METHOD OF ALIGNING INTRA-ORAL DIGITAL 3D MODELS

• US 20160070821A1
• Filed: 09/08/2014
• Published: 03/10/2016
• Est. Priority Date: 09/08/2014
• Status: Active Grant

• Current Assignee: Solventum Intellectual Properties Company (Solventum Corp.)
• Original Assignee: 3M Innovative Properties Company (3M Company)
• Inventors: Somasundaram, Guruprasad, Ribnick, Evan J., Sivalingam, Ravishankar, Scott, Shannon D., Golnari, Golshan, Eid, Aya
• Granted Patent: US 10,192,003 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61C 13/0004 Computer-assisted sizing or machining of dental prostheses
• CPC Class Codes: A61C 2007/004 Automatic construction of a set of axes for a tooth or a plurality of teeth
• CPC Class Codes: A61C 7/00 Orthodontics, i.e. obtaining or maintaining the desired position of teeth, e.g. by straightening, evening, regulating, separating, or by correcting malocclusions
• CPC Class Codes: A61C 7/002 Orthodontic computer assisted systems
• CPC Class Codes: A61C 9/004 Means or methods for taking digitized impressions
• CPC Class Codes: G06F 30/00 Computer-aided design [CAD]

Abstract

Methods for aligning a digital 3D model of teeth represented by a 3D mesh to a desired orientation within a 3D coordinate system. The method includes receiving the 3D mesh in random alignment and changing an orientation of the 3D mesh to align the digital 3D model of teeth with a desired axis in the 3D coordinate system. The methods can also detect a gum line in the digital 3D model to remove the gingiva from the model.

Claims

1. A method for aligning a digital 3D model of teeth, comprising steps of:

receiving a digital 3D model of teeth represented by a 3D mesh in random alignment;

computing surface normals for at least a subset of each face of the mesh;

computing an aggregate of the surface normals to determine a representative normal direction; and

computing and applying a rotation matrix to align the representative normal direction with a desired axis within a 3D coordinate system.

2. The method of claim 1, wherein the computing the aggregate step comprises calculating one of a mean, a sum, or a median of the surface normals.

3. A method for aligning a digital 3D model of teeth, comprising steps of:

receiving a digital 3D model of teeth represented by a 3D mesh in random alignment;

computing a grid of vectors aligned with a desired axis within a 3D coordinate system;

projecting the vectors through the mesh to count a number of intersections of the vectors with faces of the mesh;

computing a cost function for the mesh based upon the number of intersections; and

rotating the mesh and repeating the projecting and the computing the cost function steps until the cost function is maximized to align the mesh with the desired axis.

4. A method liar aligning a digital 3D model of teeth, comprising steps of:

receiving a digital 3D model of teeth represented by a 3D mesh in random alignment;

applying a regression or plane fit algorithm to data points in the mesh to find a plane that best fits the digital 3D model of teeth; and

finding a normal direction of the plane to align the mesh with a desired axis within a 3D coordinate system.

5. The method of claim 4, further comprising subtracting a mean of the data points to centralize the data points.

6. The method of claim 4, wherein the finding step comprises using a principal component analysis (PCA) or a Robust PCA to find the normal direction.

7. A method for modifying and aligning a digital 3D model of teeth, comprising steps of:

receiving a digital 3D model of teeth with associated gingiva represented by a 3D mesh in random alignment;

detecting a gum line in the digital 3D model;

fitting a surface to points of the gum line using a modified ridge estimator;

removing vertices on a side of the surface corresponding with the gingiva to remove the gingiva from the digital 3D model; and

aligning the digital 3D model without the gingiva with a desired axis within a 3D coordinate system.

8. The method of claim 7, wherein the detecting step comprises using a classification function based on mesh surface shape properties of the mesh to detect the gum line.

9. The method of claim 7, wherein the detecting step comprises using a correlation on curvature function to detect the gum line.

10. The method of claim 7, wherein the alignment step comprises:

computing surface normals for at least a subset of each face of the mesh;

computing an aggregate of the surface normals to determine a representative normal direction; and

computing and applying a rotation matrix to align the representative normal direction with a desired axis within a 3D coordinate system.

11. The method of claim 7, wherein the alignment step comprises:

computing a grid of vectors aligned with the desired axis within the 3D coordinate system;

projecting the vectors through the mesh to count a number of intersections of the vectors with faces of the mesh;

computing a cost function for the mesh based upon the number of intersections; and

rotating, the mesh and repeating the protecting and the computing the cost function steps until the cost function is maximized to align the mesh with the desired axis.

12. The method of claim 7, wherein the alignment step comprises:

applying a regression or plane fit algorithm to data points in the mesh to find the occlusal plane of the digital 3D model; and

finding a normal direction of the occlusal plane to align the mesh with the desired axis within the 3D coordinate system.

13. A system for aligning a digital 3D model of teeth, comprising:

a module for receiving a digital 3D model of teeth represented by a 3D mesh in random alignment;

a module for computing surface normals for at least a subset of each face of the mesh;

a module for computing an aggregate of the surface normals to determine a representative normal direction; and

a module for computing and applying a rotation matrix to align the representative normal direction with a desired axis within a 3D coordinate system.

14. The system of claim 13, wherein the module for computing the aggregate comprises a module for calculating one of a mean, a sum, or a median of the surface normals.

15. A system for aligning a digital 3D model of teeth, comprising:

a module for receiving a digital 3D model of teeth represented by a 3D mesh in random alignment;

a module for computing a grid of vectors aligned with a desired axis within a 3D coordinate system;

a module for projecting the vectors through the mesh to count a number of intersections of the vectors with faces of the mesh;

a module for computing, a cost function for the mesh based upon the number of intersections; and

a module for rotating the mesh and repeating the projecting and the computing the cost function until the cost function is maximized to align the mesh with the desired axis.

16. A system for aligning a digital 3D model of teeth, comprising:

a module for receiving a digital 3D model of teeth represented by a 3D mesh in random alignment;

a module for applying a regression or plane lit algorithm to data points in the mesh to find an occlusal plane of the digital 3D model of teeth; and

a module for finding a normal direction of the occlusal plane to align the mesh with a desired axis within a 3D coordinate system.

17. The system of claim 16, further comprising a module for subtracting a mean of data points to centralize the data points.

18. The system of claim 16, wherein the module for finding comprises a module for using a principal component analysis (PCA) or a Robust PCA to find the normal direction.

19. A system for modifying and aligning a digital 3D model of teeth, comprising:

a module for receiving a digital 3D model of teeth with associated gingiva represented by a 3D mesh in random alignment;

a module for detecting a gum line in the digital 3D;

a module for fitting a surface to points of the gum line using, a modified ridge estimator;

as module for removing vertices on a side of the surface corresponding with the gingiva to remove the gingiva from the digital 3D model; and

a module for aligning the digital 3D model without the gingiva with a desired axis within a 3D coordinate system.

20. The system of claim 19, wherein the module for detecting comprises a module for using a classification function based on mesh surface shape properties of the mesh to detect the gum line.

21. The system of claim 19, wherein the module for detecting comprises a module for using a correlation on curvature function to detect the gum line.

22. The system of claim 19, wherein the module for aligning comprises:

a module for computing surface normals for at least a subset of each face of the mesh;

a module for computing an aggregate of the surface normals to determine a representative normal direction; and

a module for computing and applying a rotation matrix to align the representative normal direction with a desired axis within as 3D coordinate system.

23. The system of claim 19, wherein the module for aligning comprises:

a module for computing a grid of vectors aligned with the desired axis within the 3D coordinate system;

a module for projecting the vectors through the mesh to count a number of intersections of the vectors with faces of the mesh;

a module for computing a cost function for the mesh based upon the number of intersections; and

a module for rotating the mesh and repeating the projecting and the computing the cost function until the cost function is maximized to align the mesh with the desired axis.

24. The system of claim 19, wherein the module for aligning, comprises:

a module for applying a regression or plane fit algorithm to data points in the mesh to find the occlusal plane of the digital 3D model; and

a module for finding a normal direction of the occlusal plane to align the mesh with the desired axis within the 3D coordinate system.