3D Body Shape Reconstruction via Electromagnetic Scattering
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Solution Overview
Problem
Existing technologies for generating 3D images of human body shapes are limited in their ability to accurately represent both stationary and moving bodies, as well as natural colors, using computer vision and imaging processing.
Innovation Solution
A method utilizing a body generation model that combines inverse electromagnetic scattering theory and machine learning, following the bionic process of human vision, to generate 3D images of body shapes. This method involves obtaining far-field patterns of electromagnetic waves, establishing a correspondence between body shapes and these patterns, and using machine learning to reconstruct unknown body shapes based on characteristic parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision and imaging processing are used to generate 3D images of body shapes, then the process can be implemented with existing technology, but the accuracy in representing both stationary and moving bodies and natural colors is insufficient
Solution Approach 1:
The patent replaces traditional computer vision and imaging processing mechanisms with electromagnetic scattering theory. By treating the body shape as an electromagnetic scattering object and using far-field patterns of electromagnetic waves as input data, the system achieves more accurate and reliable 3D body shape generation that can represent both stationary and moving bodies with natural colors.
Solution Approach 2:
The patent changes the fundamental parameters used for 3D body shape generation from traditional image processing parameters to electromagnetic scattering parameters. By using far-field patterns of electromagnetic waves and establishing a one-to-one correspondence between body shapes and these patterns, the system achieves improved accuracy and reliability in generating 3D images.
2Adaptability or versatility
If traditional computer vision methods are used, then the system complexity is lower, but the ability to generate accurate 3D images of moving bodies and natural colors is limited
Solution Approach 1:
The patent creates a universal body generation model that can handle multiple types of body shapes (stationary and moving) and generate various characteristics (natural colors, different geometries) using a single unified approach based on electromagnetic scattering theory and machine learning, rather than requiring separate systems for different functions.
Solution Approach 2:
The patent introduces far-field patterns of electromagnetic waves as an intermediary between the body shape and the generation model. This intermediary enables the system to capture and represent complex body characteristics including motion and color information, allowing the model to generate accurate 3D images of moving bodies with natural colors.
3Measurement precision
If inverse electromagnetic scattering theory and machine learning are combined, then the accuracy of generating 3D body shapes is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex body generation problem into distinct manageable components: (1) obtaining far-field patterns of electromagnetic waves from body shapes, (2) establishing a one-to-one correspondence between body shapes and far-field patterns, (3) representing far-field patterns as shape generating vectors, (4) mapping characteristic parameters to shape generating vectors, and (5) using machine learning to reconstruct body shapes. This segmentation allows each component to be optimized independently.
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 method can generate accurate 3D images of both stationary and moving human or animal body shapes, including natural colors, making it suitable for various applications such as online shopping, criminal detection, and film industry.
Implementation Method 1
obtaining a set of far-field patterns of electromagnetic waves from a given body shape of a human or animal
Implementation Method 2
solving a certain electromagnetic scattering problems governed by Maxwell system
Data Source
AI summary
A method for generating a 3D image of a body shape includes obtaining a set of far-field patterns of electromagnetic waves from a given body shape of a human or animal, establishing a one-to-one correspondence between the body shape and the set of far-field patterns of electromagnetic waves, representing the far-field patterns as a shape generating vector, mapping a vector of characteristic parameters to the shape generating vector, producing a body generation model by taking the vector of characteristic parameters and the shape generating vector as input and output data to form a training dataset for machine learning, specifying a characteristic vector of an unknown geometric body shape and obtaining a shape generating vector of the unknown geometric body shape based on the body generation model, and reconstructing the unknown geometric body shape based on the shape generating vector.