3D Body Scanner Data Processing Flow for Apparel Sizing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current 3D body scanning technologies face challenges in accurately processing unstructured objects, correcting geometrical distortions, and deriving reliable apparel sizes and fitness parameters, often relying on manual measurements and expensive medical equipment.
Innovation Solution
A smart body scanner with depth sensors and a turntable processes 3D depth images through HDR processing, distortion correction, model fusion, and post-processing to create a natural 3D body model, enabling accurate apparel size determination and fitness parameter measurement without expensive equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement methods are used for body dimension measurement, then the equipment cost is low, but the measurement precision and efficiency are reduced
Solution Approach 1:
The patent replaces manual mechanical measurement systems with an automated 3D body scanning system using depth sensors, turntable, and computer processing. The depth sensors capture 3D depth images, the turntable rotates the body, and the computer automatically processes the data to derive apparel sizes and fitness parameters, eliminating manual measurement operations entirely.
Solution Approach 2:
The system performs self-measurement by automatically capturing 3D depth images, processing the data through model fusion algorithms, and deriving body dimension parameters without requiring manual intervention. The computer automatically fuses multiple depth images into a 3D body model and extracts measurement parameters, making the measurement process autonomous.
2Measurement precision
If sophisticated medical equipment like computer tomography is used for body analysis, then the measurement precision is improved, but the device complexity and cost increase
Solution Approach 1:
The patent uses relatively simple and inexpensive depth sensors instead of expensive medical imaging equipment like computer tomography. The depth sensors capture sufficient 3D body data for apparel sizing and fitness monitoring purposes without requiring the complex infrastructure and high costs of medical-grade imaging systems.
Solution Approach 2:
The patent extracts only the necessary measurement information (body dimensions, circumferences, and fitness parameters) needed for apparel retail and fitness monitoring, rather than using comprehensive medical imaging systems that capture far more data than required. This selective approach reduces equipment complexity while maintaining measurement precision for the specific application.
3Manufacturing precision
If 3D depth images are processed through multiple correction and fusion steps, then the manufacturing precision of the body model is improved, but the processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary corrections on individual depth images before fusion, including geometric distortion correction and systematic error correction. By pre-processing each depth image to correct known errors, the subsequent model fusion process becomes more efficient and produces higher accuracy results without requiring excessive computational time during the fusion stage.
4Measurement precision
If multiple depth sensors are used to capture 3D depth images, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent combines multiple depth sensors mounted on a rotating mast to capture 3D depth images of the body from different angles. The sensors work together with the turntable to comprehensively scan the entire body, merging their individual measurements into a complete 3D body model through computer processing.
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
The solution provides accurate and efficient derivation of apparel sizes and fitness parameters, reducing the need for manual measurements and expensive medical equipment, while improving the accuracy of body scans and enabling detailed fitness monitoring.
Implementation Method 1
a number of depth sensors mounted on a mast to create 3D depth images of the body
Data Source
Figure 1a
Figure 1b
Figure 1c
AI summary
Processing flow, algorithms and methods implemented in a smart body scanner with scale for online apparel retail and fitness and healthcare applications, such a smart body scanner is built up by a number of depth sensors to create 3D depth images of the body, with Hardware Processing which may give recorded part-images with saturated bright regions and such with lost dark regions which will be combined in a pre-processing, the pre-processing supports the algorithms with reliable valid scaled depth data as high Dynamic Range (HDR) Processing, followed by model fusion to create a single unequivocal 3D body model from a stream of 3D depth images.