3D Tooth Setup Validation Without 2D-to-Mesh Projection
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Solution Overview
Problem
Existing dental and orthodontic systems face challenges in accurately converting 2D dental images to 3D mesh geometry due to loss of accuracy in mapping and inefficient data processing, leading to conflicting labels and increased complexity.
Innovation Solution
Direct labeling of 3D mesh elements without intermediate 2D images, using machine learning techniques such as MeshCNN and U-Net for segmentation and cleanup, and employing representation learning to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If projection operations are performed to map 2D images to 3D mesh geometry, then data conversion is achieved, but accuracy is lost and conflicting labels are generated
Solution Approach 1:
The patent extracts and removes the intermediate projection step from the traditional 2D-to-3D mapping pipeline. By directly labeling 3D mesh elements using machine learning models that consume 3D data representations, the system eliminates the source of projection-induced accuracy loss and conflicting labels, achieving precise mapping without the complexity of disambiguation algorithms.
Solution Approach 2:
Instead of projecting 2D image data onto 3D mesh geometry (the conventional approach), the patent inverts the logic by directly processing and labeling 3D mesh elements. This inversion eliminates the projection operation that causes accuracy loss and allows the machine learning model to work directly with 3D spatial information.
2Measurement precision
If additional machine learning models are used to disambiguate conflicting labels, then label accuracy improves, but processing time and computational overhead increase
Solution Approach 1:
The patent performs the labeling action directly on 3D mesh elements in the initial processing step, eliminating the need for subsequent disambiguation operations. By using machine learning models that directly consume 3D data representations, the system completes the labeling task in one operation rather than requiring multiple sequential models to resolve conflicts.
3Productivity
If 2D to 3D conversion is performed through projection, then data transformation is achieved, but processing efficiency decreases
Solution Approach 1:
The patent extracts and removes the inefficient projection operation from the data processing pipeline. By directly labeling 3D mesh elements using machine learning models that process 3D data representations, the system eliminates the computationally intensive projection step and associated disambiguation operations, significantly improving processing efficiency.
Data Source
AI summary
Systems and techniques for training one or more neural networks to automatically validate digitally generated setups for orthodontic alignment treatment are disclosed including comparing one or more assigned labels with respective one or more aspects of a second representation, automatically generating output that specifies whether the first representation is correctly formed based on the comparing, and automatically training the neural network based on one or more labels assigned by the neural network.