Mouth scanning method and mouth scanner based on fusion of posture information and point cloud data

Abstract

The present invention discloses an oral scanning method and an oral scanner based on the fusion of posture information and point cloud data, which belongs to the field of medical devices and includes the following steps: S1, selecting facial feature points with fixed positional relationships relative to the maxillary alveolar ridge and the mandibular alveolar ridge as facial markers for marking; S2, calculating the spatial positions of the oral scanning sensor and the posture sensor fixed on the oral scanner relative to the facial markers; S3, obtaining multi-angle point cloud data; S4, obtaining full-mouth point cloud data; S5, obtaining an intraoral scan model; S6, reconstructing a three-dimensional digital model of the soft and hard tissues of the edentulous jaw; S7, using image processing software to calculate the soft tissue thickness of the edentulous jaw area in the three-dimensional digital model of the soft and hard tissues of the edentulous jaw. The present invention adopts the above-mentioned oral scanning method and oral scanner based on the fusion of posture information and point cloud data, which can significantly reduce or avoid errors that occur in the splicing process of oral scanners that only rely on point cloud data, and improve the accuracy and efficiency of edentulous jaw digital impressions.

Description

Technical Field

[0001] The present invention relates to the field of medical devices, and in particular to an oral scanning method and an oral scanner based on the fusion of posture information and point cloud data. Background Art

[0002] Complete dentures are a conventional restorative treatment method for edentulous patients. They consist of two parts: artificial teeth and denture base. The denture base fits tightly with the edentulous mucosal tissue and the edge closure generates adsorption force and atmospheric pressure, which allows the denture to be adsorbed on the maxillary and mandibular alveolar ridges, restoring the patient's defective tissue and facial appearance, and restoring chewing and pronunciation functions. The mucoperiosteum and bone tissue covered by the denture base bear the occlusal pressure of the denture.

[0003] Therefore, it can be seen that obtaining an impression that meets clinical accuracy standards is a key prerequisite for achieving high-quality restoration results. Traditional impression technology has problems such as complex operation and poor patient experience. Digital impression technology has become the development direction of impression technology due to its advantages such as high efficiency, accuracy, ease of storage and transmission, and patient comfort. However, in the above-mentioned edentulous jaw, due to the lack of scanning and splicing features in the mucosa, intraoral scanning is prone to splicing errors and insufficient accuracy, which limits the use of digital impression technology in the restoration of edentulous teeth, missing teeth, and removable dentures to a certain extent. Summary of the Invention

[0004] To solve the above problems, the present invention provides an oral scanning method and an oral scanner based on the fusion of posture information and point cloud data. By fusing the scanner posture information and the point cloud data obtained by scanning, it can significantly reduce or avoid the errors that may occur in the splicing process of the oral scanner that only relies on point cloud data, thereby improving the accuracy of edentulous digital impressions and the efficiency of intraoral scanning.

[0005] To achieve the above object, the present invention provides a mouth scanning method based on the fusion of posture information and point cloud data, comprising the following steps:

[0006] S1, respectively selecting facial feature points with fixed positional relationships relative to the maxillary alveolar ridge and the mandibular alveolar ridge as facial landmark points for marking;

[0007] S2, based on the stereo camera being fixed on the mouth scanner and remaining unchanged, using the facial landmark information acquired by the stereo camera, calculating the spatial positions of the mouth scanner sensor and the posture sensor fixed on the mouth scanner relative to the facial landmarks;

[0008] S3. Use an oral scanner to perform intraoral scanning to obtain multi-angle point cloud data;

[0009] S4. Using the ICP algorithm, the facial landmark data acquired by the stereo camera in step S2 are overlapped to obtain three-dimensional spatial transformation parameters, and a spatial rectangular coordinate system of the overlapping model is established. The multiple angle scan data acquired in step S3 are fused based on the facial landmarks to obtain full-mouth point cloud data.

[0010] S5. Combining the posture information obtained by the posture sensor and the full-mouth point cloud data obtained in step S4 to obtain an intraoral scanning model;

[0011] S6. Performing a CBCT scan of the entire jaw region including the facial landmarks, converting the original DICOM format data obtained from the CBCT scan to obtain STL data of the jaw including the facial landmarks, constructing a point cloud model based on the converted point cloud data, and fitting the point cloud model to the intraoral scan model obtained in step S5 to reconstruct a three-dimensional digital model of the soft and hard tissues of the edentulous jaw;

[0012] S7. Based on the three-dimensional digital model of soft and hard tissues of the edentulous jaw reconstructed in step S6, use image processing software to calculate the soft tissue thickness of the edentulous jaw region in the three-dimensional digital model of soft and hard tissues of the edentulous jaw.

[0013] Preferably, step S1 specifically includes the following steps:

[0014] S11. Select facial feature points that are fixed relative to the maxillary alveolar ridge as maxillary landmark points, and select facial feature points that are fixed relative to the mandibular alveolar ridge as mandibular landmark points. The maxillary landmark points include the nasal root, nasal tip, anterior nasal spine, and forehead; the mandibular landmark points include the prechin point, subchin point, and chin vertex.

[0015] S12. Paste X-ray-blocking markers on the maxillary marking points and mandibular marking points selected in step S11.

[0016] Preferably, in step S12, the marker is made of an X-ray blocking material and is disposed on the adhesive bottom surface.

[0017] Preferably, the marker comprises barium sulfate, titanium or tungsten, and the adhesive backing comprises rubber, silicone or clay.

[0018] Preferably, step S4 specifically includes the following steps:

[0019] S41, perform intraoral scanning when the face is centered to obtain a source point cloud C with registration in space, and scan the remaining positions to obtain a target point cloud T;

[0020] S42, setting the target point cloud T to be stationary, and calculating the point pair matching relationship between the source point cloud C and the target point cloud T;

[0021] S43. Search for the nearest point of each point in the target point cloud T to the source point cloud C, and establish a corresponding relationship based on the search results:

[0022]

[0023] Where c k (i) indicates The index of the corresponding point in the source point cloud C; R represents the rotation matrix; represents the translation vector; and Represents a point in the source point cloud C and the target point cloud T respectively; represents the two-norm; j represents the index of the point in the target point cloud T; ​​N d Indicates the number of points in the target point cloud T;

[0024] S44. Based on the correspondence relationship obtained in step S43, the Euclidean distance between the point pairs of the source point cloud C and the target point cloud T is calculated, and the average distance of the entire point cloud is calculated;

[0025] S45. Based on the average distance of the entire point cloud calculated in step S44, obtain the three-dimensional space transformation parameters of the source point cloud C and the target point cloud T:

[0026]

[0027] Where R k represents the kth optimal solution of the rotation matrix R; t k Represents the translation vector The kth optimal solution of N s represents the number of optimized point clouds; exp(·) represents the exponential function; σ represents the parameter that controls the bandwidth of the Gaussian function; Indicates the target coordinates established according to the corresponding relationship; Represents the transformed point and corresponding points The Euclidean distance between

[0028] S46. Import the three-dimensional space transformation parameters obtained in step S45 into the source point cloud C described in step S41 to reduce the distance between the source point cloud C and the target point cloud T, and establish a spatial rectangular coordinate system of the overlapping model, fuse the multi-angle point cloud data based on the facial landmarks, and obtain full-mouth point cloud data.

[0029] Preferably, step S6 specifically includes the following steps:

[0030] S61. Data acquisition: Obtain the surface topology of the edentulous jaw using intraoral scanning and CBCT scanning;

[0031] S62, data preprocessing: wherein, the steps for processing the DICOM format raw data obtained by CBCT are as follows: converting the DICOM format raw data obtained by CBCT to obtain the mandibular STL data containing facial landmarks;

[0032] S63, fusing the oral scan STL data and the jaw STL data based on facial landmarks to obtain a three-dimensional digital model of the soft and hard tissues of the edentulous jaw;

[0033] Preferably, in step S61 , the surface topology of the edentulous jaw includes anatomical landmarks and overall soft tissue contours; and the CBCT scan includes the entire jaw region including facial landmarks.

[0034] Preferably, step S63 specifically includes the following steps:

[0035] S631. Extract facial feature points from the oral scan STL data and the jaw bone STL data using image processing technology and mark them.

[0036] S632, using the ICP algorithm to perform coarse registration on the feature points, obtaining an optimal transformation input matrix from the feature points of the oral scan STL data to the feature points of the mandibular STL data, and using the optimal transformation input matrix as the correspondence relationship;

[0037] S633. Use ICP with the maximum correlation entropy metric to perform fine registration of feature points to obtain three-dimensional space transformation parameters. The three-dimensional space transformation parameters are the scaling factor, rotation matrix, and translation vector between the oral scan STL data and the mandibular STL data.

[0038] S634. Use the KD tree nearest neighbor algorithm to delete outliers, align the CBCT model with the intraoral scan model, establish the relative position relationship between the soft tissue contour and the jaw, and use the relative position relationship between the soft tissue contour and the jaw as the three-dimensional digital model of the soft and hard tissues of the edentulous jaw.

[0039] A mouth scanner based on a mouth scanning method based on the fusion of posture information and point cloud data, comprising a body, a posture sensor arranged inside the body, and a stereo camera arranged outside the body;

[0040] Among them, the posture sensor is used to obtain the posture information of the main body;

[0041] The stereo camera is used to scan the facial markers attached to the patient, obtain facial landmarks, and improve the accuracy of the intraoral scanning model based on the facial landmark information and posture information.

[0042] The present invention has the following beneficial effects:

[0043] 1. By fusing the scanner’s posture information with the point cloud data obtained from the scan, it can significantly reduce or avoid errors that may occur during the splicing process of oral scanners that rely solely on point cloud data, thereby improving the accuracy of edentulous digital impressions and the efficiency of intraoral scanning;

[0044] 2. The design of extraoral identification markers avoids the coverage of the oral mucosa caused by conventional intraoral markers, thereby avoiding the loss of real data on the oral mucosal surface. The marking of extraoral markers is relatively easy and is not affected by the patient's mouth opening and intraoral conditions including saliva and alveolar ridge morphology. Therefore, it is suitable for scanning of edentulous jaws, edentulous jaws, tooth defects, and other cases where the external landmark features are not obvious.

[0045] 3. The oral scanner and the stereo camera are fixed in position, and the information of extraoral landmarks is collected while scanning. The operation is relatively simple.

[0046] 4. Reconstructing a three-dimensional digital model of the soft and hard tissues of the edentulous jaw helps to evaluate the thickness characteristics of different mucosal areas and assist in the design and planning of edentulous complete dentures. For example, it is beneficial to provide appropriate buffering for certain local tissue surfaces during denture design and production, and prevent and reduce mucosal trauma caused by denture sinking. At the same time, the soft and hard tissue data are integrated to understand the bony part (hard palate) and muscular part (soft palate) of the upper palate, and to determine the junction of the hard and soft palate, so as to facilitate the precise positioning of the posterior bank area and the accurate design of its morphology.

[0047] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a mouth scanning method based on the fusion of posture information and point cloud data of the present invention;

[0049] Figure 2 Schematic diagram of the structure of an oral scanner of the present invention based on an oral scanning method of fusion of posture information and point cloud data;

[0050] Figure 3 This is a diagram of the marker arrangement of an oral scanner for an oral scanning method based on the fusion of posture information and point cloud data according to the present invention.

[0051] Figure numerals: 1. body; 2. stereo camera; 3. posture sensor; 4. adhesive bottom surface; 5. marker. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar numbers throughout represent the same or similar elements or elements with the same or similar functions.

[0053] It should be noted that the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0054] Like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not require further definition or explanation in subsequent drawings.

[0055] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or are the orientations or positional relationships in which the inventive product is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they should not be understood as limiting the present invention.

[0056] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," and "connected" should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0057] Multimodal fusion technology refers to a technology that integrates and fuses data from different sources and types, such as different sensors (position sensors, attitude sensors, acceleration sensors, etc.) and information of different modalities (pixels, voxels, point clouds, etc.), which helps provide doctors with more comprehensive and accurate diagnostic and treatment information.

[0058] Based on the above analysis, the present invention is designed as follows: Figure 1 As shown, a mouth scanning method based on the fusion of posture information and point cloud data includes the following steps:

[0059] S1, respectively selecting facial feature points with fixed positional relationships relative to the maxillary alveolar ridge and the mandibular alveolar ridge as facial landmark points for marking;

[0060] Step S1 specifically includes the following steps:

[0061] S11. Select facial feature points that are fixed relative to the maxillary alveolar ridge as maxillary landmark points, and select facial feature points that are fixed relative to the mandibular alveolar ridge as mandibular landmark points. The maxillary landmark points include the nasal root, nasal tip, anterior nasal spine, and forehead; the mandibular landmark points include the prechin point, subchin point, and chin vertex.

[0062] S12. Paste the X-ray-blocking marker 5 on the maxillary marking point and the mandibular marking point selected in step S11.

[0063] In step S12 , the marker 5 is made of an X-ray blocking material and is placed on the adhesive bottom surface 4 .

[0064] The marker 5 comprises barium sulfate, titanium or tungsten, and the adhesive base 4 comprises rubber, silicone or clay.

[0065] S2, based on the fact that the stereo camera 2 is fixed on the mouth scanner and remains unchanged, the facial landmark information obtained by the stereo camera 2 is used to calculate the spatial positions of the mouth scanner sensor and the posture sensor 3 fixed on the mouth scanner relative to the facial landmarks;

[0066] S3. Use an oral scanner to perform intraoral scanning to obtain multi-angle point cloud data;

[0067] S4. Using the ICP algorithm, the facial landmark data acquired by the stereo camera 2 in step S2 are overlapped to obtain three-dimensional space transformation parameters, and a spatial rectangular coordinate system of the overlapping model is established. The multiple angle scan data acquired in step S3 are fused based on the facial landmarks to obtain full-mouth point cloud data.

[0068] Step S4 specifically includes the following steps:

[0069] S41, perform intraoral scanning when the face is centered to obtain a source point cloud C with registration in space, and scan the remaining positions to obtain a target point cloud T;

[0070] S42, setting the target point cloud T to be stationary, and calculating the point pair matching relationship between the source point cloud C and the target point cloud T;

[0071] S43. Search for the nearest point of each point in the target point cloud T to the source point cloud C, and establish a corresponding relationship based on the search results:

[0072]

[0073] Where c k (i) indicates The index of the corresponding point in the source point cloud C; R represents the rotation matrix; represents the translation vector; and Represents a point in the source point cloud C and the target point cloud T respectively; represents the two-norm; j represents the index of the point in the target point cloud T; ​​N d Indicates the number of points in the target point cloud T;

[0074] S44. Based on the correspondence relationship obtained in step S43, the Euclidean distance between the point pairs of the source point cloud C and the target point cloud T is calculated, and the average distance of the entire point cloud is calculated;

[0075] S45. Based on the average distance of the entire point cloud calculated in step S44, obtain the three-dimensional space transformation parameters of the source point cloud C and the target point cloud T:

[0076]

[0077] Where R k represents the kth optimal solution of the rotation matrix R; t k Represents the translation vector The kth optimal solution of N s represents the number of optimized point clouds; exp(·) represents the exponential function; σ represents the parameter that controls the bandwidth of the Gaussian function; Indicates the target coordinates established according to the corresponding relationship; Represents the transformed point and corresponding points The Euclidean distance between

[0078] S46. Import the three-dimensional space transformation parameters obtained in step S45 into the source point cloud C described in step S41 to reduce the distance between the source point cloud C and the target point cloud T, and establish a spatial rectangular coordinate system of the overlapping model, fuse the multi-angle point cloud data based on the facial landmarks, and obtain full-mouth point cloud data.

[0079] S5. Combining the posture information obtained by the posture sensor 3 and the full-mouth point cloud data obtained in step S4, an intraoral scanning model is obtained;

[0080] S6. Performing a CBCT scan of the entire jaw region including the facial landmarks, converting the original DICOM format data obtained from the CBCT scan to obtain STL data of the jaw including the facial landmarks, constructing a point cloud model based on the converted point cloud data, and fitting the point cloud model to the intraoral scan model obtained in step S5 to reconstruct a three-dimensional digital model of the soft and hard tissues of the edentulous jaw;

[0081] Step S6 specifically includes the following steps:

[0082] S61. Data acquisition: Obtain the surface topology of the edentulous jaw using intraoral scanning and CBCT scanning;

[0083] In step S61 , the surface topology of the edentulous jaw including anatomical landmarks and overall soft tissue contours is determined; the CBCT scan includes the entire jaw region including facial landmarks.

[0084] S62, data preprocessing: wherein, the steps for processing the DICOM format raw data obtained by CBCT are as follows: converting the DICOM format raw data obtained by CBCT to obtain the mandibular STL data containing facial landmarks;

[0085] S63, fusing the oral scan STL data and the jaw STL data based on facial landmarks to obtain a three-dimensional digital model of the soft and hard tissues of the edentulous jaw;

[0086] Step S63 specifically includes the following steps:

[0087] S631. Extract facial feature points from the oral scan STL data and the jaw bone STL data using image processing technology and mark them.

[0088] S632, using the ICP algorithm to perform coarse registration on the feature points, obtaining an optimal transformation input matrix from the feature points of the oral scan STL data to the feature points of the mandibular STL data, and using the optimal transformation input matrix as the correspondence relationship;

[0089] S633. Use ICP with the maximum correlation entropy metric to perform fine registration of feature points to obtain three-dimensional space transformation parameters. The three-dimensional space transformation parameters are the scaling factor, rotation matrix, and translation vector between the oral scan STL data and the mandibular STL data.

[0090] S634. Use the KD tree nearest neighbor algorithm to delete outliers, align the CBCT model with the intraoral scan model, establish the relative position relationship between the soft tissue contour and the jaw, and use the relative position relationship between the soft tissue contour and the jaw as the three-dimensional digital model of the soft and hard tissues of the edentulous jaw.

[0091] S7. Based on the three-dimensional digital model of edentulous soft and hard tissues reconstructed in step S6, use image processing software (such as CloudCompare, Point CloudLibrary) to calculate the soft tissue thickness of the edentulous area in the three-dimensional digital model of edentulous soft and hard tissues, so as to evaluate the thickness characteristics of different mucosal areas, assist in the design and planning of edentulous full dentures, and facilitate appropriate buffering of certain local tissue surfaces during denture design and production, prevent and reduce mucosal trauma caused by denture sinking, and alleviate patient pain; at the same time, fuse the two data of soft tissue and hard tissue to understand the bony part (hard palate) and muscular part (soft palate) of the upper palate, determine the junction of the hard and soft palate, thereby facilitating the precise positioning of the posterior bank area and the accurate design of the morphology of the posterior bank area.

[0092] like Figure 2 and Figure 3 As shown, an oral scanner employing an oral scanning method based on the fusion of posture information and point cloud data comprises a body 1, a posture sensor 3 disposed within the body 1, and a stereo camera 2 disposed outside the body 1. In this embodiment, the posture sensor 3 can be located at the front, middle, or rear end of the body 1, and the stereo camera 2 can be located above, below, or to the side of the body 1. The posture sensor 3 is used to obtain the posture information of the body 1. The stereo camera 2 is used to scan facial markers 5 attached to the patient's face to obtain facial landmarks, and is used to improve the accuracy of the intraoral scan model based on the facial landmark information and posture information. The stereo camera 2 comprises a first infrared camera, a color camera, and a second infrared camera arranged in a linear array. The first, color, and second infrared cameras all utilize CMOS cameras with a field of view of 65°×40°, a resolution of 1280×720, and a frame rate of 90 FPS. The posture sensor 3 has three degrees of freedom and an accuracy of 0.2°. The stereo camera 2 is used to scan facial markers 5 attached to the patient's face to obtain facial landmarks. The posture sensor 3 is used to obtain the posture information of the body 1.

[0093] Therefore, the present invention adopts the above-mentioned oral scanning method and oral scanner based on the fusion of posture information and point cloud data. By fusing the scanner posture information and the point cloud data obtained by scanning, it can significantly reduce or avoid the errors that may occur in the splicing process of the oral scanner that only relies on point cloud data, thereby improving the accuracy of edentulous digital impressions and the efficiency of intraoral scanning.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A mouth scanning method based on the fusion of posture information and point cloud data, characterized by: The following steps are involved: S1, respectively selecting facial feature points with fixed positional relationships relative to the maxillary alveolar ridge and the mandibular alveolar ridge as facial landmark points for marking; S2, based on the stereo camera being fixed on the mouth scanner and remaining unchanged, using the facial landmark information acquired by the stereo camera, calculating the spatial positions of the mouth scanner sensor and the posture sensor fixed on the mouth scanner relative to the facial landmarks; S3. Use an oral scanner to perform intraoral scanning to obtain multi-angle point cloud data; S4. Using the ICP algorithm, the facial landmark data acquired by the stereo camera in step S2 are overlapped to obtain three-dimensional spatial transformation parameters, and a spatial rectangular coordinate system of the overlapping model is established. The multiple angle scan data acquired in step S3 are fused based on the facial landmarks to obtain full-mouth point cloud data. S5. Combining the posture information obtained by the posture sensor and the full-mouth point cloud data obtained in step S4 to obtain an intraoral scanning model; S6. Perform a CBCT scan of the entire jaw region including the facial landmarks, convert the original DICOM format data obtained from the CBCT scan to obtain STL data of the jaw including the facial landmarks, construct a point cloud model based on the converted point cloud data, and then fit the point cloud model to the intraoral scan model obtained in step S5 to reconstruct a three-dimensional digital model of the soft and hard tissues of the edentulous jaw; Step S6 specifically includes the following steps: S61. Data acquisition: Obtain the surface topology of the edentulous jaw using intraoral scanning and CBCT scanning; S62. Data preprocessing: The steps for processing the DICOM format raw data obtained by CBCT are as follows: converting the DICOM format raw data obtained by CBCT to obtain the mandibular STL data containing facial landmarks; S63, fusing the oral scan STL data and the jaw STL data based on facial landmarks to obtain a three-dimensional digital model of the soft and hard tissues of the edentulous jaw; Step S63 specifically includes the following steps: S631. Extract facial feature points from the oral scan STL data and the jaw bone STL data using image processing technology and mark them. S632, using the ICP algorithm to perform coarse registration on the feature points, obtaining an optimal transformation input matrix from the feature points of the oral scan STL data to the feature points of the mandibular STL data, and using the optimal transformation input matrix as the correspondence relationship; S633, using the ICP algorithm that introduces the maximum correlation entropy metric to perform fine registration of the feature points to obtain three-dimensional space transformation parameters, which are the scaling factor, rotation matrix, and translation vector between the oral scan STL data and the mandibular STL data; S634: Using the KD tree nearest neighbor algorithm, outliers were deleted, the CBCT model and the intraoral scan model were aligned, and the relative position relationship between the soft tissue contour and the jaw was established. The relative position relationship between the soft tissue contour and the jaw was used as the 3D digital model of the soft and hard tissues of the edentulous jaw. S7. Based on the three-dimensional digital model of soft and hard tissues of the edentulous jaw reconstructed in step S6, use image processing software to calculate the soft tissue thickness of the edentulous jaw region in the three-dimensional digital model of soft and hard tissues of the edentulous jaw.

2. The oral scanning method based on the fusion of posture information and point cloud data according to claim 1, characterized in that: Step S1 specifically includes the following steps: S11. Select facial feature points that are fixed relative to the maxillary alveolar ridge as maxillary landmark points, and select facial feature points that are fixed relative to the mandibular alveolar ridge as mandibular landmark points. The maxillary landmark points include the nasal root, nasal tip, anterior nasal spine, and forehead; the mandibular landmark points include the prechin point, subchin point, and chin vertex. S12. Paste X-ray-blocking markers on the maxillary marking points and mandibular marking points selected in step S11.

3. The oral scanning method based on the fusion of posture information and point cloud data according to claim 2, characterized in that: In step S12, the marker is made of an X-ray blocking material and is placed on the adhesive bottom surface.

4. The oral scanning method based on the fusion of posture information and point cloud data according to claim 1, characterized in that: Markers include barium sulfate, titanium, or tungsten, and adhesive backings include rubber, silicone, or clay.

5. The oral scanning method based on the fusion of posture information and point cloud data according to claim 4, characterized in that: Step S4 specifically includes the following steps: S41, perform intraoral scanning when the face is centered to obtain a source point cloud C with registration in space, and scan the remaining positions to obtain a target point cloud T; S42, setting the target point cloud T to be stationary, and calculating the point pair matching relationship between the source point cloud C and the target point cloud T; S43. Search for the nearest point of each point in the target point cloud T corresponding to the source point cloud C, and establish a corresponding relationship based on the search results: Where, express The index of the corresponding point in the source point cloud C; represents the rotation matrix; represents the translation vector; and Represents a point in the source point cloud C and the target point cloud T respectively; represents the two-norm; Represents the index of the point in the target point cloud T; Indicates the number of points in the target point cloud T; S44. Based on the correspondence relationship obtained in step S43, the Euclidean distance between the point pairs of the source point cloud C and the target point cloud T is calculated, and the average distance of the entire point cloud is calculated; S45. Based on the average distance of the entire point cloud calculated in step S44, obtain the three-dimensional space transformation parameters of the source point cloud C and the target point cloud T: Where, Represents the rotation matrix The kth optimal solution of ; Represents the translation vector The kth optimal solution of ; Indicates the number of optimized point clouds; represents the exponential function; represents the parameter that controls the bandwidth of the Gaussian function; Indicates the target coordinates established according to the corresponding relationship; Represents the transformed point and corresponding points The Euclidean distance between S46. Import the three-dimensional space transformation parameters obtained in step S45 into the source point cloud C described in step S41 to reduce the distance between the source point cloud C and the target point cloud T, and establish a spatial rectangular coordinate system of the overlapping model, fuse the multi-angle point cloud data based on the facial landmarks, and obtain full-mouth point cloud data.

6. The oral scanning method based on the fusion of posture information and point cloud data according to claim 1, characterized in that: In step S61 , the surface topology of the edentulous jaw including anatomical landmarks and overall soft tissue contours is determined; the CBCT scan includes the entire jaw region including facial landmarks.

7. An oral scanner using the oral scanning method based on the fusion of posture information and point cloud data according to any one of claims 1 to 6, characterized in that: It includes a body, a posture sensor arranged inside the body, and a stereo camera arranged outside the body; Among them, the posture sensor is used to obtain the posture information of the main body; The stereo camera is used to scan the facial markers attached to the patient, obtain facial landmarks, and improve the accuracy of the intraoral scanning model based on the facial landmark information and posture information.

Images