3D Dental Restoration Geometry Without 2D Label Conflicts
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Solution Overview
Problem
Existing dental and orthodontic appliance fabrication systems face inaccuracies and inefficiencies due to the loss of accuracy in mapping 2D to 3D conversions, often requiring additional machine learning models to disambiguate conflicting labels, leading to complex and error-prone processes.
Innovation Solution
Direct labeling of 3D mesh elements without intermediate 2D images, utilizing advanced neural networks like MeshCNN, U-Net, and ResNet for segmentation and cleanup, and employing federated learning to enhance model training while preserving data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2D to 3D conversion is performed using existing projection operations, then the process can be completed, but accuracy is lost and conflicting labels are generated requiring additional disambiguation models
Solution Approach 1:
The patent extracts and removes the intermediate 2D image generation step from the workflow. By directly projecting 3D mesh elements to 2D image space and labeling them there, the system eliminates the source of conflicting labels that occurs when multiple 2D projections are mapped back to 3D space. This extraction simplifies the system architecture while maintaining or improving labeling accuracy.
Solution Approach 2:
Instead of the traditional approach of labeling 3D mesh elements directly (which causes conflicting labels when multiple 2D projections map to the same 3D element), the patent inverts the workflow by labeling 2D image space elements and then mapping those labels back to the 3D mesh. This reversal eliminates the conflict resolution problem while preserving accuracy.
2Measurement precision
If additional machine learning models are added to disambiguate conflicting labels, then labeling accuracy may be improved, but processing time and computational overhead increase
Solution Approach 1:
The patent removes the need for additional disambiguation models by extracting the conflicting label generation step from the pipeline. By working in 2D image space where each pixel corresponds to a unique viewing direction, the system eliminates the many-to-many mapping problem that creates conflicts, thereby reducing processing time without sacrificing accuracy.
3Productivity
If 2D to 3D conversion is performed through intermediate projection operations, then the conversion can be achieved, but efficiency is reduced due to complex processing steps
Solution Approach 1:
The patent merges the projection operation and the labeling operation into a single unified process. By performing labeling in 2D image space directly after projection and before mapping back to 3D, the system combines multiple discrete steps into one efficient workflow, thereby improving conversion efficiency while reducing processing complexity.
4Reliability
If multiple projection operations are applied to map 2D labels back to 3D mesh, then coverage is improved, but conflicting labels are generated on the same 3D elements
Solution Approach 1:
The patent inverts the traditional mapping direction by first labeling in 2D image space and then mapping those labels to 3D mesh elements, rather than projecting multiple 2D views onto the same 3D elements. This reversal ensures that each 3D element receives labels from consistent viewing angles, maintaining both coverage and consistency without conflicts.
Data Source
AI summary
Systems and techniques for training one or more machine learning models to generate digital representations of dental restoration tooth geometry are disclosed including generating one or more digital representations that define a restored state for a first digital representation, determining one or more differences between the one or more predicted representations for the restored state and the one or more reference representations of the restored state, and modifying the machine learning model based on the determined differences.