Digital Dentistry Mesh Cleanup Through Direct 3D Mesh Labeling
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Solution Overview
Problem
Existing dental and orthodontic appliance fabrication systems face accuracy issues due to conflicting labels in 3D mesh elements resulting from 2D to 3D projection operations, leading to inefficient processing and complexity.
Innovation Solution
Direct labeling of 3D mesh elements without intermediate 2D projections, using machine learning techniques such as neural networks to improve accuracy and efficiency in geometry generation and validation, including techniques like mesh segmentation, coordinate system prediction, and hardware placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If 2D to 3D projection operations are used to label mesh elements, then processing can be performed on 2D images, but conflicting labels are generated causing loss of accuracy
Solution Approach 1:
The patent extracts and removes the problematic intermediate 2D projection step from the workflow. Instead of projecting 2D image labels to 3D mesh elements, the system directly labels 3D mesh elements using a 3D convolutional neural network that processes 3D point cloud data, eliminating the source of conflicting labels while preserving 2D image analysis capabilities through the point cloud representation
Solution Approach 2:
The patent transitions from 2D image processing to 3D point cloud processing. By representing dental anatomy as 3D point clouds and using a 3D CNN to directly label mesh elements in 3D space, the system avoids the dimensional reduction that causes projection conflicts, achieving both accuracy and operational efficiency
2Manufacturing precision
If multiple projection operations are performed to label mesh elements, then comprehensive labeling can be achieved, but computational complexity and processing time increase
Solution Approach 1:
The patent merges multiple labeling operations into a single integrated 3D CNN processing step. Instead of performing separate projection operations for different anatomical structures, the 3D convolutional neural network processes the entire 3D point cloud and generates labels for multiple mesh elements simultaneously in one computational pass, reducing complexity while maintaining comprehensive coverage
Solution Approach 2:
The patent performs preliminary organization of data into 3D point cloud representations before labeling. By pre-organizing the spatial information in a structured point cloud format with appropriate coordinate systems, the system enables efficient single-pass processing by the 3D CNN, avoiding the need for multiple sequential projection operations
3Adaptability or versatility
If 2D image projection is used for mesh labeling, then existing 2D image processing tools can be utilized, but accuracy is lost during the mapping process
Solution Approach 1:
The patent introduces 3D point cloud data as an intermediary representation that bridges 2D image analysis and 3D mesh labeling. The 3D CNN processes this intermediate point cloud representation to generate accurate 3D mesh labels, maintaining the versatility of 2D image processing while achieving high-precision 3D labeling without direct projection
Data Source
AI summary
Systems and techniques for training one or more neural networks to automatically identify one or more aspects of a digital representation used in digital oral care are disclosed including identifying one or more aspects of the first digital representation for which additional processing is to be performed, based on a list of 3D elements, generating a predicted representation by labeling those one or more aspects for which additional processing is to be performed, generating an accuracy score that specifies a difference between the one or more predicted representations and one or more respective reference representations that identify the one or more aspects of the first digital representation for which additional processing is to be performed, and modifying at least one aspect of the neural network based on the accuracy score.