A digital model comparison method, apparatus, device and medium

Abstract

Embodiments of the present disclosure relate to a digital model comparison method, device, equipment and medium, wherein the method comprises: obtaining a pair of digital models to be compared, in response to a digital model comparison request, obtaining difference amount information between the digital models in the pair of digital models to be compared, processing the pair of digital models to be compared based on the difference amount information, generating indication information and displaying. By using the above technical solution, when the digital model comparison is performed, the indication information is displayed according to the difference amount information between the digital models, the difference changes of each region of the digital models can be more intuitively viewed, and the use requirements of users are met.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of data processing, and particularly relates to a digital model comparison method and device, equipment and medium. BACKGROUND

[0002] There are many scenarios in various industries that need to compare two or more similar observation targets to determine the differences. For example, in the oral medical industry, doctors need to observe the changes of the teeth of an orthodontic patient at different stages of orthodontic treatment, that is, to determine the differences between the current stage of the teeth of the orthodontic patient and the previous stage of the teeth. For another example, in the manufacturing industry, technicians need to observe the differences between the workpieces processed by them and the standard parts. At present, most scenarios are measured by manual observation or traditional measuring tools such as measuring rulers, and the efficiency and accuracy are poor. SUMMARY

[0003] In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a digital model comparison method, device, equipment and medium.

[0004] The present disclosure provides a digital model comparison method, which comprises the following steps:

[0005] Obtaining a pair of digital models to be compared;

[0006] In response to a digital model comparison request, obtaining difference amount information between the digital models in the pair of digital models to be compared;

[0007] Processing the pair of digital models to be compared based on the difference amount information, generating and displaying indication information.

[0008] The present disclosure also provides a digital model comparison device, which comprises:

[0009] A first obtaining module is configured to obtain a pair of digital models to be compared;

[0010] A second obtaining module is configured to obtain, in response to a digital model comparison request, difference amount information between the digital models in the pair of digital models to be compared;

[0011] A processing and generating module is configured to process the pair of digital models to be compared based on the difference amount information, generate and display indication information.

[0012] The present disclosure also provides an electronic device, which comprises a processor, a memory for storing executable instructions of the processor, and the processor is configured to read the executable instructions from the memory and execute the instructions to implement the digital model comparison method provided by the present disclosure.

[0013] The embodiment of the present disclosure further provides a computer readable storage medium, which stores a computer program for executing the digital model comparison method provided by the embodiment of the present disclosure.

[0014] The technical solution provided by the embodiment of the present disclosure has the following advantages compared with the prior art: the digital model comparison solution provided by the embodiment of the present disclosure acquires a pair of digital models to be compared, acquires difference quantity information between the digital models in the pair of digital models to be compared in response to a digital model comparison request, processes the pair of digital models to be compared based on the difference quantity information, generates and displays indication information. By using the technical solution, when the digital model comparison is performed, the indication information is generated and displayed according to the difference quantity information between the digital models, the difference changes of each region of the digital models can be more intuitively viewed, and the use requirements of the user are met. BRIEF DESCRIPTION OF DRAWINGS

[0015] The above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent by describing in detail the embodiments thereof with reference to the attached drawings. Throughout the drawings, the same or similar reference numerals refer to the same or similar elements. It should be understood that the drawings are schematic, and the original and elements are not necessarily drawn according to the scale.

[0016] Figure 1 A flowchart of a digital model comparison method provided by the embodiment of the present disclosure is shown in FIG. 1;

[0017] Figure 2 A flowchart of another digital model comparison method provided by the embodiment of the present disclosure is shown in FIG. 2;

[0018] Figure 3 A schematic diagram of information display provided by the embodiment of the present disclosure is shown in FIG. 3;

[0019] Figure 4 A structural schematic diagram of a digital model comparison device provided by the embodiment of the present disclosure is shown in FIG. 4;

[0020] Figure 5 A structural schematic diagram of an electronic device provided by the embodiment of the present disclosure is shown in FIG. 5. DETAILED DESCRIPTION

[0021] Embodiments of the present disclosure will be described in more detail by making reference to the drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein, rather, these embodiments are provided to make the present disclosure more thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes, and are not intended to limit the protection scope of the present disclosure.

[0022] It should be understood that each of the steps recited in the method embodiments of the present disclosure can be performed in different orders and / or in parallel. In addition, the method embodiments can include additional steps and / or omit performing the steps shown. The scope of the present disclosure is not limited in this regard.

[0023] The term "comprises" and variations thereof used in the present disclosure are open-ended, that is, "comprising 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." Related definitions are given throughout the description below.

[0024] It should be noted that the "first", "second", and the like concepts mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0025] It should be noted that the modification of "one" or "multiple" in the present disclosure is illustrative and not limiting, and those skilled in the art should understand that unless the context clearly indicates otherwise, it should be understood as "one or more".

[0026] The names of the messages or information exchanged between the devices in the embodiments of the present disclosure are only for illustrative purposes, and are not used to limit the scope of the messages or information.

[0027] Figure 1 A flowchart of a digital model comparison method is provided for the embodiments of the present disclosure. The method can be performed by a digital model comparison device, which can be implemented by software and / or hardware and generally integrated in an electronic device. As shown in Figure 1 The method includes:

[0028] Step 101, obtaining a pair of digital models to be compared.

[0029] The pair of digital models to be compared refers to two sets of digital models that have been pre-registered and aligned, such as two dental arch models obtained at different time points of the same user that have been pre-registered and aligned. The dental arch model can be a maxillary model or a mandibular model or a bite model (i.e., a digital model in which the maxillary model and the mandibular model are in occlusion), and can be a whole model or a partial model. For example, the pair of dental arch models includes a first maxillary model and a second maxillary model that are registered and aligned, the first maxillary model being obtained from the patient at a previous time, and the second maxillary model being obtained from the patient at a current time. For another example, it can be a pair of machining models and standard models corresponding to pre-registered and aligned production workpieces.

[0030] It should be noted that the pair of digital models to be compared can be any two three-dimensional digital models, preferably applied to the following scenarios: three-dimensional digital models obtained by scanning the same measured object at different times to monitor the change of the measured object over a period of time, or three-dimensional digital models obtained by scanning two similar measured objects respectively to compare the differences between the two similar measured objects.

[0031] The digital model is obtained by scanning with a three-dimensional scanner. The dental model is preferably obtained by scanning with an intraoral three-dimensional scanner, or can be obtained by scanning with an impression three-dimensional scanning method.

[0032] In the embodiments of the present disclosure, two groups of digital models to be compared can be pre-selected according to the application scenario to be aligned to obtain a pair of digital models to be compared. In a specific embodiment, a first digital model of a user at a first time point is obtained, and a second digital model of the user at a second time point is obtained. The second time point is different from the first time point. The first digital model and the second digital model are aligned based on a preset alignment algorithm to obtain a pair of digital models to be compared.

[0033] For example, the digital model is a dental model. For each group of dental models, first, the region of each tooth is identified by artificial intelligence, and then the dental model is aligned according to the tooth identification information. After the alignment of each group of dental models is completed, for example, the ICP (Iterative Closest Point, nearest point iterative algorithm) model registration method is used to align and adjust the two groups of dental models to obtain a pair of aligned dental models to be compared. The target region and the interference region are identified by artificial intelligence, and the two digital models are aligned through the target region to exclude the influence of the interference region on the alignment accuracy of the two digital models.

[0034] In the embodiments of the present disclosure, there are many ways to obtain a pair of digital models to be compared, such as displaying one digital model, loading another digital model after triggering the comparison operation, and taking the two digital models as a pair of digital models to be compared. For example, two digital models are loaded as a pair of digital models to be compared and displayed at the same time after triggering the comparison operation.

[0035] Step 102, in response to a digital model comparison request, obtaining difference information between digital models in a pair of digital models to be compared.

[0036] The difference information refers to the positional difference between an object in one digital model and an object in another digital model in the pair of digital models to be compared. Different digital models result in different difference information. When the digital model is a dental model, the difference information refers to the positional difference between a tooth in one dental model and a tooth in another dental model in the pair of dental models to be compared. It can also be understood as the amount of protrusion or concavity of a tooth in one dental model relative to a tooth in another dental model.

[0037] In this embodiment of the disclosure, upon receiving a digital model comparison request, a response is made to obtain the difference information between the digital models in the digital model pair to be compared. There are many ways to obtain the difference information between the digital models in the digital model pair to be compared. In some implementations, an arbitrary comparison point on one of the digital models in the digital model pair to be compared is obtained, and the relative position of the arbitrary comparison point to the surface of the other digital model is determined as the difference information. Here, the relative position of any comparison point on one digital model to the surface of another digital model refers to the amount of convexity or concavity of the comparison point to the surface of the other model.

[0038] In other embodiments, a first data structure and a second data structure corresponding to the first digital model and the second digital model in the digital model pair to be compared are obtained respectively. For each first data point in the first data structure, a search is performed in the second data structure to obtain a candidate set of second data points. Based on each first data point and each second data point in the candidate set of second data points, a target distance value corresponding to each first data point is obtained. The target second data point corresponding to the calculated target distance value is obtained. The target direction of the target distance value is determined based on the normal vector of the plane to which the first data point belongs and the normal vector of the plane to which the target second data point belongs. The target distance value corresponding to each first data point and the target direction of the target distance value are used as difference information.

[0039] It should be noted that the first and second digital models can be dental models of the same user at different points in time, or dental models of different users. For dental patients, comparing dental digital models at different times allows for monitoring of tooth changes, for example, enabling orthodontic monitoring during orthodontic treatment.

[0040] The above two methods are merely examples of obtaining information on the differences between digital models in a pair of digital models to be compared. This disclosure does not limit the specific implementation of obtaining information on the differences between digital models in a pair of digital models to be compared.

[0041] Step 103: Process the digital model pair to be compared based on the difference information, generate indication information and display it.

[0042] The indication information is used to indicate the difference between any comparison point on one digital model and the corresponding point on another digital model, and can be generated according to the application scenario, such as indicating color, indicating shape, etc.

[0043] In the embodiments of the present disclosure, there are many ways to generate and display the indication information based on the difference information. In some embodiments, the target distance value and the target direction between each point of the digital models in the pair of digital models to be compared are obtained based on the difference information, the association between the difference information and the color of the digital models is obtained, the target color of each point is determined based on the target direction, the target distance value and the association, and each point of the digital models in the pair of digital models to be compared is rendered based on the target color to obtain and display the indication information.

[0044] The indication information can be selected and set according to the application needs. For example, the target direction is distinguished by color 1 and color 2, the color depth of color 1 and color 2 is adjusted according to the specific data of the target distance value, and the target direction and the target distance value of each point of the digital models, such as -2mm, can also be displayed. Thus, the model comparison needs of different scenes are met.

[0045] In other embodiments, the target distance value and the target direction between each point of the digital models in the pair of digital models to be compared are obtained based on the difference information, the association between the difference information and the shape of the digital models is obtained, the target shape of each point is determined based on the target direction, the target distance value and the association, and each point of the digital models in the pair of digital models to be compared is rendered based on the target shape to obtain and display the indication information.

[0046] The above two ways are only examples of processing the pair of digital models to be compared based on the difference information, generating and displaying the indication information. The embodiments of the present disclosure do not limit the specific implementation of processing the pair of digital models to be compared based on the difference information, generating and displaying the indication information.

[0047] The digital model comparison scheme provided by the embodiments of the present disclosure responds to a digital model comparison request. The digital model comparison request includes a pair of digital models to be compared, the difference information between the digital models in the pair of digital models to be compared is obtained, the pair of digital models to be compared is processed based on the difference information, and the indication information is generated and displayed. By using the above technical scheme, when the digital models are compared, the indication information is displayed according to the difference information between the digital models, the difference changes of each region of the digital models can be more intuitively viewed, and the use needs of users are met.

[0048] Figure 2This is a flowchart illustrating another digital model comparison method provided in this embodiment of the present disclosure. This embodiment further optimizes the above-described digital model comparison method based on the previous embodiment. Figure 2 As shown, taking a digital model as a dental model as an example, the method includes:

[0049] Step 201: Obtain the first dental model to be processed, and perform artificial intelligence recognition on the first dental model to be processed to obtain each first tooth, and process the first dental model based on each first tooth to obtain the first dental model.

[0050] Step 202: Obtain the second dental model to be processed, and perform artificial intelligence recognition on the second dental model to be processed to obtain each second tooth. Then, process the second dental model based on each second tooth to obtain the second dental model.

[0051] Step 203: Obtain the registration matrix between the first and second dental models. Based on the registration matrix, register the second dental model to the first dental model to obtain the dental model pair to be compared.

[0052] The first and second dental models can be two sets of dental models obtained by the same user at different time points. By pre-registration and alignment, a pair of dental models to be compared in this embodiment of the present disclosure can be obtained.

[0053] It is understandable that when comparing two sets of dental data for the same user, the two sets of dental models are first aligned. If the position, shape, or other information of the user's teeth changes during this period, this change can be reflected in the differences between the two sets of dental models.

[0054] Specifically, for each set of dental models, the alignment method first uses artificial intelligence to identify the region of each tooth, and then aligns the dental models according to the tooth identification information. For example, the alignment position is such that the upper jaw model is above and the lower jaw model is below, with the dental models facing the screen window. The coordinate center can be taken as the geometric center of the tooth region of the model.

[0055] Furthermore, after aligning each set of dental models, the ICP (Iterative Closest Point) model registration method is used to automatically fine-tune the alignment of the two sets of dental models, resulting in a pair of dental models to be compared. During ICP registration, the upper and lower jaws of each set of dental models are treated as a single registration object.

[0056] Step 204: Obtain the dental model pair to be compared.

[0057] It should be noted that step 204 is the same as step 101, and will not be described in detail here. Please refer to the detailed description of step 101 for details.

[0058] Step 205, in response to the dental model comparison request, obtaining first data structure and second data structure corresponding to the first dental model and the second dental model respectively in the dental model comparison, searching for each first data point in the first data structure in the second data structure to obtain a second data point candidate set.

[0059] Step 206, based on each first data point and each second data point in the second data point candidate set, calculating the target distance value corresponding to each first data point, and obtaining the target second data point corresponding to the calculated target distance value.

[0060] Step 207, determining the target direction of the target distance value based on the normal vector of the plane to which the first data point belongs and the normal vector of the plane to which the target second data point belongs, and taking the target distance value corresponding to each first data point and the target direction of the target distance value as the difference quantity information.

[0061] Wherein, the first data structure or the second data structure can be a kd-tree (k-dimensional tree) data structure, a tree-shaped data structure for storing instance points in k-dimensional space for fast retrieval. Specifically, first, construct the corresponding kd-tree data structure for each point set on the two models to be compared, i.e. the first dental model and the second dental model.

[0062] Further, searching for each first data point in the first data structure in the second data structure obtains a second data point candidate set, i.e. obtaining the second data point candidate set corresponding to each first data point, i.e. the candidate nearest second data point set, there is a second data point in the subsequent second data point set that is the nearest to the first data point. Through the foregoing manner, it can be avoided to calculate the first data point with each second data point, further improving the calculation efficiency, thereby improving the difference quantity acquisition efficiency.

[0063] Further, based on each first data point and each second data point in the second data point candidate set, calculating the distance value corresponding to each first data point, taking the minimum distance value as the target distance value, and obtaining the target second data point corresponding to the calculated target distance value as the nearest second data point, thereby obtaining the target direction between the first data point and the target second data point, and taking the target distance value and the target direction as the difference quantity information between the two data points.

[0064] In the embodiments of the present disclosure, there are many ways to determine the target direction of the target distance value based on the normal vector of the plane to which the first data point belongs and the normal vector of the plane to which the target second data point belongs. In some embodiments, the average normal vector of the plane to which the first data point belongs and the average normal vector of the plane to which the target second data point belongs are obtained, and the target direction is calculated based on the two average normal vectors.

[0065] In other embodiments, a plurality of first planes to which the first data point belongs are obtained, and a first normal vector and a first weight of each first plane are obtained. The first target normal vector of the first data point is calculated based on the plurality of first normal vectors and the first weight. A plurality of second planes to which the second data point belongs are obtained, and a second normal vector and a second weight of each second plane are obtained. The second target normal vector of the second data point is calculated based on the plurality of second normal vectors and the second weight. The target direction is calculated based on the first target normal vector and the second target normal vector.

[0066] The above two ways are only examples of determining the target direction of the target distance value based on the normal vector of the plane to which the first data point belongs and the normal vector of the plane to which the target second data point belongs. The embodiments of the present disclosure do not specifically limit the implementation of the target direction of the target distance value based on the normal vector of the plane to which the first data point belongs and the normal vector of the plane to which the target second data point belongs.

[0067] As an example of a scenario, the dental model to be compared, such as model a and model b, is compared. The nearest point of each point on model a to model b is calculated, such as Figure 3 As shown in the model a, the nearest point of C point on model a is assumed to be D point. Based on the calculated nearest point D point, the triangular facets of the first-order neighborhood of the nearest point topology (such as triangular mesh topology) are found. The nearest distance (target distance value) of C point to these triangular facets is calculated, which is assumed to be E point, and the target distance value is d. Based on the normal relationship of each point of models a and b (which can be weighted and averaged according to the normal vector of the first-order neighborhood of the point), the positive and negative of the target distance value are defined, such as Figure 3 As shown in the X normal vector direction, the direction of the target distance value is opposite to the direction of the line, which is defined as positive, that is, the target distance value d calculated by C point is positive.

[0068] Therefore, the nearest distance of each point of models a and b is mapped to a color value, and the model vertex is rendered according to the color value. Thus, when the two sets of models are displayed, the difference information can be more intuitively viewed by indicating the difference information through color.

[0069] In step 208, the target distance value and the target direction between each point of the dental model to be compared are obtained based on the difference information, and the association between the difference information and the color of the dental model to be compared is obtained.

[0070] Step 209, each point determines the target color based on the target direction, the target distance value and the association relationship, and the target color is used to render each point of the compared dental model to the centering dental model to obtain and display the indication information.

[0071] The association relationship can be pre-set, and different difference amount information is associated with different colors, so that the target color is determined based on the target direction, the target distance value and the association relationship, for example, +1mm corresponds to a color A, and -0.5mm corresponds to another color B, wherein the change trend of the color can be proportional to the change trend of the distance value, for example, all the target directions are positive, and all the target directions are color A, and the color A becomes deeper as the target distance value becomes larger, and for example, all the target directions are negative, and all the target directions are color B, and the color B becomes deeper as the target distance value becomes larger, so that the difference amount information of each point between the models can be directly observed.

[0072] It can also be understood that the limit value of the color corresponds to the limit of the target distance value, for example, the maximum distance value of the target distance value is 5, and the color corresponding to the maximum distance value of 5 is the deepest.

[0073] It can be understood that after step 209, step 210 and / or step 211 and / or step 212 and / or step 213 and / or step 214 can be executed.

[0074] Step 210, in response to the difference amount information display request of the compared dental model pair, a target comparison point is determined, the difference amount value corresponding to the target comparison point is obtained and displayed.

[0075] In the embodiments of the present disclosure, the difference amount value between the comparison points can also be displayed, for example, a user selects a certain point on the model as a target comparison point through mouse, keyboard and other operations, and the difference amount value corresponding to the target comparison point is displayed, for example, 0.5mm. It can also be understood that the user can cancel the displayed difference amount value by moving the mouse and other operations.

[0076] Therefore, the user can directly see the difference amount data of any point on the model, and can quickly and more intuitively view the difference change of the model, further meeting the use demand.

[0077] Step 211, in response to a target model transparency adjustment request, a target transparency parameter of the target model is obtained, the transparency parameter of the target model is adjusted, and the target model is displayed based on the target transparency parameter.

[0078] In the embodiments of the present disclosure, the target model can be any one of the dental arch models to be compared. The target model can be hidden directly, so that the user can view another dental arch model, understand the overall indication information of the another dental arch model, and understand the specific amount of external convexity or internal concavity, thereby meeting the viewing requirements of more scenarios. The target transparency parameter is set according to the application scenario.

[0079] In step 212, in response to the target model hiding request, the target model is hidden based on the hiding request and is not displayed.

[0080] In the embodiments of the present disclosure, the target model can be any one of the dental arch models to be compared. The target model can be hidden directly, so that the user can view another dental arch model, understand the overall indication information of the another dental arch model, and understand the specific amount of external convexity or internal concavity, thereby meeting the viewing requirements of more scenarios. The target transparency parameter is set according to the application scenario.

[0081] In step 213, in response to the target model color adjustment request, the target color parameter corresponding to the target model is obtained, the current color parameter of the target model is updated to the target color parameter, and the target model is rendered and displayed based on the target color parameter.

[0082] In the embodiments of the present disclosure, the target model can be any one of the dental arch models to be compared. The target model color parameter can be adjusted according to the use requirements of different users, for example, +1 mm corresponds to a color A1 and -0.5 mm corresponds to another color B1, and then +1 mm corresponds to a color A2 and -0.5 mm corresponds to another color B2, thereby further meeting the use requirements.

[0083] It can also be understood that the limit value of the color corresponds to the limit of the target distance value, and the one-to-one correspondence between the target distance value and the color can be adjusted by adjusting the limit value of the color.

[0084] In step 214, in response to the target model color adjustment request, the association between the difference amount information of the dental arch models and the color is adjusted, the target model updates the color based on the adjusted association and is rendered and displayed.

[0085] In the embodiments of the present disclosure, the target model can be any one of the dental arch models to be compared. The association can be adjusted according to the use requirements of different users, for example, +1 mm corresponds to a color A and -0.5 mm corresponds to another color B, and then +1 mm corresponds to a color B and -0.5 mm corresponds to another color A, thereby further meeting the use requirements.

[0086] The dental model comparison scheme provided by the embodiments of the present disclosure comprises the following steps: obtaining a first dental model to be processed, and performing artificial intelligence identification on the first dental model to be processed to obtain a first dental model; obtaining a second dental model to be processed, and performing artificial intelligence identification on the second dental model to be processed to obtain a second dental model; obtaining a registration matrix between the first dental model and the second dental model, and registering the second dental model to the first dental model based on the registration matrix to obtain a dental model pair to be compared; obtaining the dental model pair to be compared, and in response to a dental model comparison request, obtaining a first data structure and a second data structure corresponding to the first dental model and the second dental model in the dental model pair to be compared respectively; searching for a second data point candidate set in the second data structure for each first data point in the first data structure; calculating a target distance value corresponding to each first data point based on each first data point and each second data point in the second data point candidate set, and obtaining a target second data point corresponding to the target distance value; determining a target direction of the target distance value based on a normal vector of a plane to which the first data point belongs and a normal vector of a plane to which the target second data point belongs; taking the target distance value corresponding to each first data point and the target direction of the target distance value as difference quantity information; obtaining a target distance value and a target direction between each point of the dental model in the dental model pair to be compared based on the difference quantity information; obtaining an association relationship between the difference quantity information and the color between the dental models; determining a target color of each point based on the target direction, the target distance value, and the association relationship; rendering each point of the dental model in the dental model pair to be compared based on the target color to obtain and display indication information; in response to a difference quantity information display request for the compared dental model pair, determining a target comparison point, obtaining and displaying a difference quantity value corresponding to the target comparison point; in response to a target model transparency adjustment request, obtaining a target transparency parameter of the target model, adjusting the transparency parameter of the target model, and rendering and displaying the target model based on the target transparency parameter; in response to a target model hiding request, hiding and not displaying the target model based on the hiding request; in response to a target model color adjustment request, obtaining a target color parameter corresponding to the target model, updating a current color parameter of the target model to the target color parameter, and rendering and displaying the target model based on the target color parameter; and in response to the target model color adjustment request, adjusting the association relationship between the difference quantity information and the color between the dental models, and updating and rendering the color of the target model based on the adjusted association relationship. By using the above technical scheme, when two groups of models are compared, the functions of color indication and difference quantity display can be used to more intuitively view the difference changes of each region of the models and measure the difference quantities of different points of the models. In addition, the indication information can be adjusted according to requirements, further meeting the use requirements of users and improving the use experience of users.

[0087] Figure 4A structural schematic diagram of a digital model comparison device provided by an embodiment of the present disclosure is shown in FIG. 1. The device can be implemented by software and / or hardware, and can be integrated in an electronic device. As shown in FIG. 1, the device includes: Figure 4

[0088] A first obtaining module 301 is configured to obtain a pair of digital models to be compared.

[0089] A second obtaining module 302 is configured to, in response to a request for comparison of dental and jaw models, obtain difference amount information between the digital models in the pair of digital models to be compared.

[0090] A processing and generating module 303 is configured to process the pair of digital models to be compared based on the difference amount information, generate and display indication information.

[0091] Optionally, the device further includes:

[0092] A response determining module is configured to, in response to a request for display of the difference amount information of the pair of compared digital models, determine a target comparison point.

[0093] An obtaining and displaying module is configured to obtain and display a difference amount value corresponding to the target comparison point.

[0094] Optionally, the difference amount information is a position difference of an object in one of the pair of digital models to be compared relative to an object in the other digital model.

[0095] Optionally, the second obtaining module 302 is specifically configured to:

[0096] Obtain an arbitrary comparison point on one of the pair of digital models to be compared.

[0097] Determine a relative position of the arbitrary comparison point to a surface of the other digital model as the difference amount information.

[0098] Optionally, the second obtaining module 302 is specifically configured to:

[0099] Obtain a first data structure and a second data structure corresponding to a first digital model and a second digital model in the pair of digital models to be compared, respectively.

[0100] Search for each first data point in the first data structure in the second data structure to obtain a candidate set of second data points.

[0101] Based on each first data point and each second data point in the candidate set of second data points, calculate a target distance value corresponding to each first data point, and obtain a target second data point corresponding to the target distance value.

[0102] ​determine a target direction of the target distance value based on a normal vector of a plane to which the first data point belongs and a normal vector of a plane to which the target second data point belongs;

[0103] use the target distance value corresponding to each of the first data points and the target direction of the target distance value as the difference amount information.

[0104] Optionally, the determining of the target direction of the target distance value based on the normal vector of the plane to which the first data point belongs and the normal vector of the plane to which the target second data point belongs comprises:

[0105] obtaining a plurality of first planes to which the first data point belongs, and obtaining a first normal vector and a first weight of each of the first planes;

[0106] performing calculation based on the plurality of first normal vectors and the first weights to obtain a first target normal vector of the first data point;

[0107] obtaining a plurality of second planes to which the second data point belongs, and obtaining a second normal vector and a second weight of each of the second planes;

[0108] performing calculation based on the plurality of second normal vectors and the second weights to obtain a second target normal vector of the second data point;

[0109] performing calculation based on the first target normal vector and the second target normal vector to obtain the target direction.

[0110] Optionally, the apparatus further comprises:

[0111] a third obtaining module configured to obtain a first to-be-processed digital model, perform artificial intelligence recognition on the first to-be-processed digital model to obtain each first tooth, and process the first digital model based on the each first tooth to obtain the first digital model;

[0112] a fourth obtaining module configured to obtain a second to-be-processed digital model, perform artificial intelligence recognition on the second to-be-processed digital model to obtain each second tooth, and process the second digital model based on the each second tooth to obtain the second digital model;

[0113] a fifth obtaining module configured to obtain a registration matrix between the first digital model and the second digital model;

[0114] a configuration module configured to register the second digital model to the first digital model based on the registration matrix to obtain the pair of to-be-compared digital models.

[0115] Optionally, the processing generation module 303 is specifically configured to:

[0116] acquire a target distance value and a target direction between each point of the digital model to be compared based on the difference amount information;

[0117] acquire an association between the difference amount information and the color between the digital models;

[0118] determine a target color for each point based on the target direction, the target distance value and the association;

[0119] render each point of the digital model to be compared based on the target color, obtain the indication information and display.

[0120] Optionally, the apparatus further comprises:

[0121] a second response module configured to respond to a target model transparency adjustment request;

[0122] a sixth acquisition module configured to acquire a target transparency parameter of the target model;

[0123] a first adjustment module configured to adjust the transparency parameter of the target model;

[0124] a first rendering and display module configured to render and display the target model based on the target transparency parameter.

[0125] Optionally, the apparatus further comprises:

[0126] a third response module configured to respond to a target model hiding request;

[0127] a hiding module configured to hide and not display the target model based on the hiding request.

[0128] Optionally, the apparatus further comprises:

[0129] a fourth response module configured to respond to a target model color adjustment request and acquire a target color parameter corresponding to the target model;

[0130] an updating module configured to update a current color parameter of the target model to the target color parameter;

[0131] a second rendering and display module configured to render and display the target model based on the target color parameter.

[0132] Optionally, the apparatus further comprises:

[0133] a fifth response module configured to respond to a target model color adjustment request;

[0134] a second adjustment module configured to adjust an association between difference amount information and a color between digital models.

[0135] a third rendering display module configured to update the color and perform rendering display of the target model based on the adjusted association relationship.

[0136] Optionally, the to-be-compared digital models comprise two digital models acquired at different time points based on a same user, and the two digital models are pre-registered and aligned.

[0137] The digital model comparison apparatus provided by the embodiments of the present disclosure can perform the digital model comparison method provided by any of the embodiments of the present disclosure, and has the corresponding function modules and beneficial effects of performing the method.

[0138] The embodiments of the present disclosure further provide a computer program product, which comprises computer programs / instructions, and the computer programs / instructions are executed by a processor to implement the digital model comparison method provided by any of the embodiments of the present disclosure.

[0139] Figure 5 A structural schematic diagram of an electronic device provided by the embodiments of the present disclosure is provided. The following specifically refers to Figure 5 which shows a structural schematic diagram of an electronic device 400 suitable for being used to implement the embodiments of the present disclosure. The electronic device 400 in the embodiments of the present disclosure can include but is not limited to a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet Personal Computer), a PMP (Portable Multimedia Player), a vehicle terminal (for example, a vehicle navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. Figure 5 The electronic device shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present disclosure.

[0140] As shown in Figure 5 , the electronic device 400 can include a processing apparatus (for example, a central processing unit, a graphic processing unit, and the like) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage apparatus 408 to a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The processing apparatus 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0141] In general, the following devices can be connected to the I / O interface 405: input devices 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, and the like; output devices 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, and the like; storage devices 408 including, for example, a magnetic tape, a hard disk, and the like; and communication devices 409. The communication devices 409 can allow the electronic device 400 to communicate wirelessly or wired with other devices to exchange data. Although Figure 5 The electronic device 400 is shown with various devices, but it is understood that all of the shown devices are not required to be implemented or present. More or less devices can alternatively be implemented or present.

[0142] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. 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 executing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication devices 409, or installed from the storage devices 408, or installed from the ROM 402. When the computer program is executed by the processing devices 401, the above-described functions defined in the digital model comparison method of embodiments of the present disclosure are performed.

[0143] It should be noted that the computer-readable medium described above can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the above. More specific examples of the computer-readable storage medium can 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 above. In the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program used by or in connection with an instruction execution system, apparatus or device. In the present disclosure, the computer-readable signal medium can include a data signal propagated in baseband or propagated as a carrier wave in a propagated data signal, in which the computer-readable program code is contained. Such a propagated data signal can take many forms, including but not limited to, an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium that can send, propagate or transfer the program for use by or in connection with the instruction execution system, apparatus or device. The program code contained in the computer-readable medium can be transmitted by any suitable medium, including but not limited to, wire, cable, RF (radio frequency), etc., or any suitable combination of the above.

[0144] In some embodiments, the client, server, or both can communicate using any current known or future developed network protocol, such as HTTP (Hyper Text Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), the Internet, and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any current known or future developed networks.

[0145] The computer-readable medium described above can be included in the electronic device described above; or can exist separately from the electronic device, and is not assembled into the electronic device.

[0146] The computer readable medium described above carries one or more programs, when the one or more programs are executed by the electronic device, cause the electronic device to: in response to a digital model comparison request; wherein the digital model comparison request includes a pair of digital models to be compared, obtain difference amount information between the digital models in the pair of digital models to be compared, process the pair of digital models to be compared based on the difference amount information, and generate and display indication information.

[0147] Computer program code for carrying out operations of the present disclosure can be written in any one or more programming languages, including object oriented programming languages such as Java, Smalltalk, C++ or conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can 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 latter scenario, the remote computer can 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 can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0148] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of present disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, a procedure, or a part of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in a different order than that noted in the figures. For example, two blocks noted in succession can in fact be executed substantially concurrently or in the opposite order, depending on the functionality involved. It should also be noted that each block in the block diagrams and / or flow diagrams, and combinations of blocks in the block diagrams and / or flow diagrams, can be implemented by dedicated hardware-based systems that perform the specified functions or operations, or can be implemented by a combination of dedicated hardware-based systems and computer instructions.

[0149] The units described in the embodiments of the present disclosure can be implemented by software, or by hardware. In some cases, the names of the units do not constitute a limitation on the units themselves.

[0150] The functionality described above in this document can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0151] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can 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 the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0152] According to one or more embodiments of the present disclosure, the present disclosure provides an electronic device, comprising:

[0153] a processor;

[0154] a memory for storing the processor-executable instructions;

[0155] the processor is configured to read the executable instructions from the memory and execute the instructions to implement any of the digital model comparison methods provided by the present disclosure.

[0156] According to one or more embodiments of the present disclosure, the present disclosure provides a computer-readable storage medium, which stores a computer program for executing any of the digital model comparison methods provided by the present disclosure.

[0157] The above description is merely illustrative of the exemplary embodiments of the present disclosure and the principles of techniques involved. It should be understood by those skilled in the art that the scope of the disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combinations of the above technical features or equivalent features without departing from the above disclosed concept. For example, the above technical features can be replaced with technical features disclosed in the present disclosure (but not limited to) having similar functions to form technical solutions.

[0158] Moreover, while operations are depicted in a particular order, this should not be understood as requiring such an order nor infringing on the scope of the disclosure. Certain of the operations described in the discussion are combinable into a single operation, and certain operations can be separated into several operations. In some embodiments, the operations described in the discussion can be performed in an order different than presented in the discussion. In some embodiments, the operations described in the discussion can be performed concurrently. Also, while several specific implementation details are discussed in the discussion, these should not be interpreted as limiting the scope of the disclosure. Rather, certain features described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination.

[0159] 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

1. A digital model comparison method, characterized by, The method comprises: obtaining a pair of digital models to be compared; in response to a digital model comparison request, obtaining difference amount information between the digital models in the pair of digital models to be compared; wherein the difference amount information is obtained by calculating between data points based on a data structure corresponding to the pair of digital models to be compared; wherein the data structure corresponding to the pair of digital models to be compared comprises a first data structure and a second data structure; for each first data point in the first data structure, searching in the second data structure to obtain a candidate set of second data points; based on each first data point and each second data point in the candidate set of second data points, calculating a target distance value corresponding to each first data point, and obtaining a target second data point corresponding to the target distance value; determining a target direction of the target distance value based on a normal vector of a plane to which the first data point belongs and a normal vector of a plane to which the target second data point belongs; and taking the target distance value corresponding to each first data point and the target direction of the target distance value as the difference amount information; wherein the determination of the target direction of the target distance value based on the normal vector of the plane to which the first data point belongs and the normal vector of the plane to which the target second data point belongs comprises: obtaining a plurality of first planes to which the first data point belongs, and obtaining a first normal vector and a first weight of each first plane; calculating based on a plurality of the first normal vectors and the first weights to obtain a first target normal vector of the first data point; obtaining a plurality of second planes to which the second data point belongs, and obtaining a second normal vector and a second weight of each second plane; calculating based on a plurality of the second normal vectors and the second weights to obtain a second target normal vector of the second data point; and calculating based on the first target normal vector and the second target normal vector to obtain the target direction; processing the pair of digital models to be compared based on the difference amount information, generating and displaying indication information; wherein the digital models to be compared are dental models to be compared.

2. The digital model comparison method of claim 1, wherein, Further comprising: in response to a difference amount information display request for the pair of digital models to be compared, determining a target comparison point; obtaining and displaying a difference amount value corresponding to the target comparison point.

3. The digital model comparison method of claim 1, wherein: the difference amount information is a positional difference of an object in one of the pair of digital models to be compared relative to an object in the other digital model.

4. The digital model comparison method of claim 1, wherein, The method comprises: obtaining an arbitrary comparison point on one of the pair of digital models to be compared; determining a relative position of the arbitrary comparison point to a surface of the other digital model as the difference amount information.

5. The digital model comparison method of claim 1, wherein, Before obtaining a first data structure and a second data structure corresponding to a first digital model and a second digital model in the pair of digital models to be compared, the method further comprises: obtaining a first digital model to be processed, and performing artificial intelligence recognition on the first digital model to be processed to obtain the first digital model; obtaining a second to-be-processed digital model, and performing artificial intelligence recognition on the second to-be-processed digital model to obtain the second digital model; obtaining a registration matrix between the first digital model and the second digital model; registering the second digital model to the first digital model based on the registration matrix to obtain the to-be-compared digital model pair.

6. The digital model comparison method of claim 1, wherein, The processing of the to-be-compared digital model pair based on the difference amount information, the generation and display of the indication information, include: obtaining a target distance value and a target direction between each point of the digital models in the to-be-compared digital model pair based on the difference amount information; obtaining an association relationship between the difference amount information and the color between the digital models; determining a target color for each point based on the target direction, the target distance value, and the association relationship; rendering each point of the digital models in the to-be-compared digital model pair based on the target color to obtain and display the indication information.

7. The digital model comparison method of any one of claims 1-6, wherein, After displaying the indication information, further comprising: in response to a target model transparency adjustment request; obtaining a target transparency parameter of the target model; adjusting the transparency parameter of the target model; the target model is rendered and displayed based on the target transparency parameter.

8. The digital model comparison method of any one of claims 1-6, wherein, After displaying the indication information, further comprising: in response to a target model hiding request; the target model is hidden and not displayed based on the hiding request.

9. The digital model comparison method of any one of claims 1-6, wherein, Further comprising: in response to a target model color adjustment request, obtaining a target color parameter corresponding to the target model; updating the current color parameter of the target model to the target color parameter; the target model is rendered and displayed based on the target color parameter.

10. The digital model comparison method of any one of claims 1-6, wherein, Further comprising: in response to a target model color adjustment request; adjusting the association relationship between the difference amount information and the color between the digital models; the target model updates the color and is rendered and displayed based on the adjusted association relationship.

11. The digital model comparison method of claim 1, wherein the to-be-compared digital model pair includes two dental arch models obtained at different time points based on the same user; wherein the two dental arch models have been pre-registered and aligned.

12. A digital model comparison apparatus, characterized by, comprising: a first obtaining module for obtaining a to-be-compared digital model pair; a second obtaining module for obtaining difference amount information between digital models in the to-be-compared digital model pair in response to a digital model comparison request; wherein the difference amount information is obtained by calculating between data points based on a data structure corresponding to the to-be-compared digital model pair. The corresponding data structure of the to-be-compared digital model comprises a first data structure and a second data structure; for each first data point in the first data structure, searching in the second data structure to obtain a second data point candidate set; based on the each first data point and each second data point in the second data point candidate set, calculating to obtain a target distance value corresponding to each first data point, and obtaining a target second data point corresponding to the target distance value; based on the normal vector of the plane to which the first data point belongs and the normal vector of the plane to which the target second data point belongs, determining a target direction of the target distance value; taking the target distance value corresponding to each first data point and the target direction of the target distance value as the difference amount information; The method comprises the following steps: obtaining a plurality of first planes to which the first data point belongs, and obtaining a first normal vector and a first weight of each first plane; based on a plurality of the first normal vectors and the first weight, calculating to obtain a first target normal vector of the first data point; obtaining a plurality of second planes to which the second data point belongs, and obtaining a second normal vector and a second weight of each second plane; based on a plurality of the second normal vectors and the second weight, calculating to obtain a second target normal vector of the second data point; based on the first target normal vector and the second target normal vector, calculating to obtain the target direction; The processing generation module is configured to process the to-be-compared digital model based on the difference amount information, generate indication information, and display; wherein the to-be-compared digital model is a to-be-compared dental model.

13. An electronic device, comprising: The electronic device comprises: a processor; a memory for storing executable instructions of the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the digital model comparison method of any one of claims 1-11.

14. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is used to execute the digital model comparison method of any one of claims 1-11. The storage medium stores a computer program, and the computer program is used to execute the digital model comparison method of any one of claims 1-11.

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