Aligning 3D Dentition Models to Camera Images via Iterative Virtual Camera Optimization
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Solution Overview
Problem
Existing methods for aligning three-dimensional models of a patient's dentition with camera images result in imprecise and unreliable visualizations due to deviations in camera positioning, leading to unrealistic aesthetic impressions.
Innovation Solution
A computer-implemented method that iteratively optimizes the positioning of a virtual camera to minimize deviation between detected features in camera images and rendered images using edge detection and color-based tooth likelihood determination, ensuring precise alignment of the three-dimensional model within the camera's field of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a three-dimensional model of dentition is aligned to a camera image using estimated camera positioning, then the visualization of dental situations can be achieved, but the alignment precision and reliability are insufficient leading to unrealistic aesthetic impressions
Solution Approach 1:
The patent implements an iterative optimization process where the system continuously compares detected features from the camera image with corresponding features from the rendered three-dimensional model, calculates alignment deviations, and adjusts the virtual camera positioning to minimize these deviations. This feedback loop significantly improves alignment precision and visualization reliability compared to using only initial camera position estimates.
Solution Approach 2:
The patent replaces reliance on physical camera positioning data with a computational approach using image processing and feature matching algorithms. By substituting the mechanical camera positioning system with an optical-computational system that detects edges, contours, and tooth features directly from images, the method achieves more reliable and precise alignment that is not limited by physical measurement errors.
2Measurement precision
If feature detection and iterative optimization are used to improve alignment precision, then realistic visualization is achieved, but the computational complexity and processing time increase
Solution Approach 1:
The patent divides the complex alignment task into multiple independent stages: initial camera positioning estimation, edge detection in camera images, feature extraction from three-dimensional models, iterative optimization of alignment parameters, and final rendering. This segmentation allows each sub-task to be optimized independently and processed efficiently, reducing overall computational complexity while maintaining high precision.
Solution Approach 2:
The patent performs preliminary actions by pre-detecting edges and features in both the camera images and three-dimensional models before the iterative optimization process. By preparing feature data in advance, the system reduces the computational burden during the optimization phase, allowing faster convergence to the optimal alignment while maintaining high precision.
Data Source
AI summary
The present invention relates to a computer implemented method for aligning a three-dimensional model (6) of a patient's dentition to an image of the face of the patient recorded by a camera (3), the image including the mouth opening, comprising:estimating the positioning of the camera (3) relative to the face of the patient during recording of the image to obtain an estimated positioning,retrieving the three-dimensional model (6) of the dentition of the patient,rendering a two-dimensional image (7) of the dentition of the patient using the virtual camera (8) processing the three-dimensional model (6) of the dentition at the estimated positioning,carrying out feature detection in a dentition area in the mouth opening of the image (1) of the patient recorded by the camera (3) and in the rendered image (7) by performing edge detection and/or a color-based tooth likelihood determination in the respective images and forming a detected feature image for the or each detected feature,calculating a measure of deviation between the detected feature images of the image taken by the camera (3) and the detected feature image of the rendered image,varying the positioning of the virtual camera (8) to a new estimated positioning and repeating the preceding three steps in an optimization process to minimize the deviation measure to determine the best fitting positioning of the virtual camera (8).