Grouped 3D Data Alignment for Complete Intraoral Models
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Solution Overview
Problem
The existing methods for aligning three-dimensional volume data in intraoral scanning often result in incomplete coupling due to interruptions during the scanning process, leading to reduced precision of the intraoral model data.
Innovation Solution
An aligning method by grouped data that classifies data into at least one grouped data and supplements data gaps by generating new grouped data when alignment is not performed for a certain time, allowing for additional alignment operations and minimizing the need for continuous scanning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous scanning is performed to maintain alignment, then data completeness is improved, but user burden and scanning time increase
Solution Approach 1:
The patent segments the scanning process into multiple independent groups of image data that can be scanned and processed separately. Each group is aligned independently, allowing interruptions between groups without compromising overall data completeness. This segmentation enables non-continuous scanning while maintaining reliable intraoral model data.
Solution Approach 2:
The patent performs preliminary alignment processing on each group of image data immediately after acquisition, storing aligned results for later combination. This preliminary action allows scanning to be paused between groups without losing alignment progress, reducing the need for continuous scanning while ensuring data completeness.
2Manufacturing precision
If alignment is performed continuously, then manufacturing precision is improved, but ease of operation deteriorates due to strict continuous scanning requirements
Solution Approach 1:
The patent divides image data into multiple groups that can be scanned and aligned independently. This segmentation removes the requirement for strict continuous scanning, making the operation easier while maintaining precision through independent alignment of each group followed by combination.
Solution Approach 2:
The patent introduces an intermediate storage mechanism that holds aligned group data between scanning sessions. This intermediary allows the scanning process to be interrupted and resumed later without affecting alignment quality, improving ease of operation while preserving manufacturing precision.
3Measurement precision
If data is processed as a single continuous set, then alignment accuracy is improved, but device complexity increases due to requirement for uninterrupted scanning
Solution Approach 1:
The patent segments image data into multiple groups that can be processed independently. Each group maintains sufficient internal consistency for accurate alignment, eliminating the need for complex uninterrupted scanning systems while preserving alignment accuracy through separate processing and combination of groups.
Solution Approach 2:
The patent processes each group of image data completely independently rather than requiring full continuous processing. This partial action approach simplifies the system by allowing each group to be aligned and stored separately, reducing device complexity while maintaining overall alignment accuracy through subsequent combination.
Data Source
AI summary
In an aligning method by grouped data according to the present disclosure, while initial grouped data is generated and a process of generating and aligning three-dimensional image data is performed, if a state in which pieces of three-dimensional volume data are not connected to each other continues for a predetermined time or longer, new grouped data is generated, so that at least one discontinuous grouped data may be generated. If three-dimensional volume data stored in new grouped data and three-dimensional volume data of previously generated grouped data are identified to have overlapping parts therebetween, an additional alignment step is performed to connect the overlapping parts to each other. As a result, a data gap is supplemented, and a patient's entire oral model data can be easily obtained.