CN114693751A - Data processing method, point cloud data registration method and device and intraoral scanning equipment - Google Patents
Data processing method, point cloud data registration method and device and intraoral scanning equipment Download PDFInfo
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Abstract
The invention discloses a data processing method, a point cloud data registration device and intraoral scanning equipment, wherein the registration method comprises the following steps: obtaining a first rotation translation matrix according to the point cloud to be registered and the target curved surface to be registered; transforming the point cloud to be registered by using the first rotation and translation matrix to obtain a first transformed point cloud, and updating the point cloud to be registered by using the first transformed point cloud; judging whether the point cloud conversion times meet a first preset condition or not; if so, obtaining an approximate rotation and translation matrix according to the first rotation and translation matrix by using an approximation algorithm, and obtaining an approximate transformation point cloud by using the approximate rotation and translation matrix for multiple transformation until a second preset condition is met; and judging whether the registration is finished or not according to the point cloud to be registered and the curved surface of the target to be registered, and if not, returning to the step of obtaining a first rotational translation matrix of the point cloud to be registered and the curved surface of the target to be registered until the registration is finished. The method improves the point-to-surface registration speed in a mode of registering the point cloud to be registered and the curved surface of the target to be registered.
Description
Technical Field
The invention relates to the technical field of three-dimensional scanning, in particular to a point cloud data processing method, a point cloud data registration device and intraoral scanning equipment.
Background
The process of reconstructing the three-dimensional model of the object comprises point cloud reconstruction, point cloud registration, point cloud fusion, curved surface reconstruction and the like. In the related art, the above-mentioned registration process is usually implemented by using a point-to-point registration manner and a point-to-plane registration manner, where the point-to-point registration speed is relatively fast, and the registration process can be used for three-dimensional reconstruction in a real-time scanning process, for example, in the process of scanning an oral cavity, the current intraoral scanning device implements registration by using the point-to-point registration manner, thereby implementing the above-mentioned three-dimensional reconstruction process.
However, since the positions of the points are discrete, the accuracy in calculating the corresponding points is low. Compared with a point-to-point registration method, the point-to-surface registration method has higher precision, but the registration speed is relatively low, so that the application range of the point-to-surface registration is influenced to a certain extent.
Disclosure of Invention
The present invention is directed to solving, at least in part, one of the technical problems in the related art. Therefore, the first purpose of the invention is to provide a point cloud data registration method, which improves the point-to-surface registration speed by using an approximate rotation translation matrix to continuously transform point clouds to be registered for multiple times in the registration process of point cloud data and a target curved surface.
A second object of the present invention is to provide a data processing method.
The third purpose of the invention is to provide a point cloud data registration device.
A fourth object of the present invention is to provide an intraoral scanning device.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides a point cloud data registration method, including: obtaining a first rotation translation matrix according to the point cloud to be registered and the target curved surface to be registered; transforming the point cloud to be registered by using the first rotational translation matrix to obtain a first transformed point cloud, and updating the point cloud to be registered by using the first transformed point cloud; judging whether the current point cloud conversion times meet a first preset condition or not; if so, obtaining an approximate rotation translation matrix according to the first rotation translation matrix by using an approximation algorithm, continuously and repeatedly transforming the point cloud to be registered by using the approximate rotation translation matrix to obtain an approximate transformation point cloud until a second preset condition is met, and updating the point cloud to be registered by using the approximate transformation point cloud; and judging whether the registration is finished or not according to the point cloud to be registered and the curved surface of the target to be registered, if not, returning to the step of obtaining the first rotational translation matrix according to the point cloud to be registered and the curved surface of the target to be registered until the registration is finished.
According to the point cloud data registration method provided by the embodiment of the invention, a first rotation and translation matrix is obtained through point clouds to be registered and a curved surface of a target to be registered, the point clouds to be registered are converted by using the first rotation and translation matrix to obtain a first conversion point cloud, the point clouds to be registered are updated by using the first conversion point cloud, then whether the conversion frequency of the current point cloud meets a first preset condition or not is judged, if yes, an approximation algorithm is used for obtaining an approximation rotation and translation matrix according to the first rotation and translation matrix, the point clouds to be registered are continuously converted for multiple times by using the approximation rotation and translation matrix to obtain an approximation conversion point cloud until a second preset condition is met, the point clouds to be registered are updated by using the approximation conversion point cloud, and then whether the registration is completed or not is judged according to the point cloud to be registered and the curved surface of the target to be registered, so that the registration speed from point to surface can be effectively improved.
In order to achieve the above object, a second aspect of the present invention provides a data processing method, including:
cutting out a curved surface of a target to be registered according to the point cloud to be registered and the curved surface which is subjected to registration and fusion; and registering according to the point cloud data registration method to obtain registered point clouds, fusing the registered point clouds to a curved surface of a target to be registered to obtain a new curved surface which is subjected to registration and fusion, updating the point clouds to be registered, returning to the step of cutting out the curved surface of the target to be registered according to the point clouds to be registered and the curved surface which is subjected to registration and fusion until all point cloud data participate in registration and fusion.
According to the data processing method provided by the embodiment of the invention, the curved surface of the target to be registered is cut out based on the point cloud to be registered and the curved surface which is subjected to registration and fusion, the point cloud to be registered is registered by the point cloud data registration method to obtain the point cloud after registration, the point cloud after registration is fused to the curved surface of the target to be registered to obtain a new curved surface which is subjected to registration and fusion, the point cloud to be registered is updated, and the step of cutting out the curved surface of the target to be registered according to the point cloud to be registered and the curved surface which is subjected to registration and fusion is returned until all the point cloud data participates in registration and fusion is carried out.
In order to achieve the above object, a point cloud data registration apparatus according to a third aspect of the present invention includes a memory, a processor, and a computer program stored in the memory, where the computer program is executed by the processor to implement the point cloud data registration method.
According to the point cloud data registration device provided by the embodiment of the invention, the point cloud to be registered and the curved surface of the target to be registered are accurately matched by the point cloud data registration method, and the point-to-surface registration speed is increased.
In order to achieve the above object, a fourth aspect of the present invention provides an intraoral scanning apparatus, including the above point cloud data registration device.
According to the intraoral scanning equipment disclosed by the embodiment of the invention, the point cloud data registration device on the intraoral scanning equipment realizes the accurate matching of the point cloud to be registered and the curved surface of the target to be registered by the point cloud data registration method, and the point-to-surface registration speed is improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of a method of point cloud data registration according to one embodiment of the invention;
FIG. 2 is a flow chart of a point cloud data registration process according to one embodiment of the present invention;
FIG. 3 is a block diagram of an intraoral scanning device according to one embodiment of the present invention;
FIG. 4 is a general flow diagram of a method of point cloud data registration according to one specific example of the invention;
FIG. 5 is a flow chart of a preprocessing of a point cloud data registration method according to one embodiment of the present invention;
FIG. 6 is a flow chart illustrating a first-level decision of a point cloud data registration method according to an embodiment of the present invention;
FIG. 7 is a flow chart of point-to-surface registration of a point cloud data registration method according to one specific example of the present invention;
FIG. 8 is a schematic representation of a surface build after real-time scanning according to one embodiment of the present disclosure;
fig. 9 is a schematic diagram of post-finishing optimization of a curved surface according to a specific example of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
A data processing method, a point cloud data registration method, an apparatus, and an intraoral scanning device of an embodiment of the present invention are described below with reference to the accompanying drawings.
The embodiment of the invention provides a data processing method, which comprises the following steps:
(1) and cutting out the curved surface of the target to be registered according to the point cloud to be registered and the curved surface which is subjected to registration and fusion.
The intraoral scanning equipment can acquire point cloud data during scanning, wherein the point cloud data comprises a plurality of point clouds, and the plurality of point clouds are traversed in the registration process and serve as a plurality of point clouds to be registered. During later-stage curved surface reconstruction, curved surface reconstruction software corresponding to the intraoral scanning equipment can obtain point cloud data with the highest registration degree according to the obtained multiple point clouds to be registered, reconstruct an initial curved surface which is subjected to registration and fusion, namely an initial curved surface to be registered, according to the point cloud data, and of course, select one point cloud from the obtained point cloud data according to other modes and perform curved surface reconstruction according to the point cloud to obtain an initial curved surface which is subjected to registration and fusion and an initial curved surface to be registered.
In the subsequent cycle process, the curved surface which is subjected to registration fusion is segmented according to the surrounding grid of the point cloud to be registered so as to obtain the segmented curved surface of the target to be registered.
(2) And under the condition that the point cloud to be registered and the curved surface of the target to be registered are matched, fusing the point cloud data after registration to the curved surface of the target to be registered to obtain a new curved surface which is subjected to registration and fusion, updating the point cloud to be registered, returning to the curved surface which is subjected to registration and fusion according to the point cloud to be registered and the curved surface which is subjected to registration and fusion, and cutting out the curved surface of the target to be registered until all the point cloud data participate in registration and fusion.
And after the curved surface of the target to be registered is obtained, performing registration judgment on other point clouds and the curved surface of the target to be registered, fusing the other point clouds and the curved surface of the target to be registered when a registration condition is met, and continuously enlarging the curved surface after the registration fusion is completed along with the circulation of the fusion process, thereby realizing the construction of the target three-dimensional model. The curved surface which is obtained in the step and has completed registration and fusion comprises a curved surface which has completed registration and fusion last time and a part which is fused to the current curved surface to be registered.
In specific implementation, whether matching is performed or not can be judged according to whether the distance between the point cloud after registration and the curved surface of the target to be registered is smaller than a preset threshold value or not, if the distance is smaller than or equal to the preset threshold value, matching can be considered, and if the distance is larger than the preset threshold value, matching is not performed. The distance may be exemplified by a registration point pair of the point cloud after registration and the curved surface of the target to be registered, and the preset threshold may be a preset registration distance threshold.
In the embodiment, after the point cloud to be registered and the curved surface which is subjected to registration and fusion are obtained, the curved surface of the target to be registered is segmented according to the surrounding grid of the point cloud to be registered, so that the segmented curved surface of the target to be registered is obtained, the data processing task amount can be effectively reduced, and the registration efficiency of the point cloud to be registered and the curved surface of the target to be registered is improved.
The point cloud data registration method in this embodiment may be implemented according to any one of the following point cloud data registration methods.
Fig. 1 is a flow chart of a point cloud data registration method according to an embodiment of the invention. Referring to fig. 1, the method comprises the steps of:
s101, obtaining a first rotation and translation matrix according to the point cloud to be registered and the curved surface of the target to be registered.
The method comprises the steps of obtaining point clouds to be registered and a curved surface of a target to be registered before obtaining a first rotational translation matrix according to the point clouds to be registered and the curved surface of the target to be registered. The point cloud to be registered and the curved surface of the target to be registered can be determined according to the method.
The obtaining of the first rotation-translation matrix according to the point cloud to be registered and the target curved surface to be registered may include performing segmentation processing on the target curved surface to be registered according to each point in the point cloud to be registered to obtain a target sub-curved surface corresponding to each point, obtaining a registration point pair according to each point in the point cloud to be registered and the target sub-curved surface corresponding to each point, and obtaining the first rotation-translation matrix according to the registration point pair. Referring to fig. 2, the segmented target curved surface to be registered may be segmented according to each point in the point cloud to be registered, so as to obtain a target sub-curved surface corresponding to each point.
In an embodiment of the present invention, segmenting the target curved surface to be registered according to each point in the point cloud to be registered, and obtaining the target sub-curved surface corresponding to each point may include: correspondingly generating a three-dimensional surrounding frame around each point in the point cloud to be registered; and segmenting the target curved surface to be registered according to the three-dimensional surrounding frame of each point to obtain a target sub-curved surface corresponding to each point.
Specifically, in the registration process of the point cloud to be registered and the target curved surface to be registered, the segmented target curved surface to be registered can be further segmented. As an example, a size of 5 × 5mm may be generated around each point in the point cloud to be registered3Or 9 x 9mm3Then, the segmented target curved surface to be registered is subjected to segmentation again according to the three-dimensional bounding box of each point, so that a target sub-point corresponding to each point is obtainedA curved surface. Therefore, the registration accuracy of the point cloud to be registered and the curved surface of the target to be registered can be improved.
In an embodiment of the present invention, obtaining the target sub-surface according to the triangular mesh point, and obtaining the registration point pair of the point cloud to be registered according to each point in the point cloud to be registered and the corresponding target sub-surface thereof may include: projecting each point in the point cloud to be registered on each triangular mesh of the corresponding target sub-curved surface to obtain a corresponding projection point; calculating the distance between each point in the point cloud to be registered and each corresponding projection point to obtain a plurality of first distances; and obtaining a registration point pair of the point cloud to be registered according to a plurality of first distances corresponding to each point in the point cloud to be registered.
Specifically, after the curved surface to be registered obtained according to the triangular mesh points is subjected to segmentation processing, the obtained target sub-curved surface may include a plurality of triangular mesh surfaces. In this embodiment, each point, for example, a point P in the point cloud to be registered may be projected onto each triangular mesh surface, for example, gj (j is 0,1, … …, m), of the target sub-curved surface corresponding to the point cloud to be registered, and it is determined whether the corresponding projected point falls within the triangular mesh surface. If yes, the distance D [ i ] from the point P to the projection point is saved, wherein the projection point can be obtained by adopting an interior angle sum method, a homodromous method, an area method, a gravity center method and the like.
Obtaining a registration point pair of the point cloud to be registered according to a plurality of first distances corresponding to each point in the point cloud to be registered may include: selecting a projection point corresponding to a first distance with the minimum value from a plurality of corresponding first distances and forming a point pair with each point according to each point in the point cloud to be registered to obtain a first point pair; discarding the point pairs with the distance between the first point pair larger than the first preset distance to obtain second point pairs; and obtaining a registration point pair of the point cloud to be registered according to the second point pair.
Specifically, taking P point as an example, if the value of D [5] in the plurality of first distances, such as D [1], D [2], D [3], … … and D [ i ], corresponding to the P point is the smallest, then the projection point, such as P5, corresponding to D [5] and P form a first point pair, and since the number of points in the point cloud to be registered is a plurality, a plurality of first point pairs can be obtained. Referring to fig. 2, after a plurality of first point pairs are obtained, point pairs with a distance from the projection point to the first point pair being greater than a first preset distance (e.g., 0.243-0.3mm) may be discarded, so as to obtain a second point pair, and a registration point pair of the point cloud to be registered is obtained according to the second point pair.
In the embodiment, the point cloud to be registered and the point with the minimum distance between the point in the point cloud to be registered and the projection point form the first point pair, and the point with the maximum distance between the first point pair and the projection point is abandoned, so that the accuracy of the registration point pair is improved, and the registration accuracy of the point cloud to be registered and the curved surface of the target to be registered is improved.
In an embodiment of the present invention, the second point pairs include a first sub-point cloud and a second sub-point cloud, where the points in the first sub-point cloud are all points in the point cloud to be registered, and the points in the second sub-point cloud are all points on the curved surface of the target to be registered, and obtaining the registration point pairs of the point cloud to be registered according to the second point pairs includes: determining a first zero point and a second zero point, wherein the coordinate value of the first zero point is the average value of the coordinate values of all points of the first sub-point cloud, and the coordinate value of the second zero point is the average value of the coordinate values of all points of the second sub-point cloud; and calculating the weighted distance between each point in the first sub-point cloud and the first zero point to obtain a first registration point, calculating the weighted distance between each point in the second sub-point cloud and the second zero point to obtain a second registration point, and pairing the first registration point and the second registration point to form a registration point pair.
The weighted distance can be calculated according to the following formula:
the weight dw [ i ] (D-distance [ i ]) T [ i ], which is the minimum distance of the projection of the corresponding point found, T [ i ] is the weighting coefficient, D is the threshold, i.e. the first predetermined distance, e.g. 0.243-0.3 mm.
R1[ i ] (P1[ i ] -L1) × dw [ i ], i ═ 0 … … N, N is the number of dot pairs, R1 is the first registration point, P1 is the first sub-point cloud, and L1 is the first zero point.
R2[ i ] (P2[ i ] -L2) × dw, i ═ 0 … … N, N is the number of dot pairs, R2 is the second registration point, P2 is the first sub-point cloud, and L2 is the first zero point.
It is understood that the obtained second point pair is composed of a point in the point cloud to be registered and its projection point, wherein the projection point is a point on the triangular mesh surface of the target sub-curved surface. Because the number of the points in the point cloud to be registered is multiple, the second point pair comprises a first sub-point cloud and a second sub-point cloud, wherein the points in the first sub-point cloud are all the points in the point cloud to be registered, and the points in the second sub-point cloud are all the points on the target sub-curved surface, namely the target curved surface to be registered. For example, a first sub-point cloud a [ X ] = { P1, P2, P3, … …, Px }, and a second sub-point cloud B [ X ] = { P11, P21, P31, … …, Px1}, where Px1 is a projection point of Px, and the first sub-point cloud a [ X ] and the second sub-point cloud B [ X ] may constitute X second point pairs such as (Px, Px 1).
Further, when obtaining the alignment point pair of the point cloud data according to the second point pair, the first sub-point cloud a [ X ] may be first obtained according to the first sub-point cloud a [ X ] respectively]P1, P2, P3, … …, Px, and a second sub-point cloud B [ X ]]The first sub-point cloud A [ X ] is obtained from the points P11, P21, P31, … … and Px1 in the first sub-point cloud]And a second sub-point cloud B [ X ]]First zero point P0 and second zero point P0’Then, the first sub-point cloud A [ X ] is processed]A first zero point P0 is calculated as a weighted distance between each of the points P1, P2, P3, … …, Px and the first zero point P0’[X]P10, P20, P30, … …, Px0, and a second sub-point cloud B [ X ″)]The points P11, P21, P31, … …, Px1 and the second zero point P0’Calculating the weighted distance to obtain a second registration point B’[X]P110, P210, P310, … …, Px10, resulting in registration point pair a’[X]-B’[X]Such as (Px0, Px 10).
In this embodiment, the registration point pair is obtained by calculating the weighted distance between each point in the first sub-point cloud and the second sub-point cloud and the first zero point and the second zero point respectively, so that the point impact factor with the closer distance between the corresponding points is larger, and the registration efficiency is improved.
S102, transforming the point cloud to be registered by using the first rotation and translation matrix to obtain a first transformed point cloud, and updating the point cloud to be registered by using the first transformed point cloud. In particular, pairs A may be paired according to the registration points’[X]-B’[X]Acquiring a first rotation and translation matrix T1 by a quaternion method, a singular value decomposition method and the like, and then processing a first sub-point cloud A in the point cloud to be registered by a first rotation and translation matrix T1[X]Transforming to obtain the first transformed point cloud Q [ X ]]And using the first transformed point cloud Q [ X ]]Updating a first sub-point cloud A [ X ] in the point cloud to be registered]。
S103, judging whether the current point cloud conversion times meet a first preset condition.
The condition that the current point cloud transformation times meet the first preset condition may include that the current point cloud transformation times are less than or equal to a first preset value (for example, 1 time). If the number of times of the current point cloud transformation exceeds the preset number (such as 5 times), the approximation step is skipped, namely S104 is skipped.
And S104, if so, obtaining an approximate rotation and translation matrix according to the first rotation and translation matrix by using an approximation algorithm, continuously and repeatedly transforming the point cloud to be registered by using the approximate rotation and translation matrix to obtain an approximate transformation point cloud until a second preset condition is met, and updating the point cloud to be registered by using the approximate transformation point cloud.
Specifically, referring to fig. 2, if the number of times of transformation of the current point cloud satisfies a first preset condition, if the updated point cloud to be registered, i.e., the first transformed point cloud Q [ X ], is obtained by once transforming the point cloud to be registered, i.e., is obtained by first transforming, an approximation algorithm may be used to iteratively obtain an approximation rotation-translation matrix T according to the first rotation-translation matrix T1.
Obtaining an approximation rotation translation matrix T according to the first rotation translation matrix T1 by using an approximation algorithm may include obtaining a rotation angle (theta X, theta y, theta z) and a translation amount h according to the first rotation translation matrix T1, then calculating a first average distance dMean of the point cloud to be registered before updating and a second average distance dMean 'of the first transformed point cloud Q [ X ], wherein the first average distance dMean of the point cloud to be registered is an average value of distances of all point pairs in the point pair to be registered, the second average distance of the first transformed point cloud Q [ X ] is an average value of distances of points on the first transformed point cloud Q [ X ] and corresponding points on the target surface to be registered in the point pair to be registered, and obtaining the approximation rotation translation matrix T according to the first average distance dMean, the second average distance dMean', the point (theta X, theta y, theta z) and the translation amount h.
Obtaining an approximate rotation transformation matrix T according to the first average distance dMean, the second average distance dMean ', the rotation angle (thetax, thetay, thetaz) and the translation amount h, wherein the obtaining of the approximate rotation transformation matrix T can comprise calculating a ratio of the second average distance dMean' to the first average distance dMean; the ratio is multiplied by the rotation angle (thetax, thetay, thetaz) and the translation amount h to obtain an approximate rotation and translation matrix T.
Further, performing continuous multiple transformation on the point cloud to be registered by using the approximate rotation translation matrix T to obtain an approximate transformation point cloud, and until a second preset condition is met, the method may include: transforming the updated point cloud to be registered by using the approximate rotation translation matrix T to obtain a second transformed point cloud; calculating a second average distance of the second conversion point cloud, and judging whether the second average distance is smaller than the second average distance calculated last time, wherein the second average distance of the second conversion point cloud is the average value of the distances between the point on the second conversion point cloud and the corresponding point on the target curved surface to be registered in the registration point pair; if the point cloud to be registered is smaller than the preset value, updating the point cloud to be registered by using the second transformation point cloud, and returning to the step of transforming the point cloud to be registered by using the approximate rotation translation matrix to obtain a second transformation point cloud; and if the current value is larger than or equal to the preset value, a second preset condition is met.
For example, a first transformed point cloud Q [ X ] is transformed using an approximating rotational translation matrix T]Converting to obtain a second conversion point cloud Q [ X ]]’Then, a second transformed point cloud Q [ X ] is calculated]’And then, whether the second average distance dMean 'is smaller than the second average distance dMean' calculated last time is determined. If so, the approximation rotation translation matrix T is utilized to carry out the second transformation on the point cloud Q [ X ]]’And transforming to obtain the updated point cloud to be registered, calculating a second average distance dMean ' of the updated point cloud to be registered, and judging whether the second average distance dMean ' is smaller than the second average distance dMean ' calculated last time. If the second average distance dMean ' is greater than or equal to the second average distance dMean ' calculated last time, it indicates that the second average distance dMean ' of the updated cloud of points to be registered is not decreased any more, and the updated cloud of points to be registered satisfies a second preset condition.
And S105, judging whether the registration is completed or not according to the point cloud to be registered and the curved surface of the target to be registered, and if not, returning to the step of obtaining a first rotation and translation matrix according to the point cloud to be registered and the curved surface of the target to be registered until the registration is completed.
The method for judging whether the registration is finished or not according to the point cloud to be registered and the curved surface of the target to be registered comprises the following steps:
judging whether the distance of the registration point pair is smaller than a preset registration distance threshold value or not, and judging whether the current point cloud transformation frequency is equal to a preset transformation frequency threshold value or not;
if the distance of the registration point pair is smaller than a preset registration distance threshold value, or the current point cloud transformation times are equal to a preset transformation time threshold value, judging that the registration is finished; otherwise, the registration is judged to be incomplete.
The registration can be completed by judging whether the distance between the updated point cloud to be registered and the curved surface to be registered is small enough or exceeds the set iteration times or not through the step.
Specifically, the updated point cloud to be registered may be updated to approximate transformation point cloud, and then it is determined whether the dMean' at this time is smaller than a set threshold, if so, it is indicated that the point cloud to be registered and the target curved surface to be registered satisfy the registration condition, and the registration is completed. Or judging whether the iteration times are greater than the set iteration times, if so, indicating that the point cloud to be registered and the target curved surface to be registered do not meet the registration condition, and finishing the registration. And if the two conditions are not met, iteration is carried out, then a first rotation translation matrix is obtained according to the updated point cloud to be registered and the curved surface of the target to be registered, and then the updated point cloud to be registered is transformed by using the first rotation translation matrix until the transformed point cloud to be registered and the curved surface of the target to be registered meet the registration conditions or exceed the iteration times.
According to the point cloud data registration method provided by the embodiment of the invention, a first rotation and translation matrix is obtained through point clouds to be registered and a curved surface of a target to be registered, the point clouds to be registered are converted by using the first rotation and translation matrix to obtain first conversion point clouds to update the point clouds to be registered, then whether the conversion times of the current point clouds meet a first preset condition or not is judged, if yes, an approximation algorithm is used for obtaining an approximation rotation and translation matrix according to the first rotation and translation matrix, the point clouds to be registered are continuously converted for multiple times by using the approximation rotation and translation matrix to obtain approximation conversion point clouds until a second preset condition is met, the point clouds to be registered are updated by using the approximation conversion point clouds to be registered, and then whether the point clouds to be registered and the curved surface of the target to be registered are registered or not judged, so that the registration speed from point to surface can be effectively improved.
Further, the present invention also provides a data processing method, including: cutting out a curved surface of a target to be registered according to the point cloud to be registered and the curved surface which is subjected to registration and fusion; and registering according to the point cloud data registration method to obtain registered point clouds, fusing the registered point clouds to a curved surface of a target to be registered to obtain a new curved surface which is subjected to registration and fusion, updating the point clouds to be registered, returning to the step of cutting out the curved surface of the target to be registered according to the point clouds to be registered and the curved surface which is subjected to registration and fusion until all point cloud data participate in registration and fusion.
According to the data processing method provided by the embodiment of the invention, the curved surface of the target to be registered is cut out based on the point cloud to be registered and the curved surface which is subjected to registration and fusion, the point cloud to be registered is registered by the point cloud data registration method to obtain the point cloud after registration, the point cloud after registration is fused to the curved surface of the target to be registered to obtain a new curved surface which is subjected to registration and fusion, the point cloud to be registered is updated, and the step of cutting out the curved surface of the target to be registered according to the point cloud to be registered and the curved surface which is subjected to registration and fusion is returned until all the point cloud data participates in registration and fusion is carried out.
Further, the present invention also provides a point cloud data registration apparatus 100, which includes a memory 101, a processor 102, and a computer program stored on the memory 101, wherein when the computer program is executed by the processor 102, the point cloud data registration method is implemented.
According to the point cloud data registration device provided by the embodiment of the invention, the point cloud to be registered and the curved surface of the target to be registered are accurately matched by the point cloud data registration method, and the point-to-surface registration speed is increased. Further, the present invention also provides an intraoral scanning device, and as shown in fig. 3, the intraoral scanning device 1000 includes the point cloud data registration apparatus 100 described above.
According to the intraoral scanning equipment disclosed by the embodiment of the invention, the point cloud data registration device on the intraoral scanning equipment realizes the accurate matching of the point cloud to be registered and the curved surface of the target to be registered by the point cloud data registration method, and the point-to-surface registration speed is improved.
According to the above-described embodiment, a preferable example is provided, which will be described in detail below.
In the scanning process, three-dimensional model data position errors caused by data errors or registration fusion errors often exist, and the errors of the previous frame are superposed when the next registration fusion is carried out according to the time sequence. And selecting the spatial position with the best registration as a first point cloud, searching a neighborhood point cloud (a second point cloud) on the spatial position for registration, and matching and fusing the neighborhood point cloud to the first curved surface until the second point cloud is completely traversed. And then selecting the position with the best registration fusion result as a new first point cloud, continuously searching the neighborhood point cloud on the spatial position for registration fusion, and circularly repeating the steps until all the point clouds are registered and fused on the first curved surface.
If the registration effect is not good, and possibly the original data has a problem, the original data is influenced by many factors, such as reflection, transmission, movement, unnecessary soft tissue (such as tongue) and the like to cause stripe extraction error, the depth position of the reconstructed point is wrong, and the error can slowly smooth the point in the registration, so that the best point cloud in the registration is selected to create the initial first curved surface, and compared with other initial point clouds and the initial curved surface, the superposition of errors can be reduced, and the registration accuracy is improved. In combination with the problems in real-time scanning, the method has the advantages that the spatial information and the registration fusion precision are combined and considered, on one hand, the spatial position with the highest precision is selected as the first point cloud through the registration fusion returned result, and the position precision is effectively prevented from being more and more inaccurate due to error superposition of a plurality of point clouds. On the other hand, the registration fusion of multiple neighborhood point clouds effectively smooths the superposition influence of large single original point cloud errors on the overall result of a single image caused by jitter, hardware jump and the like in the scanning process.
The general flow is shown in FIG. 4, where the reference numbers refer to
The preprocessing is performed before the first-level judgment, the second-level judgment and the third-level judgment.
The pretreatment process is shown in fig. 5, and the pretreatment steps are as follows:
(1) in the real-time scanning process, each point cloud (such as point cloud Points [ i ] of the ith frame), a corresponding rotational translation s matrix (such as a rotational translation matrix TransFromRealTime [ i ] of the ith frame) and a registration return value of each point cloud (namely, an average value of distances between corresponding Points of the point cloud and the overall point cloud after registration is completed is obtained, the smaller the value is, the better registration is obtained, the more accurate the position is, and if the registration return value of the ith frame is DistRalTime [ i ]) can be obtained according to the time sequence.
And in the average value of the distances between the corresponding points of the point cloud and the overall point cloud after the registration is finished, the overall point cloud refers to all the point clouds after the registration is finished before the point cloud is registered, the average value of the distances refers to the sum of the distances between each point in the point cloud and the corresponding point in the overall point cloud respectively divided by the number of the point cloud, then, the scanning is continued, and new point clouds continue to participate in the registration.
It should be noted that the point cloud 1 does not actually participate in registration, two sets of point cloud registration (existing point cloud and total point cloud) are required for registration, and only one set of point cloud is required for registration in the point cloud 1, so that the first return value is 10000 (a set maximum value). The collection of a single point cloud can reach about 2000 pieces, and the return value is only used for selecting the initial position, so that the first piece is not considered as the initial position and has no influence.
(2) And converting the Points [ i ] according to the corresponding rotation and translation matrix to obtain Points ' [ i ], calculating the gravity center of the Points ' [ i ] to obtain a gravity center coordinate pCenter [ i ], and calculating the MC grid surrounding frame of the Points ' [ i ] to obtain a surrounding grid pGrid [ i ]. The registration fusion is based on the idea of MC curved surface reconstruction, and not only needs to calculate the position of a point, but also needs to be accompanied by a topological relation to carry out subsequent MC curved surface reconstruction, so that the MC curved surface can be directly obtained according to the position of a triangular grid point and the topological information of the triangular grid point, and complete curved surface reconstruction is not needed.
(3) Calculating a neighborhood point cloud group of each point cloud: and defining that if the barycentric coordinate distance of the point cloud is less than 5mm and the contact ratio of the surrounding grids is more than 50%, the point cloud is the neighborhood point cloud. And numbering the neighborhood point clouds according to the order of the degree of coincidence, if a neighborhood point cloud group of a certain point cloud has N neighborhood point clouds, the number is 0,1,2 … … N-1 according to the degree of coincidence from large to small.
The barycentric coordinate distance is the distance between the barycentric coordinates of Points [ i ] and Points [ j ], and the contact ratio of the surrounding grids is the contact ratio of pGrid [ i ] and pGrid [ j ].
(4) And selecting the corresponding point cloud with the minimum DistRallTime [ i ] as an initial first point cloud, marking the attribute of the point cloud as False, marking other point clouds as tube, and performing MC curved surface reconstruction on the first point cloud to form a first curved surface.
Note that, a neighborhood point cloud group (second point cloud) of each point cloud (first point cloud) may be obtained by preprocessing.
Further, the primary judgment process is shown in fig. 6, and the primary judgment steps are as follows:
and finding a second point cloud (neighborhood point cloud) with the number of 0 of the first point cloud, judging whether the point cloud attribute is Ture, if so, entering a processing module, otherwise, judging the next second point cloud with the number of 1 until all the second point clouds are traversed.
Wherein, the processing module comprises the following steps:
and performing corresponding matrix transformation of the TransFromRealTime [ i ] on the second point cloud to obtain a transformed third point cloud. And then carrying out point-to-surface registration on the third point cloud and the first curved surface. And then judging whether the third point cloud and the first curved surface are matched, if so, carrying out registration matrix transformation on the third point cloud to obtain a fourth point cloud, calculating the average distance between the fourth point cloud and the nearest corresponding point of the first curved surface, namely Dist [ i ], fusing the fourth point cloud MC to the first curved surface, and marking the attribute of the second point cloud as False.
If not, the next numbered second point cloud is judged.
It should be noted that, the neighborhood point cloud group of the first point cloud can be registered and fused through the primary judgment, so that the superposition influence of a single original point cloud error on the overall result caused by the jitter, hardware jump and the like of a single image (second point cloud) in the scanning process is effectively smoothed.
Further, the secondary judgment steps are as follows: and after traversing processing is finished on the neighborhood point clouds (second point clouds) of the current first point cloud, performing two-stage judgment, finding out the point cloud with the valid corresponding Dist and the minimum value in the point cloud group, taking the point cloud as the first point cloud, and setting the Dist of the first point cloud as invalid. And returning to the first-level judgment to perform neighborhood traversal until the point cloud corresponding to the effective Dist cannot be found, and entering the third-level judgment, wherein the specific judgment steps are as follows:
and judging whether point clouds with the point cloud attribute of Ture exist, if not, completing registration and fusion of all the point clouds to generate a final three-dimensional curved surface model. If yes, randomly selecting a point cloud with the Ture point cloud attribute as a second point cloud, performing point-to-curved surface registration, and judging whether a registration result meets the matching condition. If yes, marking the point cloud attribute of the current second point cloud as False, then fusing the point cloud attribute to the first curved surface, taking the point cloud as the first point cloud, and returning to the first-level judgment again for neighborhood traversal. If not, the third-level judgment is carried out again, and other point clouds with the point cloud attribute of Ture are selected.
Referring to fig. 7, the point-to-surface registration process used in the above process is as follows:
(1) and segmenting a part (namely a second curved surface) of the first curved surface according to the surrounding grid of the point cloud to participate in the registration calculation from the point to the curved surface.
The first curved surface is gradually increased through multiple registration fusion, but the registration fusion of each step does not need all data of the first curved surface to participate, for example, a new point cloud is at the right molar position, the curved surface of the left molar does not need to be calculated, and otherwise, only the calculation amount is increased.
(2) And forming a small enclosing frame for each point on the point cloud, dividing the second curved surface into third curved surfaces according to the small enclosing frame, namely, each point on the point cloud corresponds to one third curved surface, and the whole point cloud corresponds to one third curved surface group.
The small bounding box can be selected according to a threshold value, and the threshold value is generally selected to be a bounding box of 5 × 5 or 9 × 9.
(3) And projecting the point P on the point cloud to each triangular mesh surface G [ j ] (j is 0,1, … …, m) of the corresponding third curved surface, judging whether the projected point falls in the triangular mesh, if so, storing the distance D [ i ] from the point on the point cloud to the projected point and the projected point coordinate Pro [ i ] (i is 0,1, … …, n), and knowing that n is less than m. The overall point cloud is obtained by fusion after each registration, the process is based on the TSDF fusion of the MC curved surface in the existing algorithm, whether a triangle exists in each mesh or not is determined, the relation between the triangle and the vertex of the triangle (namely the point on the edge of the mesh) is always determined, and a topological relation is always attached, so that which three points form one three mesh surface is determined.
The method for acquiring the projection point can be obtained by an interior angle sum method, an equidirectional method, an area method, a gravity center method and the like.
(4) And selecting the point with the minimum D [ i ] as the corresponding point of the point P, and removing the point pairs with the distance larger than 0.245mm (the points which are considered as the registration errors). Finally, forming a corresponding point pair of ai (effective point on the point cloud) -Bi (effective point on the third curved surface).
(5) And respectively calculating the coordinate value average points (Point _ a and Point _ B) of the two Point clouds of A [ i ] and B [ i ] as the zero points of the two Point clouds, and recalculating the Point cloud coordinates according to the weighted distance (the corresponding distance multiplied by the corresponding weighted value) of each Point and the Point _ a and the Point _ B as a new corresponding Point pair A '[ i ] -B' [ i ]. And then, obtaining a rotation and translation matrix Transform by methods such as a quaternion method, a singular value decomposition method and the like.
Wherein, the weighted value dw [ i ] (D-distance [ i ]) T [ i ], distance is the minimum distance found in step (4), T is a weighting coefficient, and D may be a threshold value determined according to the registration accuracy, similar test results or experience, i.e., a first preset distance, e.g., 0.243 to 0.3 mm.
It should be noted that the purpose of calculating the weighting distance is to control the influence of the corresponding point on the rotation and translation matrix, so that the influence factor of the point with the closer corresponding point distance is larger, thereby speeding up the registration iteration number.
(6) And (3) transforming the point cloud by using the rotation translation matrix, judging whether the distance of the point pair is small enough, if so, finishing the registration, otherwise, repeating the steps (2), (3), (4), (5) and (6) until the matching condition is met or the set iteration number is exceeded.
(7) When the rotation translation matrix transformation is performed for the first time, that is, when the iteration number is the first time, additional operations need to be performed, specifically, the following operations are performed: calculating rotation angles (theta x, theta y and theta z) through a rotation translation matrix Transform, and calculating approximation coefficients according to the average distance dMean before rotation transformation and the average distance dMean' after rotation transformation: the approximation coefficient is multiplied by the rotation angle (θ x, θ y, θ z) and the translation amount (Tx, Ty, Tz) to obtain a reversely calculated rotation/translation matrix Δ Transform. And continuously carrying out rotational translation matrix delta Transform on the transformed point cloud for multiple times until the dMean' is not reduced any more.
The purpose of the first rotation and translation processing is to perform quick iteration, and in practical engineering, the point cloud and the first curved surface can be matched only by performing multiple iterations, namely the iteration speed is too low.
As shown in fig. 8 and fig. 9, a set of data results processed by actual scanning is given, the accuracy obtained after real-time scanning is 0.098817mm and standard deviation is 0.092013mm, and the accuracy is improved to 0.041527mm and standard deviation is 0.041207mm after sorting and optimization. Therefore, the accuracy of the whole model can be effectively improved.
It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
Although embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are exemplary and not to be construed as limiting the present invention, and that changes, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (14)
1. A point cloud data registration method is characterized by comprising the following steps:
obtaining a first rotation translation matrix according to the point cloud to be registered and the target curved surface to be registered;
transforming the point cloud to be registered by using the first rotational translation matrix to obtain a first transformed point cloud, and updating the point cloud to be registered by using the first transformed point cloud;
judging whether the current point cloud conversion times meet a first preset condition or not;
if so, obtaining an approximate rotation translation matrix according to the first rotation translation matrix by using an approximation algorithm, continuously and repeatedly transforming the point cloud to be registered by using the approximate rotation translation matrix to obtain an approximate transformation point cloud until a second preset condition is met, and updating the point cloud to be registered by using the approximate transformation point cloud;
judging whether the registration is finished according to the point cloud to be registered and the curved surface of the target to be registered,
and if not, returning to the step of obtaining the first rotation translation matrix according to the point cloud to be registered and the target curved surface to be registered until the registration is completed.
2. The point cloud data registration method of claim 1, wherein the deriving a first rotational-translational matrix from the point cloud to be registered and the target surface to be registered comprises:
segmenting the target curved surface to be registered according to each point in the point cloud to be registered to obtain a target sub-curved surface corresponding to each point;
obtaining registration point pairs according to each point in the point cloud to be registered and the corresponding target sub-curved surface;
and obtaining a first rotation and translation matrix according to the registration point pairs.
3. The point cloud data registration method of claim 2, wherein the deriving an approximated rotation-translation matrix from the first rotation-translation matrix using an approximation algorithm comprises:
obtaining a rotation angle and a translation amount according to the first rotation and translation matrix;
calculating a first average distance of the point cloud to be registered before updating and a second average distance of the first conversion point cloud before updating, wherein the first average distance of the point cloud to be registered before updating is an average value of distances between points on the point cloud to be registered and corresponding points on a curved surface of a target to be registered in the registration point pair, and the second average distance of the first conversion point cloud is an average value of distances between points on the first conversion point cloud and corresponding points on the curved surface of the target to be registered in the registration point pair;
and obtaining an approximate rotation and translation matrix according to the first average distance, the second average distance, the rotation angle and the translation amount.
4. The point cloud data registration method of claim 3, wherein the performing continuous multiple transformations on the point cloud to be registered by using the approximate rotational-translation matrix to obtain an approximate transformation point cloud until a second preset condition is met comprises:
transforming the point cloud to be registered by using the approximate rotation and translation matrix to obtain a second transformed point cloud;
calculating a second average distance of the second conversion point cloud, and judging whether the second average distance is smaller than a second average distance calculated last time, wherein the second average distance of the second conversion point cloud is an average value of distances between a point on the second conversion point cloud and a corresponding point on the target curved surface to be registered in the registration point pair;
if the point cloud to be registered is smaller than the preset value, updating the point cloud to be registered by using the second transformation point cloud, and returning to the step of transforming the point cloud to be registered by using the approximate rotation translation matrix to obtain a second transformation point cloud;
and if the current value is larger than or equal to the preset value, a second preset condition is met.
5. The point cloud data registration method of claim 3, wherein deriving an approximated rotation transformation matrix from the first average distance, the second average distance, the rotation angle, and the translation amount comprises:
calculating the ratio of the second average distance to the first average distance;
and multiplying the ratio by the rotation angle and the translation amount to obtain an approximate rotation and translation matrix.
6. The point cloud data registration method of any one of claims 2 to 5, wherein the segmenting the target surface to be registered according to each point in the point cloud to be registered to obtain a target sub-surface corresponding to each point comprises:
correspondingly generating a three-dimensional surrounding frame around each point in the point cloud to be registered;
and segmenting the target curved surface to be registered according to the three-dimensional surrounding frame of each point to obtain a target sub-curved surface corresponding to each point.
7. The point cloud data registration method of any one of claims 2-5, wherein the target sub-surface is obtained from a triangular mesh point, wherein obtaining registration point pairs of the point cloud to be registered from each point in the point cloud to be registered and its corresponding target sub-surface comprises:
projecting each point in the point cloud to be registered on each triangular mesh of the corresponding target sub-curved surface to obtain a corresponding projection point;
calculating the distance between each point in the point cloud to be registered and each corresponding projection point to obtain a plurality of first distances;
and obtaining a registration point pair of the point cloud to be registered according to a plurality of first distances corresponding to each point in the point cloud to be registered.
8. The point cloud data registration method of claim 7, wherein obtaining the registration point pair of the point cloud to be registered according to a plurality of first distances corresponding to each point in the point cloud to be registered comprises:
selecting a projection point corresponding to a first distance with the minimum value from a plurality of corresponding first distances and forming a point pair with each point according to each point in the point cloud to be registered to obtain a first point pair;
discarding the point pairs with the distance between the first point pair larger than the first preset distance to obtain second point pairs;
and obtaining a registration point pair of the point cloud to be registered according to the second point pair.
9. The point cloud data registration method of claim 8, wherein the second point pairs comprise a first sub-point cloud and a second sub-point cloud, the points in the first sub-point cloud are all the points in the point cloud to be registered, and the points in the second sub-point cloud are all the points on the curved surface of the target to be registered, and wherein obtaining the registration point pairs of the point cloud to be registered according to the second point pairs comprises:
determining a first zero point and a second zero point, wherein the coordinate value of the first zero point is the average value of the coordinate values of all points of the first sub-point cloud, and the coordinate value of the second zero point is the average value of the coordinate values of all points of the second sub-point cloud;
and calculating the weighted distance between each point in the first sub-point cloud and the first zero point to obtain a first registration point, calculating the weighted distance between each point in the second sub-point cloud and the second zero point to obtain a second registration point, and pairing the first registration point and the second registration point to form a registration point pair.
10. The point cloud data registration method of claim 1, wherein the current point cloud transformation number satisfies a first preset condition, comprising:
the transformation times of the current point cloud are less than or equal to a first preset value.
11. The point cloud data registration method of claim 2, wherein determining whether registration is complete according to the point cloud to be registered and the target surface to be registered comprises:
judging whether the distance of the registration point pair is smaller than a preset registration distance threshold value or not, and judging whether the current point cloud transformation times are equal to a preset transformation time threshold value or not;
and if the distance of the registration point pair is smaller than a preset registration distance threshold value, or the current point cloud transformation times are equal to a preset transformation time threshold value, judging that the registration is finished, otherwise, judging that the registration is not finished.
12. A method of data processing, the method comprising:
cutting out a curved surface of the target to be registered according to the point cloud to be registered and the curved surface which is subjected to registration fusion;
the point cloud data registration method according to any one of claims 1 to 11, registering the point cloud to be registered and the target curved surface to be registered to obtain a registered point cloud;
and under the condition that the point cloud to be registered is matched with the curved surface of the target to be registered, fusing the point cloud after registration to the curved surface of the target to be registered to obtain a new curved surface which is subjected to registration fusion, updating the point cloud to be registered, returning to the step of segmenting the curved surface of the target to be registered according to the point cloud to be registered and the curved surface which is subjected to registration fusion, and till all point cloud data participate in registration fusion.
13. A point cloud data registration apparatus comprising a memory, a processor, and a computer program stored on the memory, which when executed by the processor, implements the point cloud data registration method of any of claims 1-11.
14. An intraoral scanning device comprising the point cloud data registration apparatus of claim 13.
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