3D Dental Model Reconstruction From 2D Images
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Solution Overview
Problem
Existing orthodontic treatment planning systems face challenges in accurately and efficiently generating 3D models of an individual's dentition and positioning them, as current methods are resource-intensive and difficult to present, and 2D-to-3D conversion techniques are inefficient.
Innovation Solution
Implementing a 3D geometry optimization framework using differentiable rendering techniques to compare 2D and 3D dental models, and training machine learning neural networks to reconstruct 3D dental models from 2D images, allowing for automated optimization and accurate 3D model generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D dental modeling methods are used, then model accuracy can be achieved, but computational resource consumption increases and processing time extends
Solution Approach 1:
The patent replaces traditional mechanical scanning and manual modeling processes with machine learning-based automated reconstruction systems. The ML model directly predicts 3D tooth geometries from 2D images, eliminating the need for complex point cloud processing and iterative mesh generation, thereby reducing computational resource consumption while maintaining accuracy
Solution Approach 2:
The patent transforms the modeling approach by changing from explicit geometric parameterization to latent space representation. Instead of manually adjusting thousands of mesh parameters, the system uses a trained neural network that maps 2D image parameters directly to 3D tooth representations through learned transformations, significantly reducing computational complexity
2Measurement precision
If traditional 3D dental modeling methods are used, then model accuracy can be achieved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on extensive datasets of 2D images and corresponding 3D tooth models. This training phase captures the essential relationships between 2D appearances and 3D geometries, allowing the system to rapidly reconstruct new tooth models by simply inputting 2D images without requiring time-consuming real-time computation during actual dental scans
Solution Approach 2:
The patent uses copying by creating a digital replica of the tooth geometry through machine learning inference. Instead of physically scanning and processing actual tooth surfaces through complex algorithms, the system copies the essential geometric information from 2D images through the trained neural network's learned representations, achieving fast and accurate 3D reconstruction
3Measurement precision
If complex 3D dental models are created, then treatment planning accuracy improves, but system complexity and difficulty of manipulation increase
Solution Approach 1:
The patent extracts the complexity from the final output by separating the complex computational tasks into the pre-trained model's internal representations. The system only needs to input simple 2D images and receives ready-to-use 3D tooth models without requiring users to manage complex point clouds, meshes, or geometric algorithms, thereby reducing operational complexity while maintaining high treatment planning accuracy
Solution Approach 2:
The patent introduces an intermediary layer in the form of a pre-trained machine learning model that mediates between simple 2D image inputs and complex 3D tooth geometries. This intermediary handles all the computational complexity internally through its learned transformations, presenting a simplified interface to users while delivering accurate 3D models for treatment planning
4Productivity
If 2D-to-3D conversion is performed using traditional methods, then 3D models can be generated, but conversion efficiency remains low
Solution Approach 1:
The patent replaces traditional iterative 2D-to-3D conversion algorithms with a direct machine learning inference system. Instead of using expectation-maximization or other iterative optimization methods that require multiple rounds of computation, the trained neural network performs a single forward pass to directly convert 2D images to 3D tooth representations, dramatically improving conversion efficiency and reducing processing time
Data Source
AI summary
Provided herein are systems and methods for optimizing a 3D model of an individual's teeth. A 3D dental model may be reconstructed from 3D parameters. A differentiable renderer may be used to derive a 2D rendering of the individual's dentition. 2D image(s) of an individual's dentition may be obtained, and features may be extracted from the 2D image(s). Image loss between the 2D rendering and the 2D image(s) can be derived, and back-propagation from the image loss can be used to calculate gradients of the loss to optimize the 3D parameters. A machine learning model can also be trained to predict a 3D dental model from 2D images of an individual's dentition.