3D Mesh Validation for Dental Restoration Component Labeling
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Solution Overview
Problem
Existing dental appliance fabrication systems face accuracy issues due to 2D to 3D conversion inefficiencies and conflicting labels, leading to complex and error-prone processing.
Innovation Solution
Direct labeling of 3D mesh elements without intermediate 2D images, using machine learning techniques like MeshCNN and Encoder for improved segmentation and cleanup, and employing representation learning to enhance model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If 2D to 3D conversion is performed using existing projection operations, then the processing can be completed, but the accuracy is lost and conflicting labels are generated
Solution Approach 1:
Instead of projecting 2D labels onto 3D mesh elements (traditional approach), the patent inverts the process by directly labeling 3D mesh elements in their native 3D space. This avoids the fundamental accuracy loss inherent in 2D-to-3D projection methods while eliminating conflicting labels caused by multiple projections of the same 3D element.
Solution Approach 2:
The patent transitions from operating in 2D image space to operating directly in 3D mesh space. By performing labeling operations natively in 3D space rather than converting from 2D, the system preserves spatial relationships and geometric accuracy while avoiding the dimensionality conversion errors that cause conflicting labels.
2Measurement precision
If additional machine learning models are added to disambiguate conflicting labels, then the accuracy may be improved, but the system complexity and error increase
Solution Approach 1:
The patent extracts and removes the source of conflicting labels by eliminating the 2D-to-3D projection step entirely. By working directly with 3D mesh elements, the system prevents the generation of conflicting labels at the source, making additional disambiguation models unnecessary and reducing overall system complexity.
Solution Approach 2:
The patent performs labeling operations directly on 3D mesh elements from the beginning, preventing conflicting labels from being generated in the first place. This preliminary action of working in the correct 3D space avoids the need for subsequent corrective measures or additional models to resolve conflicts.
3Ease of manufacture
If 2D images are used as intermediate representation, then the processing can be simplified, but the computational overhead increases and accuracy is lost
Solution Approach 1:
The patent removes the intermediate 2D image representation step from the processing pipeline. By working directly with 3D mesh data throughout the entire workflow, the system eliminates the computational overhead of generating, processing, and converting 2D images while preserving geometric accuracy.
Solution Approach 2:
The patent creates a unified 3D-based processing framework that handles all labeling operations directly in 3D space. This universal approach eliminates the need for separate 2D image processing pipelines and conversion steps, simplifying the overall system while reducing computational time.
Data Source
AI summary
Systems and techniques for training one or more neural networks to automatically validate geometrical characteristics of a digital representation of a dental restoration appliance component are disclosed including analyzing one or more assigned labels, automatically generating output that specifies whether the dental restoration appliance is incorrect, automatically training the neural network based on the one or more result labels assigned by the neural network.