3D Body Modeling With Honeycomb Marker Suits for Higher Accuracy
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Solution Overview
Problem
Existing garments for measuring body sizes are limited in their ability to create accurate 3D models of a user's body shape, necessitating a technique for higher accuracy in measurement.
Innovation Solution
A new model body suit with densely distributed, stretchable markers and a pseudo-random dot pattern, combined with a smartphone application and image processing algorithms, allows for precise 3D body modeling by capturing multiple images and forming a honeycomb structure for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional body measuring garments are used, then the device complexity is low, but the measurement precision is insufficient (average surface measurement error greater than 4 mm)
Solution Approach 1:
The garment surface is segmented into multiple measurement zones with different marker densities. High-curvature regions (knees, elbows, waist) have denser marker distributions while low-curvature regions have sparser markers, optimizing measurement precision where needed without uniformly increasing complexity across the entire garment
Solution Approach 2:
Different regions of the garment are assigned different marker densities based on local curvature characteristics. The marker distribution adapts to local geometric properties, providing higher measurement precision in critical areas while maintaining lower complexity in less critical areas
Solution Approach 3:
The marker density parameter is dynamically adjusted based on the curvature radius of different body regions. Areas with smaller curvature radii (higher curvature) receive denser marker distributions, while areas with larger curvature radii receive sparser distributions, optimizing the balance between measurement precision and device complexity
2Measurement precision
If markers are sparsely distributed on the garment, then the device complexity is low, but the measurement precision deteriorates
Solution Approach 1:
The garment is divided into multiple measurement zones with differentiated marker densities. Critical measurement regions receive higher marker concentrations while non-critical regions use sparser distributions, reducing the total quantity of markers needed while maintaining high measurement precision where it matters most
Solution Approach 2:
Marker density is locally optimized according to the functional importance and geometric complexity of different body regions. This selective distribution ensures adequate measurement precision in critical areas without unnecessarily increasing the overall marker quantity
3Measurement precision
If markers are densely distributed across the entire garment, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
Instead of uniform dense marker distribution, the garment is segmented into regions with varying marker densities based on local curvature requirements. This targeted approach achieves high measurement precision in critical areas while avoiding the unnecessary complexity of dense markers across the entire garment surface
Solution Approach 2:
The marker density parameter is spatially varied according to the curvature characteristics of different body regions. This adaptive parameter distribution achieves optimal measurement precision where needed while minimizing overall device complexity through sparser marker placement in less critical regions
Data Source
AI summary
An information processing device includes: a formation unit configured to analyze relative positions of dots as measurement markers that expand and contract together with a suit, and form a cluster of the dots; an assignment unit configured to assign numbers to the cluster and the dots; a specification unit configured to acquire the assigned number, set the assigned number to a predetermined coordinate position, and specify a silhouette; and a generation unit configured to generate a three-dimensional (3D) model based on the specified silhouette. The formation unit forms a hexagonal cluster from a dot pattern of the suit, and increases the hexagonal cluster to form a unique honeycomb structure. The assignment unit assigns numbers to the cluster and the dots based on the honeycomb structure. Solid dots and hollow dots are printed as the dots on the whole suit. Thus, a 3D model of a body shape of a user is created with higher accuracy by using a garment for measuring body sizes.