A fast method and system for detecting only the surface patches of the skull mesh that intersect the tooth mesh

Abstract

This invention relates to a rapid method and system for detecting only the intersection points of the outer surface of a skull mesh and a dental mesh. This method and system utilize the characteristics of surface meshes to achieve the goal of detecting only the intersection points on the outer surface of the skull. This invention is particularly suitable for scenarios where the skull mesh is semi-transparent. In such scenarios, by filtering out the intersection results of the dental mesh with the alveolar portion and its surrounding areas, and only displaying the intersection results generated by the teeth protruding from the outer surface, visual interference can be greatly reduced, which is especially important for detecting the quality of tooth arrangement. Furthermore, this invention divides the detection into an initialization data stage and an intersection detection stage. The initialization data only needs to be calculated once and saved, and does not need to be recalculated during subsequent intersection detections. The intersection detection stage can calculate only the intersection results of the teeth being moved, which is fast and can achieve the effect of real-time display of the intersection results of the moved teeth.

Description

Technical Field

[0001] This invention relates to the field of digital dental imaging technology, and more particularly to a rapid method for detecting only the intersecting surfaces of the skull mesh and the dental mesh. Background Technology

[0002] The skull surface mesh is recessed inward at the tooth area, forming a local double-layer surface mesh structure (alveolar mesh). At the molars, there is an irregular multi-layer structure, which makes it a challenging task to detect the intersection of the tooth mesh with the skull surface. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to design a rapid method for detecting only the intersecting surfaces of the skull mesh and the tooth mesh, which can quickly remove excess surfaces and solve the existing technical problems.

[0004] To solve the above-mentioned technical problems, the present invention provides a rapid method for detecting only the intersecting surface of the skull mesh and the dental mesh, comprising the following steps:

[0005] Step S1: Initialization, specifically including:

[0006] Step S11: Calculate a centroid for each tooth mesh; the initial centroid calculated here should still be used after subsequent teeth are moved, and it should not be calculated repeatedly, otherwise it will lead to poor filtering effect in step S24;

[0007] Step S12: Calculate the centroid distance between teeth 3-3;

[0008] Step S13: Establish a fast intersection detection data structure for the upper and lower skulls respectively, and the upper and lower skull meshes need to be separate;

[0009] Step S14: Calculate the normal vector and the centroid of the corresponding triangular facet for each facet on the skull mesh;

[0010] Step S15: Use the centroid and normal vector from step S14 to construct a line segment with a length λ times the distance between the centroids of the teeth in step S12;

[0011] Step S16: Use the intersection detection data structure of the corresponding skull established in step S13 to perform intersection detection with each line segment established in step S15, filter out the face patches with intersection detection results, and finally take the largest connected component to filter out independent fragment meshes; finally, establish intersection detection data structures for the filtered upper and lower skull meshes respectively.

[0012] Step S2: Intersection detection, specifically including:

[0013] Step S21: Establish an intersection detection data structure for the tooth meshes that need to be detected, and perform intersection detection with the intersection detection data structure of the corresponding skull established in step S16;

[0014] Step S22: Traverse each vertex of the upper surface of the tooth mesh corresponding to the intersection facet of each tooth and skull in step S21;

[0015] Step S23: Connect each vertex in step S22 to the centroid calculated in step S11 of the tooth mesh to create a line segment;

[0016] Step S24: Perform intersection detection using the line segment generated in step S23 and the corresponding unfiltered skull intersection detection data structure established in step S13. If the number of intersection results is greater than or equal to 2, it indicates that the line segment has passed through the alveolar bone and skull double-layer mesh, and the corresponding vertex is exposed on the outer surface of the skull. The intersection facet corresponding to the tooth mesh facet to which the vertex belongs is retained and aligned with the facet on the skull mesh. The line segment generated by connecting the tooth mesh vertex near the alveolar bone edge to the centroid and intersecting the original skull mesh corresponding to the intersection detection data structure in step S13 will not pass through the double-layer mesh to generate two intersection points, thus achieving the filtering effect. After filtering, normal teeth no longer have intersection detection results.

[0017] Furthermore, the intersection detection data structure can be an OBB tree or an AABB tree. AABB trees are faster to construct, but filter out more potential intersection objects, resulting in slower detection; conversely, OBB trees are slower to construct because they require calculating bounding boxes, but detection is faster. Since this invention only requires constructing the tree twice and then performing a large number of intersection checks, the OBB tree is a better choice for this invention.

[0018] Furthermore, in step S14, the cranial mesh includes the alveolar portion.

[0019] Furthermore, in step S15, the distance λ between the centroids of the teeth is greater than the alveolar diameter, allowing the normal vector segment of the alveolar portion to intersect with the alveolar itself. There is no strict range for the value of λ, but if it is too large, it will cause many insignificant surfaces to intersect, increasing the computational load and reducing speed.

[0020] The present invention also provides a rapid system for detecting only the intersection of the outer surface of the skull mesh and the dental mesh, comprising:

[0021] One or more processors; and

[0022] One or more memories storing a computer-executable program, wherein the aforementioned method is performed when the processor executes the computer-executable program.

[0023] The beneficial effects of this invention are as follows: The method and system of this invention for rapidly detecting only the intersecting surfaces of the skull mesh and the tooth mesh utilize the characteristics of the surface mesh to achieve the goal of detecting only the intersecting surfaces of the skull's outer surface. This invention is particularly suitable for scenarios where the skull mesh is semi-transparent. In such scenarios, by filtering out the intersection results of the tooth mesh with the alveolar portion and its surrounding surfaces, and only displaying the intersection results generated by the teeth protruding from the outer surface, visual interference can be greatly reduced, which is especially important for detecting the quality of tooth arrangement. In addition, this invention divides the detection into an initialization data stage and an intersection detection stage. The initialization data only needs to be calculated once and saved, and does not need to be calculated again in subsequent intersection detections. The intersection detection stage can calculate only the intersection results of the teeth being moved, which is fast and can achieve the effect of displaying the intersection results of the moved teeth in real time. Attached Figure Description

[0024] The specific embodiments of the present invention will be further explained below with reference to the accompanying drawings.

[0025] Figure 1 A schematic diagram of the skull mesh and part of the tooth mesh;

[0026] Figure 2 A close-up diagram of the alveolar mesh formed by the depressions;

[0027] Figure 3 A schematic diagram of the skull mesh after initial filtration to remove most of the alveolar mesh;

[0028] Figure 4 for Figure 3 A diagram illustrating the intersection of the skull and each tooth;

[0029] Figure 5 A schematic diagram showing that normal teeth no longer have intersecting test results after filtering (I);

[0030] Figure 6 Schematic diagram (II) showing that normal teeth no longer have intersecting test results after filtering. Detailed Implementation

[0031] like Figure 1 and Figure 2 As shown, the target skull is a surface grid. This invention achieves the detection of only the intersection of the teeth and the skull surface, while eliminating the intersection of the teeth and the alveolar grid surface.

[0032] The rapid method for detecting only the intersection of the outer surface of the skull mesh and the dental mesh in this embodiment includes the following steps:

[0033] Step S1: Initialization, specifically including:

[0034] Step S11: Calculate a centroid for each tooth mesh; the initial centroid calculated here should still be used after subsequent teeth are moved, and it should not be calculated repeatedly, otherwise it will lead to poor filtering effect in step S24;

[0035] Step S12: Calculate the centroid distance between teeth 3-3;

[0036] Step S13: Establish a fast intersection detection data structure for the upper and lower skulls. In this embodiment, OBBTree is used, and the upper and lower skull meshes need to be separated.

[0037] Step S14: Calculate the normal vector and the centroid of the corresponding triangular facet for each facet on the skull mesh, which includes the alveolar portion;

[0038] Step S15: Use the centroid and normal vector from step S14 to construct a line segment with a length λ times the distance between the centroids of the teeth in step S12. The distance between the centroids of the teeth is greater than the diameter of the alveolar bone, so that the line segment of the normal vector of the alveolar bone can intersect with the alveolar bone itself. There is no strict range for the value of λ, but if it is too large, it will cause many insignificant patches to intersect, increasing the amount of calculation and reducing the speed.

[0039] Step S16: Perform intersection detection between the OBB Tree of the corresponding skull established in Step S13 and each line segment established in Step S15, filter out the faces with intersection detection results, and finally filter out independent fragment meshes by taking the largest connected component. The effect is as follows: Figure 3 As shown; finally, OBB Trees are also built for the filtered upper and lower skull meshes;

[0040] Step S2: Intersection detection, specifically including:

[0041] Step S21: Establish an OBB Tree for the intersecting tooth meshes to be detected, and perform intersection detection with the intersection detection data structure of the corresponding skull established in step S16. The result is as follows. Figure 4 As shown;

[0042] Step S22: Traverse each vertex of the upper surface of the tooth mesh corresponding to the intersection facet of each tooth and skull in step S21;

[0043] Step S23: Connect each vertex in step S22 to the centroid calculated in step S11 of the tooth mesh to create a line segment;

[0044] Step S24: Perform intersection detection between the line segment generated in Step S23 and the corresponding unfiltered skull OBB Tree established in Step S13. If the number of intersection results is greater than or equal to 2, it indicates that the line segment has passed through the alveolar and skull double-layer mesh, and the corresponding vertex is exposed on the outer surface of the skull. The intersection facet corresponding to the tooth mesh facet to which this vertex belongs is retained and aligned with the facet on the skull mesh. The line segment generated by connecting the tooth mesh vertex near the alveolar edge to the centroid and intersecting the original skull mesh corresponding to the OBB Tree in Step S13 will not pass through the double-layer mesh to generate two intersection points, thus achieving the filtering effect. After filtering, normal teeth no longer have intersection detection results. Figure 5 and Figure 6 As shown.

[0045] While AABB Tree construction is faster, it filters out more potentially intersecting objects, resulting in slower detection. Conversely, OBB Tree construction is slower because it requires calculating bounding boxes, but detection is faster. Since this invention only requires constructing the Tree twice and then performing a large number of intersection checks, this implementation chooses OBB Tree.

[0046] This embodiment also provides a system for rapidly detecting only the intersection of the outer surface of the skull mesh and the dental mesh, comprising:

[0047] One or more processors; and

[0048] One or more memories storing a computer-executable program, wherein the aforementioned method is performed when the processor executes the computer-executable program.

[0049] This embodiment of a rapid method and system for detecting only the intersection of the outer surface of the skull mesh and the tooth mesh utilizes the characteristics of the surface mesh to achieve the goal of detecting only the intersection of the outer surface of the skull. This invention is particularly suitable for scenarios where the skull mesh is semi-transparent. In such scenarios, by filtering out the intersection results of the tooth mesh with the alveolar portion and its surrounding areas, and only coloring the intersection results generated by the teeth protruding from the outer surface, visual interference can be greatly reduced, which is especially important for detecting the quality of tooth arrangement. Furthermore, this embodiment divides the detection into an initialization data stage and an intersection detection stage. The initialization data only needs to be calculated once and saved; subsequent intersection detections do not require recalculation. The intersection detection stage can calculate only the intersection results of the teeth being moved, resulting in high speed and the ability to display the intersection results of the moved teeth in real time.

[0050] Many specific details have been set forth in the foregoing description to provide a thorough understanding of the present invention. However, the above description is merely a preferred embodiment of the present invention, and the present invention can be implemented in many other ways different from those described herein. Therefore, the present invention is not limited to the specific embodiments disclosed above. Furthermore, any person skilled in the art can make many possible variations and modifications to the technical solutions of the present invention, or modify them into equivalent embodiments, using the methods and techniques disclosed above, without departing from the scope of the present invention. Any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention, without departing from the content of the present invention, shall still fall within the protection scope of the present invention.

Claims

1. A rapid method for detecting only the intersection patch between the outer surface of a skull mesh and a dental mesh, characterized in that: Includes the following steps: Step S1: Initialization, specifically including: Step S11: Calculate a centroid for each tooth mesh; Step S12: Calculate the centroid distance between teeth 3-3; Step S13: Establish a fast intersection detection data structure for the upper and lower skulls respectively, and the upper and lower skull meshes need to be separate; Step S14: Calculate the normal vector and the centroid of the corresponding triangular facet for each facet on the skull mesh; Step S15: Use the centroid and normal vector from step S14 to establish a line segment with a length λ times the distance between the centroids of the teeth in step S12. The distance between the centroids of the teeth is greater than the diameter of the alveolar bone, so that the line segment of the normal vector of the alveolar bone can intersect with the alveolar bone itself. Step S16: Use the intersection detection data structure of the corresponding skull established in step S13 to perform intersection detection with each line segment established in step S15, filter out the face patches with intersection detection results, and finally take the largest connected component to filter out independent fragment meshes; finally, establish intersection detection data structures for the filtered upper and lower skull meshes respectively. Step S2: Intersection detection, specifically including: Step S21: Establish an intersection detection data structure for the tooth meshes that need to be detected, and perform intersection detection with the intersection detection data structure of the corresponding skull established in step S16; Step S22: Traverse each vertex of the upper surface of the tooth mesh corresponding to the intersection of each tooth and skull in step S21; Step S23: Connect each vertex in step S22 to the centroid calculated in step S11 of the tooth mesh to create a line segment; Step S24: Perform intersection detection using the line segment generated in step S23 and the corresponding unfiltered skull intersection detection data structure established in step S13. If the number of intersection results is greater than or equal to 2, it indicates that the line segment has passed through the alveolar bone and skull double-layer mesh, and the corresponding vertex is exposed on the outer surface of the skull. Retain the intersection facet corresponding to the tooth mesh facet to which the vertex belongs to the facet on the skull mesh.

2. The method for rapidly detecting only the intersecting surface of the skull mesh and the dental mesh according to claim 1, characterized in that: The intersection detection data structure is either an OBB tree or an AABB tree.

3. The method for rapidly detecting only the intersection surface of the outer surface of the skull mesh and the dental mesh according to claim 1, characterized in that: In step S14, the cranial mesh includes the alveolar portion.

4. A rapid system for detecting only the intersection of the outer surface of a skull mesh and a dental mesh, characterized in that: include: One or more processors; as well as One or more memories storing a computer-executable program, which, when executed by the processor, performs the method of any one of claims 1-3.

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