertex in the first three-dimensional mesh model and the second three-dimensional mesh model comprises mark information, a first tooth area and a second tooth area which correspond to the first three-dimensional mesh model and the second three-dimensional mesh model respectively are determined based on the mark information of each vertex, and occlusion adjustment is carried out based on the first tooth area and the second tooth area to obtain the occlusion relation of the first three-dimensional mesh model and the second three-dimensional mesh model. By adopting the technical scheme, in the occlusion adjustment process, the tooth areas corresponding to the upper jaw and the lower jaw are determined by the mark information of the vertexes in the three-dimensional mesh model corresponding to the upper jaw and the lower jaw to perform occlusion adjustment, so that the reliability of the occlusion adjustment result is improved, and the adjusted occlusion positions of the upper jaw and the lower jaw are further ensured to be more correct and true.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and components are not necessarily drawn to scale.
Fig. 1 is a schematic flow chart of an occlusion adjustment method according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of another occlusion adjustment method provided in the embodiments of the present disclosure;
fig. 3 is a schematic structural diagram of an occlusion adjustment device according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more complete and thorough understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
In practical application, in the field of oral cavity restoration, the accuracy of the occlusion relation of the upper and lower jaw models is very important, and the occlusion relation can directly influence the comfort degree of a finally designed restoration body after being worn in the mouth of a patient and even cannot be normally used. If the occlusion relationship of the model is inconsistent with the actual occlusion relationship in the patient's mouth, the prosthesis may be too high or too low after being worn, which may affect the patient's normal chewing.
Generally, an intraoral scanner is used to directly obtain an occlusion relationship in an oral cavity, generally, after an maxilla model and a mandible model are obtained through scanning, the intraoral cavity is kept in a natural occlusion state, then, the intraoral scanner is used to scan partial areas of the maxilla and the mandible (also called as a full jaw) in the intraoral occlusion state, and when the full jaw is scanned, the maxilla and the mandible are aligned to a current occlusion state (or a coordinate system) based on partial overlapping relationship between the maxilla and the full jaw.
Therefore, the whole-jaw scan can be performed for 2 times (one sheet on the left side and the right side) or 3 times (one sheet on the left side, the middle side and the right side) like the above-mentioned whole-jaw scan, one sheet of whole-jaw data can be obtained by the whole-jaw scan every time, an occlusion constraint with the upper jaw and the lower jaw is formed at the same time, the multi-side occlusion constraint can be formed with the upper jaw and the lower jaw through the multi-side (sheet) whole-jaw data, and the matching optimization of the final occlusion relation of the upper jaw and the lower jaw is performed based on the constraints, so that a more real occlusion relation can be obtained.
Although the accuracy of the occlusion matching result can be improved through multi-side occlusion constraint, the occlusion result may still be inaccurate due to the model (deformation) error of the upper and lower jaw data, the (deformation) error of the full jaw data, and the influence of the miscellaneous data on the model, and the situations such as occlusion or serious occlusion may occur.
When occlusion errors occur, the doctor needs to optimally adjust the occlusion positional relationship of the upper and lower jaw models, and this operation is called occlusion adjustment. For example, manual occlusion adjustment is performed, and a doctor performs position fine adjustment on scanned and acquired upper and lower jaw tooth models (using some operation controls and buttons) through operations such as rotation and translation in three-dimensional digital design software according to experience and actual conditions in an oral cavity, so that the occlusion relationship of the upper and lower jaw tooth models is more accurate (close to the actual occlusion state in the oral cavity).
However, although a doctor can flexibly and freely adjust according to experience and actual occlusion relation in the oral cavity, the adjustment is often complicated and time-consuming, the doctor is required to be skilled in software operation, certain difficulty is caused in operation, and certain learning cost is brought to the doctor; in addition, it is difficult to obtain a correct occlusion positional relationship by manual operation if it is not an experienced doctor or a doctor who knows the actual occlusion relationship in the oral cavity.
The embodiment of the disclosure provides an occlusion adjustment method, which can set some constraint conditions (such as full jaw, bite penetration amount, occlusion point, moving direction, and the like), then perform optimization iteration by using an algorithm, and finally obtain an occlusion adjustment matrix (moving and rotating adjustment amount) of an upper jaw (or lower jaw) model, so that the adjusted occlusion relationship of the upper and lower jaw tooth models is more reasonable and accurate, and the occlusion adjustment becomes simple in operation by an automatic adjustment mode, thereby reducing the learning cost.
In addition, both the maxilla and mandible models are treated as a whole, i.e. without distinguishing between teeth, gums, other soft tissue in the mouth or miscellaneous data. Stretching or movement may occur during scanning because of gums, other soft tissue in the mouth, or miscellaneous data, which if left untreated, may be more adherent to the teeth or partially protruding than the teeth. Because the occlusion adjustment algorithm imposes the restriction of preventing the upper and lower jaw models from being bitten through, the distance between the upper and lower jaws can be increased by the algorithm when the occlusion adjustment algorithm is adhered to the teeth or part of the occlusion adjustment algorithm protrudes beyond the gum of the teeth, other soft tissues in the mouth or miscellaneous data, so that the occlusion adjustment algorithm fails because the occlusion adjustment algorithm cannot bite through the upper and lower jaw models but causes the occlusion phenomenon between the actually corresponding teeth of the upper and lower jaws.
Therefore, the occlusion adjustment method of the embodiment of the present disclosure identifies and segments information such as different teeth, gums, other soft tissues and miscellaneous data from the intraoral picture during scanning; and the information is carried into the three-dimensional grid model corresponding to the upper jaw and the lower jaw through a series of processing, so that data of different areas of the upper jaw and the lower jaw can be distinguished, and only the tooth area in the three-dimensional grid model is subjected to occlusion adjustment during occlusion adjustment, so that the reliability of the occlusion adjustment result is improved.
Fig. 1 is a schematic flowchart of a bite adjustment method provided in an embodiment of the present disclosure, where the method may be executed by a bite adjustment apparatus, where the apparatus may be implemented by software and/or hardware, and may be generally integrated in an electronic device. As shown in fig. 1, the method includes:
101, acquiring a first three-dimensional grid model and a second three-dimensional grid model corresponding to an upper jaw and a lower jaw of a target oral cavity; wherein each vertex in the first three-dimensional mesh model and the second three-dimensional mesh model comprises labeling information.
The target oral cavity can be any oral cavity of a user needing oral cavity detection, and the setting is selected according to an application scene. The first three-dimensional grid model refers to a three-dimensional network model generated by a three-dimensional point cloud data frame generated by scanning the upper jaw of a target oral cavity in real time by scanning equipment through a grid fusion algorithm; the second three-dimensional grid model refers to a three-dimensional network model generated by a scanning device through a grid fusion algorithm on a three-dimensional point cloud data frame generated by scanning the lower jaw of the target oral cavity in real time. Wherein, the vertex of each three-dimensional network model comprises mark information, and the mark information refers to information for uniquely identifying the vertex, such as mark information of teeth, gingiva, soft tissues and the like.
In some embodiments, when the upper jaw of the target oral cavity is scanned, a two-dimensional color image and a three-dimensional point cloud data frame of each scanning position are obtained, the two-dimensional color image is identified based on a preset artificial intelligence identification model, identification results corresponding to pixel points on the two-dimensional color image are obtained and identified, identification results corresponding to the pixel points on the two-dimensional color image are obtained, the identification results are mapped to three-dimensional data points of the three-dimensional point cloud data frame based on the same scanning position, marking information of the three-dimensional data points is obtained, and all the three-dimensional point cloud data frames are subjected to fusion processing to obtain the first three-dimensional grid model; wherein the label information for the vertices in the first three-dimensional mesh model is determined based on the label information for the three-dimensional data points.
In other embodiments, when the upper jaw of the target oral cavity is scanned, a three-dimensional point cloud data frame is obtained for each scanning position, all the three-dimensional point cloud data frames are subjected to fusion processing to obtain a first three-dimensional grid model, the first three-dimensional grid model is identified based on a preset artificial intelligence identification model, the identification result of each vertex in the first three-dimensional grid model is obtained and identified, and the marking information of each vertex in the first three-dimensional grid model is obtained.
The above two manners are merely examples of obtaining the first three-dimensional mesh model corresponding to the upper jaw of the target oral cavity, and the present disclosure does not specifically limit the manner of obtaining the first three-dimensional mesh model corresponding to the upper jaw of the target oral cavity.
It should be noted that, the manner of obtaining the second three-dimensional mesh model corresponding to the lower jaw of the target oral cavity is the same as the manner of obtaining the first three-dimensional mesh model corresponding to the upper jaw of the target oral cavity, which is specifically described in detail in the description of obtaining the first three-dimensional mesh model corresponding to the upper jaw of the target oral cavity, and is not described in detail here.
Specifically, in the process of scanning the upper jaw and the lower jaw of the target oral cavity, a first three-dimensional mesh model and a second three-dimensional mesh model corresponding to the upper jaw and the lower jaw can be obtained, and each vertex in each three-dimensional mesh model comprises mark information; the label information of each vertex can uniquely identify the information of the vertex.
And 102, determining a first tooth area and a second tooth area which respectively correspond to the first three-dimensional mesh model and the second three-dimensional mesh model based on the marking information of each vertex.
The marking information of each vertex can uniquely identify the information of the vertex, so that the marking information is determined to form a first tooth area for all the vertices of the tooth based on the marking information corresponding to each vertex in the first three-dimensional mesh model; similarly, the marking information is determined to form a second tooth area for all the vertexes of the tooth based on the marking information corresponding to each vertex in the second three-dimensional mesh model. Wherein the first tooth region refers to a region of the upper jaw that is only teeth and the second tooth region refers to a region of the lower jaw that is only teeth.
In some embodiments, the first tooth region corresponding to the first three-dimensional mesh model is determined based on the label information of each vertex, and in some embodiments, the analysis is performed based on the label information of each vertex in the first three-dimensional mesh model, a plurality of target vertices whose label information is a tooth identifier are obtained, and the first tooth region is determined based on the plurality of target vertices; in other embodiments, the region is divided based on the labeling information of each vertex in the first three-dimensional mesh model, and the region with the labeling information of the tooth is determined as the first tooth region.
The above two manners are merely examples of determining the first tooth region corresponding to the first three-dimensional mesh model based on the label information of each vertex, and the present disclosure does not specifically limit the manner of determining the first tooth region corresponding to the first three-dimensional mesh model based on the label information of each vertex.
It should be noted that, the same way of determining the first tooth region corresponding to the first three-dimensional mesh model based on the mark information of each vertex is as that of determining the second tooth region corresponding to the second three-dimensional mesh model based on the mark information of each vertex, and specifically, refer to the detailed description of determining the first tooth region corresponding to the first three-dimensional mesh model based on the mark information of each vertex, and the detailed description is not repeated here.
In the embodiment of the present disclosure, after the first three-dimensional mesh model and the second three-dimensional mesh model are obtained, the first tooth region and the second tooth region respectively corresponding to the first three-dimensional mesh model and the second three-dimensional mesh model may be determined based on the label information of each vertex in the first three-dimensional mesh model and the second three-dimensional mesh model.
103, carrying out occlusion adjustment based on the first tooth area and the second tooth area to obtain an occlusion relation between the first three-dimensional grid model and the second three-dimensional grid model.
The occlusion relation of the first three-dimensional grid model and the second three-dimensional grid model refers to the occlusion relation of the upper jaw and the lower jaw, and the occlusion relation can be determined through the occlusion matrix, namely the occlusion relation of the three-dimensional grid models of the upper jaw and the lower jaw is adjusted by adjusting the occlusion matrix (moving and rotating adjustment amount) of the upper jaw and the lower jaw. In order to ensure the accuracy of adjustment, constraints on the translation amount and the rotation amount of the occlusion matrix in each direction can be added during occlusion adjustment, for example, the constraints are that after the three-dimensional grid model is translated and rotated in each direction, the displacement weighted average of all points on the three-dimensional grid model is smaller than a preset threshold; the preset threshold may determine a specific value according to a specific application scenario.
In the embodiment of the present disclosure, there are many ways to obtain the occlusion relationship between the first three-dimensional mesh model and the second three-dimensional mesh model based on performing occlusion adjustment on the first tooth region and the second tooth region, and in some specific implementations, for example, for each point in the first tooth region, a point closest to the point is found in the second tooth region, and a distance between the two points is calculated as a penalty value, and if the distance is negative, it indicates that a bite-through point exists between the two points. Therefore, a plurality of penalty values are obtained by traversing all points in the first tooth area and the second tooth area, and the plurality of penalty values are weighted and averaged to obtain the penalty value of the whole upper and lower jaws in the current relative occlusion matrix. And continuously modifying the occlusion matrix to adjust the relative postures of the upper jaw and the lower jaw so as to minimize the punishment value of the corresponding occlusion matrix, thus obtaining the occlusion matrix, namely the target occlusion matrix, and determining the occlusion relation between the first three-dimensional grid model and the second three-dimensional grid model based on the target occlusion matrix.
According to the occlusion adjustment scheme provided by the embodiment of the disclosure, a first three-dimensional grid model and a second three-dimensional grid model corresponding to an upper jaw and a lower jaw of a target oral cavity are obtained; each vertex in the first three-dimensional mesh model and the second three-dimensional mesh model comprises mark information, a first tooth area and a second tooth area which correspond to the first three-dimensional mesh model and the second three-dimensional mesh model respectively are determined based on the mark information of each vertex, and occlusion adjustment is carried out based on the first tooth area and the second tooth area to obtain the occlusion relation of the first three-dimensional mesh model and the second three-dimensional mesh model. By adopting the technical scheme, in the occlusion adjustment process, other non-tooth areas are removed through the mark information of the vertexes in the three-dimensional mesh model corresponding to the upper jaw and the lower jaw, and the occlusion adjustment is only carried out according to the tooth areas corresponding to the upper jaw and the lower jaw, so that the interference of other non-tooth areas is reduced, the reliability of the occlusion adjustment result is improved, and the adjusted occlusion position of the upper jaw and the lower jaw is further ensured to be more correct and real.
Fig. 2 is a schematic flow chart of another occlusion adjustment method provided in the embodiment of the present disclosure, and the embodiment further optimizes the occlusion adjustment method based on the above embodiment.
As shown in fig. 2, the method includes:
step 201, when scanning the upper jaw/lower jaw of the target oral cavity, acquiring a two-dimensional color image and a three-dimensional point cloud data frame of each scanning position.
Step 202, identifying the two-dimensional color image based on a preset artificial intelligence identification model to obtain an identification result corresponding to a pixel point on the two-dimensional color image and identifying the identification result to obtain an identification result corresponding to the pixel point on the two-dimensional color image.
In the embodiment of the present disclosure, recognizing a two-dimensional color image based on a preset artificial intelligence recognition model, obtaining a recognition result and an identification corresponding to a pixel point on the two-dimensional color image, and obtaining an identification result corresponding to the pixel point on the two-dimensional color image, includes: the method comprises the steps of identifying a two-dimensional color image based on a preset artificial intelligence identification model to obtain a tooth area, a gum area and other areas, determining tooth identification of the tooth area, gum identification of the gum area and other identifications of the other areas, identifying pixel points of the tooth area based on the tooth identification, identifying pixel points of the gum area based on the gum identification and identifying pixel points of the other areas based on the other identifications to obtain an identification result.
Specifically, the identification and marking of artificial intelligence information in the target oral cavity, that is, the intraoral scanner synchronously acquires two-dimensional color images in the target oral cavity when scanning the target oral cavity, and the artificial intelligence identification model (marked and trained by a large amount of actual data), such as a deep learning model, is used for carrying out artificial intelligence identification on the two-dimensional color images, so that teeth, gums and miscellaneous data (soft tissues and others) can be respectively identified and marked on the pixel coordinates of each two-dimensional color image.
And 203, mapping the identification result to a three-dimensional data point of the three-dimensional point cloud data frame based on the same scanning position to obtain the marking information of the three-dimensional data point.
Specifically, the marking result mark on the two-dimensional color image is mapped into the three-dimensional point cloud data frame. When the intraoral scanner scans, three-dimensional reconstruction is carried out on a series of images collected at the same position to generate a three-dimensional point cloud data frame corresponding to the current scanning position. Based on the principle of three-dimensional reconstruction, a corresponding relation can be established between each three-dimensional point in the three-dimensional point cloud data frame and a pixel point on the two-dimensional color image corresponding to the position of the current frame, so that the identification result on the two-dimensional image can be mapped to the three-dimensional point cloud data frame based on the corresponding relation and stored in the marking information of each three-dimensional point.
Step 204, performing fusion processing on all three-dimensional point cloud data frames to obtain a first three-dimensional grid model/a second three-dimensional grid model; wherein the labeling information of the vertices in the first/second three-dimensional mesh model is determined based on the labeling information of the three-dimensional data points.
Specifically, after the intraoral scanner completes the scanning of an upper (or lower) jaw, a three-dimensional mesh model of the upper (or lower) jaw is generated by using all three-dimensional point cloud data frames generated by the scanning through a mesh fusion algorithm, and at this time, the marking information of each three-dimensional point in the three-dimensional point cloud data frames is fused to the vertex of the three-dimensional mesh model correspondingly (in the fusion process, it is possible that different three-dimensional points in a plurality of different three-dimensional point cloud data frames correspond to the same mesh vertex, and the marking information of the mesh vertex is determined by the type of the marking information with the highest proportion among the three-dimensional points) and is stored in the marking information of the vertex of the mesh model.
Step 205, analyzing based on the marking information of each vertex in the first three-dimensional mesh model/the second three-dimensional mesh model, obtaining a plurality of target vertices with the marking information being the tooth identification, and determining the first tooth region/the second tooth region based on the plurality of target vertices.
Step 206, determining a plurality of tooth pairs based on the first tooth area and the second tooth area, calculating based on the distance between each tooth pair to obtain a current distance, and adjusting the relative occlusion matrix between the first tooth area and the second tooth area to adjust the current distance to obtain an updated distance.
And step 207, acquiring a relative occlusion matrix when the updated distance meets a preset distance condition as a target occlusion matrix, and determining the occlusion relation of the first three-dimensional grid model and the second three-dimensional grid model based on the target occlusion matrix.
Specifically, the vertex in the three-dimensional mesh model of the upper and lower jaws has marking information, for example, when calculating the bite-through constraint of the upper and lower jaws, a non-tooth area on the lower upper jaw model is filtered firstly (namely, the tooth area is made to participate in the bite-through calculation); for example, when the matching (distance between corresponding points of the overlapping region) of the whole jaw and the maxilla and the mandible are restricted, a data region (not participating in matching degree calculation) is filtered, the weight of the gum region is reduced (because the deformation degree of the gum is relatively larger), and only the data region is filtered for occlusion adjustment, so that the processing efficiency is further improved. Thus, the influence of the miscellaneous data or gum deformation on the final occlusion relationship can be avoided or reduced.
Specifically, for example, in the case of detecting the upper and lower jaws biting through, a certain function value of the distance between the upper and lower jaws is calculated as a penalty value (an increase in the penalty value is caused by an increase in the positive distance or the negative distance, and a larger value represents a stronger penalty) to be weighted and averaged. For example, for each vertex in the maxilla model, the vertex closest to this point is found in the mandible model, the distance between the two points is calculated, and if the distance is negative, it indicates that there is a bite-through point between the two points. By traversing all vertices in the maxilla, a series of such penalty values are obtained (here it is more desirable to limit the occurrence of the bite point, the penalty function can be adjusted so that it penalizes more negative distances than positive distances). And performing weighted average on the penalty values to obtain the penalty value of the whole upper and lower jaws in the current relative occlusion matrix. The algorithm adjusts the relative posture between the upper jaw and the lower jaw by continuously modifying the occlusion matrix, so that the punishment value of the corresponding occlusion matrix is minimum, the obtained occlusion matrix is the target occlusion matrix, and the bite-through is avoided as much as possible on the premise of strictly limiting the bite-through.
According to the occlusion adjustment scheme provided by the embodiment of the disclosure, when the upper jaw/lower jaw of a target oral cavity is scanned, a two-dimensional color image and a three-dimensional point cloud data frame of each scanning position are obtained, the two-dimensional color image is identified based on a preset artificial intelligence identification model, an identification result corresponding to a pixel point on the two-dimensional color image is obtained and identified, an identification result corresponding to the pixel point on the two-dimensional color image is obtained, the identification result is mapped to a three-dimensional data point of the three-dimensional point cloud data frame based on the same scanning position, marking information of the three-dimensional data point is obtained, and fusion processing is performed on all the three-dimensional point cloud data frames to obtain a first three-dimensional grid model/a second three-dimensional grid model; the method comprises the steps of determining mark information of vertexes in a first three-dimensional mesh model/a second three-dimensional mesh model based on mark information of three-dimensional data points, analyzing based on the mark information of each vertex in the first three-dimensional mesh model/the second three-dimensional mesh model, obtaining the mark information as a plurality of target vertexes of tooth identification, determining a first tooth area/a second tooth area based on the plurality of target vertexes, determining a plurality of tooth pairs based on the first tooth area and the second tooth area, calculating based on the distance between each tooth pair to obtain a current distance, adjusting a relative occlusion matrix between the first tooth area and the second tooth area to adjust the current distance to obtain an updated distance, obtaining the relative occlusion matrix when the updated distance meets a preset distance condition as a target occlusion matrix, and determining the occlusion relation of the first three-dimensional mesh model and the second three-dimensional mesh model based on the target occlusion matrix. By adopting the technical scheme, the interference of the gum area is eliminated during the calculation of the occlusion adjustment, so that the adjustment result is more accurate than that of a common occlusion adjustment algorithm, namely, the teeth and the gum part in the model are distinguished, and the matching relation between teeth and teeth can be really obtained during the calculation of the occlusion matching without being interfered by the gum.
Fig. 3 is a schematic structural diagram of an occlusion adjustment apparatus provided in an embodiment of the present disclosure, which may be implemented by software and/or hardware, and may be generally integrated in an electronic device. As shown in fig. 3, the apparatus includes:
a first obtaining module 301, configured to obtain a first three-dimensional mesh model corresponding to an upper jaw of a target oral cavity; wherein each vertex in the first three-dimensional mesh model comprises label information;
a second obtaining module 302, configured to obtain a second three-dimensional mesh model corresponding to a lower jaw of the target oral cavity; wherein each vertex in the second three-dimensional mesh model comprises label information;
a first determining module 303, configured to determine a first tooth region corresponding to the first three-dimensional mesh model based on the label information of each vertex;
a second determining module 304, configured to determine a second tooth region corresponding to the second three-dimensional mesh model based on the label information of each vertex;
an adjusting module 305, configured to perform occlusion adjustment based on the first tooth region and the second tooth region, so as to obtain an occlusion relationship between the first three-dimensional mesh model and the second three-dimensional mesh model.
Optionally, the first obtaining module 301 includes:
the acquisition unit is used for acquiring a two-dimensional color image and a three-dimensional point cloud data frame of each scanning position when the upper jaw of the target oral cavity is scanned;
the identification unit is used for identifying the two-dimensional color image based on a preset artificial intelligence identification model to obtain an identification result corresponding to a pixel point on the two-dimensional color image and identify the identification result to obtain an identification result corresponding to the pixel point on the two-dimensional color image;
the mapping unit is used for mapping the identification result to a three-dimensional data point of the three-dimensional point cloud data frame based on the same scanning position to obtain the marking information of the three-dimensional data point;
the fusion processing unit is used for performing fusion processing on all the three-dimensional point cloud data frames to obtain the first three-dimensional grid model; wherein the label information for the vertices in the first three-dimensional mesh model is determined based on the label information for the three-dimensional data points.
Optionally, the determining label information of vertices in the first three-dimensional mesh model based on the label information of the three-dimensional data points includes:
when one three-dimensional data point corresponds to one vertex, taking the mark information of the three-dimensional data point as the mark information of the vertex;
and when the plurality of three-dimensional data points correspond to one vertex, analyzing the plurality of marking information corresponding to the plurality of three-dimensional data points, and acquiring the target marking information with the largest number of the same identification types in the plurality of marking information as the marking information of the vertex.
Optionally, the identification unit is specifically configured to:
the two-dimensional color image is identified based on a preset artificial intelligence identification model to obtain a tooth area, a gum area and other areas;
determining a tooth identification of the tooth region, a gum identification of the gum region, and other identifications of the other regions;
and identifying the pixel points of the tooth area based on the tooth identification, identifying the pixel points of the gum area based on the gum identification, and identifying the pixel points of other areas based on other identifications to obtain the identification result.
Optionally, the first obtaining module 301 is specifically configured to:
when the upper jaw of the target oral cavity is scanned, a three-dimensional point cloud data frame of each scanning position is obtained;
performing fusion processing on all the three-dimensional point cloud data frames to obtain the first three-dimensional grid model; and identifying the first three-dimensional grid model based on a preset artificial intelligence identification model to obtain an identification result and identification corresponding to a vertex on the first three-dimensional grid model, and obtaining marking information corresponding to the vertex on the first three-dimensional grid model.
Optionally, the first determining module 303 is specifically configured to:
analyzing based on the marking information of each vertex in the first three-dimensional mesh model, and acquiring a plurality of target vertices of which the marking information is tooth identification;
determining the first tooth region based on the plurality of target vertices.
Optionally, the adjusting module 305 is specifically configured to:
determining a plurality of tooth pairs based on the first and second tooth regions;
calculating based on the distance between each pair of teeth to obtain a current distance;
adjusting a relative occlusion matrix between the first dental area and the second dental area to adjust the current distance to obtain an updated distance;
and acquiring a relative occlusion matrix when the updated distance meets a preset distance condition as a target occlusion matrix, and determining the occlusion relation of the first three-dimensional grid model and the second three-dimensional grid model based on the target occlusion matrix.
The occlusion adjusting device provided by the embodiment of the disclosure can execute the occlusion adjusting method provided by any embodiment of the disclosure, and has corresponding functional modules and beneficial effects of the executing method.
Embodiments of the present disclosure also provide a computer program product, which includes a computer program/instruction, and when executed by a processor, the computer program/instruction implements the bite adjustment method provided in any embodiment of the present disclosure.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. Referring now specifically to fig. 4, a schematic diagram of an electronic device 400 suitable for use in implementing embodiments of the present disclosure is shown. The electronic device 400 in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle-mounted terminal (e.g., a car navigation terminal), and the like, and fixed terminals such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, electronic device 400 may include a processing device (e.g., central processing unit, graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 402 or a program loaded from a storage device 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate wirelessly or by wire with other devices to exchange data. While fig. 4 illustrates an electronic device 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 409, or from the storage device 408, or from the ROM 402. The computer program, when executed by the processing device 401, performs the above-described functions defined in the bite adjustment method of the embodiment of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (Hyper Text Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a first three-dimensional grid model and a second three-dimensional grid model corresponding to an upper jaw and a lower jaw of a target oral cavity; each vertex in the first three-dimensional mesh model and the second three-dimensional mesh model comprises mark information, a first tooth area and a second tooth area which correspond to the first three-dimensional mesh model and the second three-dimensional mesh model respectively are determined based on the mark information of each vertex, and occlusion adjustment is carried out based on the first tooth area and the second tooth area to obtain the occlusion relation of the first three-dimensional mesh model and the second three-dimensional mesh model.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In accordance with one or more embodiments of the present disclosure, there is provided an electronic device including:
a processor;
a memory for storing the processor-executable instructions;
the processor is used for reading the executable instructions from the memory and executing the instructions to realize the occlusion adjustment method provided by the disclosure.
According to one or more embodiments of the present disclosure, there is provided a computer-readable storage medium storing a computer program for executing the bite adjustment method according to any one of the embodiments provided in the present disclosure.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. A bite adjustment method, comprising:
acquiring a first three-dimensional grid model and a second three-dimensional grid model corresponding to an upper jaw and a lower jaw of a target oral cavity; wherein each vertex in the first three-dimensional mesh model and the second three-dimensional mesh model comprises labeling information;
determining a first tooth area and a second tooth area respectively corresponding to the first three-dimensional mesh model and the second three-dimensional mesh model based on the marking information of each vertex;
and carrying out occlusion adjustment on the basis of the first tooth area and the second tooth area to obtain the occlusion relation of the first three-dimensional grid model and the second three-dimensional grid model.
2. The bite adjustment method of claim 1, wherein obtaining a first three-dimensional mesh model corresponding to an upper jaw of the target oral cavity comprises:
when the upper jaw of the target oral cavity is scanned, a two-dimensional color image and a three-dimensional point cloud data frame of each scanning position are obtained;
identifying the two-dimensional color image based on a preset artificial intelligence identification model to obtain an identification result corresponding to a pixel point on the two-dimensional color image and identifying the identification result to obtain an identification result corresponding to the pixel point on the two-dimensional color image;
mapping the identification result to a three-dimensional data point of the three-dimensional point cloud data frame based on the same scanning position to obtain marking information of the three-dimensional data point;
performing fusion processing on all the three-dimensional point cloud data frames to obtain the first three-dimensional grid model; wherein the label information for the vertices in the first three-dimensional mesh model is determined based on the label information for the three-dimensional data points.
3. The bite adjustment method of claim 2, wherein said determining labeling information for vertices in the first three-dimensional mesh model based on the labeling information for the three-dimensional data points comprises:
when one three-dimensional data point corresponds to one vertex, taking the mark information of the three-dimensional data point as the mark information of the vertex;
and when the plurality of three-dimensional data points correspond to one vertex, analyzing the plurality of marking information corresponding to the plurality of three-dimensional data points, and acquiring the target marking information with the largest number of the same identification types in the plurality of marking information as the marking information of the vertex.
4. The occlusion adjustment method according to claim 2, wherein the identifying the two-dimensional color image based on a preset artificial intelligence identification model to obtain an identification result and an identification corresponding to a pixel point on the two-dimensional color image to obtain an identification result corresponding to a pixel point on the two-dimensional color image comprises:
the two-dimensional color image is identified based on a preset artificial intelligence identification model to obtain a tooth area, a gum area and other areas;
determining a tooth identification of the tooth region, a gum identification of the gum region, and other identifications of the other regions;
identifying the pixel points of the tooth area based on the tooth identification, identifying the pixel points of the gum area based on the gum identification, and identifying the pixel points of other areas based on other identifications to obtain the identification result.
5. The bite adjustment method of claim 1, wherein said obtaining a first three-dimensional mesh model corresponding to an upper jaw of the target oral cavity comprises:
when the upper jaw of the target oral cavity is scanned, a three-dimensional point cloud data frame of each scanning position is obtained;
performing fusion processing on all the three-dimensional point cloud data frames to obtain the first three-dimensional grid model; and identifying the first three-dimensional mesh model based on a preset artificial intelligence identification model to obtain an identification result and identification corresponding to a vertex on the first three-dimensional mesh model, and obtaining marking information corresponding to the vertex on the first three-dimensional mesh model.
6. The bite adjustment method of claim 1, wherein said determining a first dental region corresponding to the first three-dimensional mesh model based on the labeling information of each vertex comprises:
analyzing based on the marking information of each vertex in the first three-dimensional mesh model, and acquiring a plurality of target vertices of which the marking information is tooth identification;
determining the first tooth region based on the plurality of target vertices.
7. The bite adjustment method according to any one of claims 1 to 6, wherein the performing the bite adjustment based on the first tooth region and the second tooth region to obtain the bite relationship of the first three-dimensional mesh model and the second three-dimensional mesh model includes:
determining a plurality of tooth pairs based on the first and second tooth regions;
calculating based on the distance between each pair of teeth to obtain a current distance;
adjusting a relative occlusion matrix between the first dental area and the second dental area to adjust the current distance to obtain an updated distance;
and acquiring a relative occlusion matrix when the updated distance meets a preset distance condition as a target occlusion matrix, and determining the occlusion relation between the first three-dimensional grid model and the second three-dimensional grid model based on the target occlusion matrix.
8. An occlusion adjustment device, comprising:
the first acquisition module is used for acquiring a first three-dimensional grid model corresponding to the upper jaw of the target oral cavity; wherein each vertex in the first three-dimensional mesh model comprises label information;
the second acquisition module is used for acquiring a second three-dimensional grid model corresponding to the lower jaw of the target oral cavity; wherein each vertex in the second three-dimensional mesh model comprises label information;
a first determining module, configured to determine a first tooth region corresponding to the first three-dimensional mesh model based on the label information of each vertex;
a second determining module, configured to determine a second tooth region corresponding to the second three-dimensional mesh model based on the labeling information of each vertex;
and the adjusting module is used for carrying out occlusion adjustment on the basis of the first tooth area and the second tooth area to obtain the occlusion relation of the first three-dimensional grid model and the second three-dimensional grid model.
9. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the occlusion adjustment method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the storage medium stores a computer program for executing the bite adjustment method of any one of the preceding claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116492082A (en) * | 2023-06-21 | 2023-07-28 | 先临三维科技股份有限公司 | Data processing method, device, equipment and medium based on three-dimensional model |
| CN116671956A (en) * | 2023-07-17 | 2023-09-01 | 广州医思信息科技有限公司 | Oral cavity data acquisition method based on comparison model |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116492082A (en) * | 2023-06-21 | 2023-07-28 | 先临三维科技股份有限公司 | Data processing method, device, equipment and medium based on three-dimensional model |
| CN116492082B (en) * | 2023-06-21 | 2023-09-26 | 先临三维科技股份有限公司 | Data processing method, device, equipment and medium based on three-dimensional model |
| CN116671956A (en) * | 2023-07-17 | 2023-09-01 | 广州医思信息科技有限公司 | Oral cavity data acquisition method based on comparison model |
| CN116671956B (en) * | 2023-07-17 | 2023-10-03 | 广州医思信息科技有限公司 | Oral cavity data acquisition method based on comparison model |
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