Oral scanning method and oral scanning instrument based on pose information and point cloud data fusion

Abstract

The invention discloses a mouth scanning method and mouth scanning instrument based on pose information and point cloud data fusion, and belongs to the field of medical instruments, and the method comprises the following steps: S1, selecting facial feature points which are fixed relative to a position relation between an upper jaw alveolar ridge and a lower jaw alveolar ridge as facial marking points for marking; s2, calculating the spatial positions of a mouth scanning sensor and an attitude sensor fixed on the mouth scanning instrument relative to the facial mark point; s3, obtaining multi-angle point cloud data; s4, obtaining full-port point cloud data; s5, obtaining an intraoral scanning model; s6, reconstructing an edentulous jaw soft and hard tissue three-dimensional digital model; and S7, calculating the soft tissue thickness of an edentulous region in the edentulous soft and hard tissue three-dimensional digital model by using image processing software. According to the oral scanning method and the oral scanning instrument based on the pose information and the point cloud data fusion, errors occurring in the splicing process of the oral scanning instrument only depending on the point cloud data can be remarkably reduced or avoided, and the precision and the efficiency of edentulous jaw digital impression are improved.

Description

Technical Field

[0001] The present invention relates to the field of medical devices, and in particular to a mouth scanning method and a mouth scanning device 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 are composed of two parts: artificial teeth and denture base. The denture base fits closely with the edentulous jaw 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, restore the patient's defective tissue and facial appearance, and restore 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 impressions that meet clinical accuracy standards is the key prerequisite for achieving high-quality restoration results. Traditional impression technology has problems such as complex operation and poor patient experience, while digital impression technology has become the development direction of impression technology with its advantages such as high efficiency, accuracy, easy storage and transmission, and patient comfort. However, in the above-mentioned edentulous jaw, due to the lack of scanning and splicing features of the mucosa, it is easy to cause splicing errors and insufficient accuracy during intraoral scanning, which limits the use of digital impression technology in edentulous, missing teeth, and removable denture restorations 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, the errors that may occur in the splicing process of the oral scanner that only relies on point cloud data can be significantly reduced or avoided, 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 relationship relative to the maxillary alveolar ridge and the mandibular alveolar ridge as facial landmark points for marking;

[0007] S2, based on the fact that the stereo camera is fixed on the mouth scanner and remains unchanged, the facial marker information obtained by the stereo camera is used to calculate the spatial positions of the mouth scanner sensor and the posture sensor fixed on the mouth scanner relative to the facial markers;

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

[0009] S4, using the ICP algorithm, overlapping the facial marker point data obtained by the stereo camera in step S2, obtaining three-dimensional space transformation parameters, and establishing a spatial rectangular coordinate system of the overlapping model, fusing the multiple angle scanning data obtained in step S3 based on the facial marker points, and obtaining 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 intra-oral scanning model;

[0011] S6, performing CBCT scanning on the entire jaw region including facial landmarks, and converting the original data in DICOM format obtained by the CBCT scanning to obtain STL data of the jaw including facial landmarks, constructing a point cloud model based on the converted point cloud data, and then fitting the point cloud model with the intraoral scanning 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, selecting facial feature points with a fixed position relative to the maxillary alveolar ridge as maxillary marker points, and selecting facial feature points with a fixed position relative to the mandibular alveolar ridge as mandibular marker points: the maxillary marker points include the nasal root, the nose tip, the anterior nasal spine point, and the forehead; the mandibular marker points include the prechin point, the subchin point, and the chin vertex;

[0015] S12, sticking 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 a sticky 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 corresponding to the source point cloud C, and establish a corresponding relationship based on the search results:

[0022]

[0023] In the formula, c k (i) Representation The index of the corresponding point in the source point cloud C; R represents the rotation matrix; represents the translation vector; and Represent a point in the source point cloud C and the target point cloud T respectively; represents the binary 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 corresponding relationship obtained in step S43, calculating the Euclidean distance between the point pairs of the source point cloud C and the target point cloud T, and calculating the average distance of the entire point cloud;

[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] In the formula, R k represents the kth optimal solution of the rotation matrix R; t k Represents the translation vector The kth optimal solution of 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 With corresponding point 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 marker points, and obtain the full-mouth point cloud data.

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

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

[0031] S62, data preprocessing: wherein, the steps for processing the original data in DICOM format acquired by CBCT are as follows: converting the original data in DICOM format acquired by CBCT to obtain the jaw 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, extracting facial feature points from the oral scan STL data and the jawbone STL data using image processing technology, and marking them;

[0036] S632, using the ICP algorithm to roughly align the feature points, obtain the optimal transformation input matrix from the feature points of the oral scan STL data to the feature points of the jawbone STL data, and use the optimal transformation input matrix as the corresponding relationship;

[0037] S633. Use ICP with maximum correlation entropy measurement to perform fine alignment on 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. The KD tree nearest neighbor algorithm was used 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 3D digital model of the edentulous jaw's soft and hard tissues.

[0039] A mouth scanner based on a mouth scanning method of 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 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 oral scanners that only rely on point cloud data, and improve the accuracy of edentulous digital impressions and the efficiency of intraoral scanning;

[0044] 2. The design of identifying marking points outside the mouth avoids the coverage of the oral mucosa caused by conventional intraoral marking points, thereby avoiding the loss of real data on the oral mucosal surface; and the marking of extraoral marking points is relatively easy and is not affected by the patient's mouth opening and intraoral conditions including saliva, alveolar ridge morphology, etc., so it is suitable for scanning of edentulous jaws, edentulous jaws, tooth defects and other scans with unclear external marking features;

[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 the three-dimensional digital model of the soft and hard tissues of the edentulous jaw is helpful 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 the design and production of dentures, and prevent and reduce mucosal trauma caused by denture sinking; at the same time, the two data of soft tissue and hard tissue are integrated to understand the bony part (hard palate) and muscular part (soft palate) of the upper palate, and determine the junction between the hard and soft palate, so as to facilitate the precise positioning of the posterior bank area and the accurate design of the morphology of the posterior bank area.

[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 It 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 It is a structural schematic diagram of a mouth scanner of the present invention based on a mouth 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 of the oral scanning method based on the fusion of posture information and point cloud data of the present invention.

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

[0052] In order to make the purpose, technical scheme and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail in conjunction with 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 reference numerals throughout represent the same or similar elements or elements with the same or similar functions.

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

[0054] Like reference numerals and letters denote similar items in the following drawings, and thus, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0055] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, or are directions or positional relationships in which the product of the invention 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 direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation on the present invention.

[0056] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "setting", "installation" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to 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 diagnosis 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 relationship 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, selecting facial feature points with a fixed position relative to the maxillary alveolar ridge as maxillary marker points, and selecting facial feature points with a fixed position relative to the mandibular alveolar ridge as mandibular marker points: the maxillary marker points include the nasal root, the nose tip, the anterior nasal spine point, and the forehead; the mandibular marker points include the prechin point, the subchin point, and the chin vertex;

[0062] S12, sticking 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 disposed on the adhesive bottom surface 4 .

[0064] The marker 5 comprises barium sulfate, titanium or tungsten, and the adhesive bottom surface 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 marker 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 markers;

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

[0067] S4, using the ICP algorithm, overlapping the facial marker point data obtained by the stereo camera 2 in step S2, obtaining three-dimensional space transformation parameters, and establishing a spatial rectangular coordinate system of the overlapping model, fusing the multiple angle scanning data obtained in step S3 based on the facial marker points, and obtaining 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 corresponding to the source point cloud C, and establish a corresponding relationship based on the search results:

[0072]

[0073] In the formula, c k (i) Representation The index of the corresponding point in the source point cloud C; R represents the rotation matrix; represents the translation vector; and Represent a point in the source point cloud C and the target point cloud T respectively; represents the binary 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 corresponding relationship obtained in step S43, calculating the Euclidean distance between the point pairs of the source point cloud C and the target point cloud T, and calculating the average distance of the entire point cloud;

[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] In the formula, R k represents the kth optimal solution of the rotation matrix R; t k Represents the translation vector The kth optimal solution of 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 With corresponding point 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 marker points, and obtain the 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 to obtain an intra-oral scanning model;

[0080] S6, performing CBCT scanning on the entire jaw region including facial landmarks, and converting the original data in DICOM format obtained by the CBCT scanning to obtain STL data of the jaw including facial landmarks, constructing a point cloud model based on the converted point cloud data, and then fitting the point cloud model with the intraoral scanning 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: intraoral scanning to obtain the surface topology of the edentulous jaw and CBCT scanning;

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

[0084] S62, data preprocessing: wherein, the steps for processing the original data in DICOM format acquired by CBCT are as follows: converting the original data in DICOM format acquired by CBCT to obtain the jaw 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, extracting facial feature points from the oral scan STL data and the jawbone STL data using image processing technology, and marking them;

[0088] S632, using the ICP algorithm to roughly align the feature points, obtain the optimal transformation input matrix from the feature points of the oral scan STL data to the feature points of the jawbone STL data, and use the optimal transformation input matrix as the corresponding relationship;

[0089] S633. Use ICP with maximum correlation entropy measurement to perform fine alignment on 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. The KD tree nearest neighbor algorithm was used 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 3D digital model of the edentulous jaw's soft and hard tissues.

[0091] S7. Based on the three-dimensional digital model of the soft and hard tissues of the edentulous jaw reconstructed in step S6, use image processing software (such as CloudCompare, Point CloudLibrary) 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, so as to evaluate the thickness characteristics of different mucosal areas, assist in the design and planning of edentulous complete 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, merge 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 between the hard and soft palate, so as to facilitate the precise positioning of the posterior bank area, so as to facilitate the accurate design of the morphology of the posterior bank area.

[0092] like Figure 2 and Figure 3 As shown, a mouth scanner based on a mouth scanning method based on the fusion of posture information and point cloud data includes a body 1, a posture sensor 3 arranged inside the body 1, and a stereo camera 2 arranged outside the body 1; in this embodiment, the posture sensor 3 can be located at the front end, middle end or rear end of the body 1, and the stereo camera 2 can be arranged above, below or on the side of the body 1, wherein the posture sensor 3 is used to obtain the posture information of the body 1; the stereo camera 2 is used to scan the facial marker 5 attached to the patient to obtain facial landmarks, and is used to improve the accuracy of the intraoral scanning model based on the facial landmark information and posture information. The stereo camera 2 includes a first infrared camera, a color camera and a second infrared camera arranged in a linear array, and the first infrared camera, the color camera and the second infrared camera all use CMOS cameras, and the field of view of the CMOS camera is 65°×40°, the resolution is 1280×720, and the frame rate is 90FPS; the posture sensor 3 has 3 degrees of freedom and an accuracy of 0.2°; wherein the stereo camera 2 is used to scan the facial marker 5 attached to the patient 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 solution of the present invention rather than to limit it. 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 solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A mouth scanning method based on the fusion of posture information and point cloud data, characterized in that: The following steps are involved: S1, respectively selecting facial feature points with fixed positional relationship relative to the maxillary alveolar ridge and the mandibular alveolar ridge as facial landmark points for marking; S2, based on the fact that the stereo camera is fixed on the mouth scanner and remains unchanged, the facial marker information obtained by the stereo camera is used to calculate the spatial positions of the mouth scanner sensor and the posture sensor fixed on the mouth scanner relative to the facial markers; S3, use an oral scanner to perform intraoral scanning to obtain multi-angle point cloud data; S4, using the ICP algorithm, overlapping the facial marker point data obtained by the stereo camera in step S2, obtaining three-dimensional space transformation parameters, and establishing a spatial rectangular coordinate system of the overlapping model, fusing the multiple angle scanning data obtained in step S3 based on the facial marker points, and obtaining 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 intra-oral scanning model; S6, performing CBCT scanning on the entire jaw region including facial landmarks, and converting the original data in DICOM format obtained by the CBCT scanning to obtain STL data of the jaw including facial landmarks, constructing a point cloud model based on the converted point cloud data, and then fitting the point cloud model with the intraoral scanning model obtained in step S5 to reconstruct a three-dimensional 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, selecting facial feature points with a fixed position relative to the maxillary alveolar ridge as maxillary marker points, and selecting facial feature points with a fixed position relative to the mandibular alveolar ridge as mandibular marker points: the maxillary marker points include the nasal root, the nose tip, the anterior nasal spine point, and the forehead; the mandibular marker points include the prechin point, the subchin point, and the chin vertex; S12, sticking 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 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 2, 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: In the formula, c k (i) Representation The index of the corresponding point in the source point cloud C; R represents the rotation matrix; represents the translation vector; and Represent a point in the source point cloud C and the target point cloud T respectively; represents the binary 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; S44, based on the corresponding relationship obtained in step S43, calculating the Euclidean distance between the point pairs of the source point cloud C and the target point cloud T, and calculating the average distance of the entire point cloud; 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: In the formula, R k represents the kth optimal solution of the rotation matrix R; t k Represents the translation vector The kth optimal solution of 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 With corresponding point 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 marker points, and obtain the full-mouth point cloud data.

6. The oral scanning method based on the fusion of posture information and point cloud data according to claim 5, characterized in that: Step S6 specifically includes the following steps: S61. Data acquisition: intraoral scanning to obtain the surface topology of the edentulous jaw and CBCT scanning; S62, data preprocessing: wherein, the steps for processing the original data in DICOM format acquired by CBCT are as follows: converting the original data in DICOM format acquired by CBCT to obtain the jaw STL data containing facial landmarks; S63. The oral scan STL data and the jaw bone STL data are fused based on facial landmarks to obtain a three-dimensional digital model of the hard and soft tissues of the edentulous jaw.

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

8. The oral scanning method based on the fusion of posture information and point cloud data according to claim 6, characterized in that: Step S63 specifically includes the following steps: S631, extracting facial feature points from the oral scan STL data and the jawbone STL data using image processing technology, and marking them; S632, using the ICP algorithm to roughly align the feature points, obtain the optimal transformation input matrix from the feature points of the oral scan STL data to the feature points of the jawbone STL data, and use the optimal transformation input matrix as the corresponding relationship; S633, using ICP with maximum correlation entropy measurement to perform fine registration of feature points, and obtain three-dimensional space transformation parameters, where the three-dimensional space transformation parameters are the scaling factor, rotation matrix and translation vector between the oral scan STL data and the jawbone STL data; S634. The KD tree nearest neighbor algorithm was used 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.

9. An oral scanner using the oral scanning method based on the fusion of posture information and point cloud data as described in any one of claims 1 to 8, 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.

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