3D Coordinate System Prediction for Dental Mesh Labeling
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Solution Overview
Problem
Existing dental and orthodontic appliance fabrication systems face challenges in accurately converting 2D images to 3D meshes due to loss of accuracy in mapping and inefficient data processing, often requiring additional machine learning models to disambiguate conflicting labels, leading to complexity and errors.
Innovation Solution
The system directly labels 3D mesh elements without intermediate 2D images, using neural networks like MeshCNN and Encoders for improved accuracy and efficiency in geometry generation and validation, employing techniques such as representation learning, federated learning, and contrastive learning to enhance model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If projection operations are used to map 2D images to 3D mesh elements, then the system can process 2D image data, but the mapping accuracy is lost and conflicting labels are generated
Solution Approach 1:
The patent extracts and eliminates the intermediate projection step from the traditional 2D-to-3D pipeline. Instead of projecting 2D image labels onto 3D mesh elements (which causes accuracy loss and conflicts), the system directly processes 3D mesh data to generate labels, removing the source of the mapping inaccuracies while preserving 2D image input for reference
Solution Approach 2:
The patent transitions from a 2D-based labeling approach to a 3D-based labeling approach. By operating directly in the 3D mesh domain rather than projecting from 2D images, the system achieves more accurate and unambiguous label assignment to mesh elements, eliminating the dimensional conversion errors inherent in projection operations
2Measurement precision
If additional machine learning models are used to disambiguate conflicting labels, then the labeling accuracy improves, but the system complexity and error rate increase
Solution Approach 1:
The patent removes the need for additional disambiguation models by eliminating the source of label conflicts in the first place. By using direct 3D mesh processing instead of projection-based 2D to 3D mapping, the system achieves unambiguous labels without requiring extra machine learning models to resolve conflicts, thus reducing overall system complexity
Solution Approach 2:
The patent segments the labeling process into independent operations on individual 3D mesh elements rather than attempting to resolve conflicts through additional models. Each mesh element is labeled independently through direct 3D processing, avoiding the need for complex multi-step disambiguation procedures
3Adaptability or versatility
If traditional 2D to 3D conversion methods are used, then the system can utilize existing image processing techniques, but the processing efficiency is reduced
Solution Approach 1:
The patent extracts the inefficient projection operation from the workflow and replaces it with direct 3D mesh processing. This eliminates the computationally intensive 2D-to-3D conversion step while maintaining the ability to process dental imaging data, achieving faster and more efficient processing without sacrificing adaptability to existing dental imaging modalities
Data Source
AI summary
Systems and techniques for training one or more encoders to automatically generate coordinate systems used in digital dentistry are disclosed including predicting one or more predicted transformations pertaining to one or more coordinate axes, determining a loss value that specifies a difference between the one or more predicted transformations and one or more respective reference transformations and modifying at least one aspect of the encoder structure based on the loss.