Intraoral Scanning With Surface Differentiation for Weighted Stitching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Intraoral 3D scanners face challenges in accurately stitching together views due to deformable or moving surfaces within the oral cavity, leading to inaccuracies in the combined 3D topography measurement, as existing methods require additional information or are computationally expensive.
Innovation Solution
A method using a score-finding algorithm, potentially machine learning-based, to determine the type of surface and assign weights for weighted stitching, considering surface geometry and neighborhood data, allowing for more accurate stitching without direct detection of movement or deformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional stitching algorithms are used that assume rigid surfaces, then the stitching process is computationally efficient and simple, but the accuracy of the combined 3D topography measurement deteriorates due to deformable surfaces
Solution Approach 1:
The patent applies local quality by differentiating between various types of surfaces (teeth, gingiva, tongue, cheeks) and assigning different weights to each surface type during the stitching process. This allows the system to treat each surface type according to its specific deformation characteristics, improving measurement accuracy without requiring a completely complex non-rigid stitching algorithm for the entire oral cavity.
Solution Approach 2:
The patent changes the parameter of surface weight in the stitching algorithm based on surface type classification. By dynamically adjusting the weight parameter for different surface types (with deformable surfaces receiving lower weights and rigid surfaces receiving higher weights), the system resolves the contradiction between using simple rigid-assumption algorithms and achieving accurate measurements on deformable surfaces.
2Measurement precision
If non-rigid stitching algorithms are used to handle deformable surfaces, then the accuracy of the combined 3D topography measurement is improved, but the computational cost and processing time increase significantly
Solution Approach 1:
Instead of applying a computationally expensive non-rigid stitching algorithm uniformly across all surfaces, the patent applies local quality by identifying and selectively weighting specific surface types. This allows the system to maintain high scanning efficiency while improving accuracy only where needed - on deformable surfaces that require special handling.
Solution Approach 2:
The patent applies partial action by using a simplified weighting approach for only the necessary surface types rather than implementing a full non-rigid stitching solution. This partial application of complexity achieves the required accuracy improvement without the full computational burden of non-rigid algorithms.
3Productivity
If color-based classification is used to distinguish teeth from other tissue, then the stitching process can focus on rigid tooth surfaces, but the accuracy deteriorates when teeth are discolored or when non-white rigid tissue needs to be included
Solution Approach 1:
The patent changes the classification parameters from relying solely on color to using multiple parameters including surface geometry, texture, and reflectivity. This multi-parameter approach maintains stitching efficiency while significantly improving classification accuracy for discolored teeth and non-white rigid tissues like palatal rugae.
Solution Approach 2:
The patent uses a composite classification approach that combines multiple surface characteristics (geometric features, optical properties, texture patterns) rather than relying on a single color parameter. This composite method improves classification accuracy while maintaining processing efficiency.
Data Source
AI summary
A method for generating a digital 3D representation of at least a part of an intraoral cavity, the method including recording a plurality of views containing surface data representing at least the geometry of surface points of the part of the intraoral cavity using an intraoral scanner; determining a weight for each surface point at least partly based on scores that are measures of belief of that surface point representing a particular type of surface; executing a stitching algorithm that performs weighted stitching of the surface points in said plurality of views to generate the digital 3D representation based on the determined weights; wherein the scores for the surface points are found by at least one score-finding algorithm that takes as input at least the geometry part of the surface data for that surface point and surface data for points in a neighbourhood of that surface point.