3D Tooth Reconstruction From 2D Images Without Intraoral Scanning
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Solution Overview
Problem
Existing methods for obtaining 3D dental features, such as dental impressions and intraoral scanning, are time-consuming, prone to errors, and require specialized equipment, limiting accessibility and increasing costs.
Innovation Solution
A device and method using a single optical sensor or multiple sensors to capture 2D images of teeth, combined with a trained machine-learning model, to generate accurate 3D representations without the need for calibration or specialized equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dental impressions or intraoral scanning are used to obtain 3D dental features, then measurement precision is improved, but device complexity and time consumption increase
Solution Approach 1:
The patent uses 2D photographs as copies of the actual tooth structure. Instead of requiring complex 3D scanning equipment, the system captures multiple 2D images from different angles and uses machine learning to reconstruct the 3D dental features. This copying approach simplifies the device while maintaining measurement precision through computational methods.
Solution Approach 2:
The patent replaces mechanical 3D scanning systems with a computational approach using 2D image capture and machine learning algorithms. The mechanical complexity of intraoral scanners is substituted by capturing 2D photographs and processing them through trained neural networks to generate accurate 3D representations of dental structures.
2Manufacturing precision
If traditional intraoral scanning is used, then manufacturing precision for dental prosthetics is improved, but loss of time during the scanning process increases
Solution Approach 1:
The system performs preliminary action by capturing multiple 2D photographs from different angles before the actual 3D reconstruction process. The machine learning model is pre-trained on extensive datasets of tooth images and their corresponding 3D structures, enabling rapid inference. This preliminary preparation allows the system to quickly generate accurate 3D models without time-consuming scanning during the clinical procedure.
Solution Approach 2:
The patent changes the parameter space by working in 2D image domain rather than direct 3D space. By capturing 2D photographs and using machine learning to infer 3D characteristics, the system transforms the problem from direct 3D measurement to 2D image analysis followed by computational reconstruction, significantly reducing the time required while maintaining manufacturing precision.
3Measurement precision
If specialized scanning equipment is used, then measurement precision is improved, but ease of operation deteriorates due to calibration requirements
Solution Approach 1:
The system implements self-service by using the tooth structure itself as the reference for scaling and orientation. The machine learning model automatically identifies anatomical landmarks and uses the known geometry of teeth to self-calibrate the 3D reconstruction. This eliminates the need for external calibration objects or manual calibration procedures, making the system easier to operate while maintaining measurement precision.
Solution Approach 2:
Instead of using external calibration standards to define the measurement scale, the patent inverts the approach by using the tooth structure itself as the calibration reference. The machine learning model learns the relationship between 2D image features and 3D tooth geometry, allowing the system to self-calibrate based on the inherent geometric properties of teeth rather than requiring external calibration equipment or procedures.
Data Source
AI summary
Provided herein are methods, systems, algorithms, computer programs, kits, devices, and computer-executable code for generating a three-dimensional (3D) representation of a user's tooth or teeth from two-dimensional (2D) images.