3D Tooth Model Gum Line Detection Using Curvature Fitting
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Solution Overview
Problem
Current orthodontic data processing in invisible orthodontics is labor-intensive, time-consuming, and lacks automation, leading to low efficiency and poor user experience, particularly in identifying gum lines of 3D tooth models.
Innovation Solution
A method and system utilizing geometric algorithms and artificial intelligence to automatically identify gum lines by extracting feature points, matching fitting reference lines, and smoothing initial fitted lines using dimensionality reduction algorithms, reducing manual labor and enhancing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2D images or simple 3D scans are used for teeth representation, then data acquisition is fast and easy, but accuracy in representing complex tooth structures and gum lines is insufficient
Solution Approach 1:
The patent segments the teeth model into multiple components (individual teeth, gum lines,咬合 surfaces) and processes each segment separately through multiple scanning passes. This allows accurate capture of complex geometries while maintaining manageable system complexity by treating different regions with appropriate scanning strategies.
Solution Approach 2:
The patent transitions from 2D imaging to multi-dimensional 3D scanning with multiple degrees of freedom (X, Y, Z positions plus rotation angles). This dimensional expansion enables accurate capture of complex tooth surfaces and gum line geometries that cannot be represented in 2D, while the modular scanner design keeps system complexity manageable.
2Measurement precision
If multiple scans from different angles are performed to improve accuracy, then teeth model precision increases, but processing time and computational load increase
Solution Approach 1:
The patent performs preliminary alignment and rough registration of multiple scans before detailed processing. By pre-positioning scan data in approximate correct locations and orientations, the system reduces the computational burden of subsequent precise alignment, thereby decreasing total processing time while maintaining accuracy.
Solution Approach 2:
The patent creates intermediate simplified representations (digital impressions, preliminary 3D models) that copy essential geometric features from raw scan data. These simplified copies enable faster processing and visualization while preserving critical accuracy information needed for dental applications.
3Manufacturing precision
If detailed 3D scanning is performed to accurately capture gum lines and tooth surfaces, then model fidelity improves, but data processing complexity and computational resources increase
Solution Approach 1:
The patent extracts and isolates specific features (gum lines, tooth surfaces,咬合 contacts) from the complete 3D scan data through specialized processing algorithms. By separating gum line extraction from general surface modeling, the system achieves high precision for each feature type while managing processing complexity through modular, specialized routines rather than attempting to process all data uniformly.
Solution Approach 2:
The patent applies different processing qualities and detail levels to different regions of the teeth model. High-resolution processing is applied specifically to critical areas like gum lines and咬合 surfaces, while less critical regions use standard processing. This local differentiation maintains manufacturing precision where needed while reducing overall computational complexity.
Data Source
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AI summary
A method and system for identifying a gum line of a 3D tooth model, a device, and a storage medium are disclosed. The method includes: extracting a plurality of feature points from the 3D tooth model based on a geometric algorithm of curvature, pre-processing each of the feature points, and outputting a feature contour point group (S1); obtaining a first fitting reference line from a pre-stored fitting reference line pool with the first fitting reference line being matched with a shape parameter (S2); performing an iterative operation on the first fitting reference line based on the feature contour point group, to generate an initial fitted line (S3); and performing smoothing processing on the initial fitted line by using a dimensionality reduction algorithm, to output the gum line (S4).