Three-dimensional graph recognition method, device and apparatus and storage medium

Abstract

The invention discloses a three-dimensional graph recognition method, device and apparatus and a storage medium. The method comprises: obtaining point cloud of a target three-dimensional model, preprocessing the point cloud of the target three-dimensional model to obtain a target two-dimensional array and a mapping relationship between points in the target three-dimensional model and two-dimensional pixels, obtaining a target two-dimensional image corresponding to the target three-dimensional model based on the target two-dimensional array and the feature data of each point in the point cloudof the target three-dimensional model, performing image recognition on the target two-dimensional image based on a two-dimensional image recognition network, and mapping the target two-dimensional image recognition result to a target three-dimensional model to obtain a target three-dimensional graph recognition result. By utilizing the technical scheme provided by the invention, a three-dimensional graph processing problem can be converted into a two-dimensional image processing problem. By utilizing the characteristics of more mature two-dimensional image recognition, high efficiency and accurate recognition, a more reliable three-dimensional graph recognition result is obtained, less training data is needed, and the cost and time are saved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a three-dimensional graphic recognition method, device, equipment and storage medium. Background technique [0002] In today's technical background, 3D scanning technology has become more and more perfect. The application of 3D scanning technology has achieved remarkable results in various industries. In the medical industry, it has become one of the important technologies to help doctors conduct pathological analysis and anatomical research. For example, when performing dental implant, restoration, or orthodontic treatment planning, it is necessary to identify the crown, gingiva, cervical line, undercut area between crowns, and key anatomical features of the crown from the 3D jaw model. For example, from the 3D data obtained by scanning the gypsum model of teeth and jaws, the obvious noise information in the gypsum model (caused by the air bubbles generated during the gypsum perf...

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Problems solved by technology

For example, when performing dental implant, restoration, or orthodontic treatment planning, it is necessary to identify the crown, gingiva, cervical line, undercut area between crowns, and key anatomical features of the crown from the 3D jaw model. For example, from the 3D data obtained by scanning the gypsum model of teeth and jaws, the obvious noise information in the gypsum model (caused by the air bubbles generated during the gypsum perfusion process or the depressed area caused by the bubble rupture) can be identified. However, the current 3D graphic recognition technology still has limitations. Complex, 3D graphic recognition based on deep learning requires more training data and is not flexible enough. Therefore, a more effective solution needs to be provided

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