Triangular mesh optimization method for denture model

Abstract

The invention provides a triangular mesh optimization method for a denture model. The triangular mesh optimization method comprises the following steps: importing a model, obtaining an offset point, calculating a projection direction of the offset point, calculating a projection point distance [delta], determining [delta] of all peaks, sorting the peaks from large to small, translating and freezing the peaks, sequentially translating all points for carrying out mesh optimization, and the like. The invention belongs to the technical field of computer graphics processing. According to the triangular mesh optimization method for the denture model provided by the invention, a long-narrow trianglecan be removed on the premise that the shape of an original model does not greatly change and does not contract along with an increase of iterative times; and the optimization process does not involve element deletion to ensure that the optimization effect is not limited by the number of the peaks or the mesh density.

Description

technical field [0001] The invention belongs to the technical field of computer graphics processing, and in particular relates to a method for optimizing a denture model triangular mesh. Background technique [0002] In order to obtain the oral information of the patient, CAD / CAM technology adopts a method of reverse modeling: the probe head of the 3D scanner is inserted into the patient's oral cavity for scanning, so that a tooth similar in shape to the tooth can be obtained Point cloud model. In order to represent the shape of teeth as accurately as possible, this point cloud model is often very large in number and cannot be used directly. A common processing method is to directly delete some points according to a certain ratio, so that the model points become sparse, but it can still represent the shape of the original model. After obtaining this simplified point cloud model, the topological relationship can be established and converted into a triangular mesh model. The...

AI Technical Summary

Problems solved by technology

The triangular mesh model represents the shape of the original model, but the triangular mesh model obtained through the data points inevitably has various noises and disturbances, and the quality of the triangular mesh will be poor (there are a large number of long and narrow triangular pieces), and the long and narrow Triangular patches are not conducive to display, will affect the entire shape of the model, and will also have a negative impact on subsequent mesh operations

However, as the number of iterations increases, due to the lack of precision control, it is easy to cause the degradation and loss of the original model features, resulting in the over-optimized mesh

For example, in Laplace optimization, if the number of iterations is too many, the original model will be deformed in a large range, and the model will appear "indented". The disturbance, that is to say, the smoother position becomes not smooth

[0007] Laplace optimization and Laplace-based Taubin optimization are either limited by the number of iterations or the shape of the model; Wang Kunling mentioned the folded edge or folded triangle method, which deletes edge elements or triangle elements, mesh It is more suitable when the number of model points is large, but when the grid is relatively sparse, the deletion of each grid element will greatly affect the shape of the model

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