3D Scanning with Automatic Region Selection
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Solution Overview
Problem
Conventional 3D scanning methods for objects, particularly in dental applications, are slow and prone to human error due to manual identification of regions of interest, leading to inefficient scanning times and potential inaccuracies.
Innovation Solution
An automated method for 3D scanning that uses optical scanners to record test 2D images, identify regions of interest, and adjust scanning parameters such as resolution and coverage, allowing for faster scanning by prioritizing high-resolution imaging of critical areas while reducing unnecessary scanning of less relevant regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual identification of regions of interest is used, then user control over scanning parameters is maintained, but scanning time increases and human error occurs
Solution Approach 1:
The system performs automatic identification of regions of interest using image analysis algorithms that process test 2D images to detect features such as tooth preparations, margins, and scan flags. This self-service capability eliminates manual operator intervention, reducing scanning time while maintaining consistent accuracy through automated feature detection and classification.
2Measurement precision
If uniform high resolution is applied to entire object surface, then measurement precision is improved, but scanning time and data volume increase
Solution Approach 1:
The system implements variable resolution scanning by automatically identifying regions of interest and assigning different scanning parameters to different areas. High-resolution scanning is applied only to critical regions such as tooth preparations and margin lines, while non-critical areas are scanned at lower resolution or skipped entirely. This local quality approach maintains measurement precision where needed while significantly reducing overall scanning time and data volume.
3Loss of information
If comprehensive coverage of entire object is scanned, then completeness of data is improved, but productivity decreases
Solution Approach 1:
The system extracts and prioritizes scanning of critical regions by automatically detecting regions of interest from test images. It separates the object surface into regions requiring high-detail scanning (such as preparations, margins, and scan flags) and regions that can be scanned with lower priority. This extraction approach ensures complete coverage of essential features while improving productivity by avoiding unnecessary scanning of non-critical areas.
4Productivity
If automated region identification is implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The system replaces manual mechanical operation with automated image analysis and computer vision algorithms. Test 2D images are processed through software that automatically detects features, classifies regions of interest, and generates optimized scanning paths. This substitution of mechanical/manual processes with automated computational methods increases productivity while the added complexity is confined to software processing rather than hardware complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces scanning time by minimizing user interaction and improving accuracy by automatically selecting optimal scanning strategies based on the analysis of test 2D images, ensuring higher resolution and coverage of regions of interest without the need for initial coarse digital representations.
Implementation Method 1
When an object is 3D scanned using an optical method, a digital 3D representation of the object can be derived from a series of 2D images each providing surface topography information for a portion of the object surface
Implementation Method 2
Many triangulation 3D scanners use a laser beam to probe the object surface and exploit a 2D camera to look for the location of the reflection of the laser beam. Depending on how far away the laser strikes a surface, the laser beam appears at different places in the camera's field of view. This technique is called triangulation because the point where the laser beam impinges on the surface, the camera and the light source together form a triangle.
Data Source
AI summary
According to an embodiment, a method for 3D scanning at least a part of a surface of an object is disclosed. The method includes recording, using an optical scanner comprising at least one camera, one or more test 2D images of the at least a part of the surface of the object; automatically identifying a first segment of a first level of interest within the test 2D images, the first segment imaging a region of interest on the at least a part of the surface of the object; identifying a first 3D volume comprising the region of interest of the at least a part of the surface of the object; selecting a first input defining a first resolution and/or a first coverage; and 3D scanning the at least a part of the surface of the object within the first 3D volume using the first input.