# US 12,223,579 B2 · Method and system for generating a 3D image of a body shape > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,223,579 B2 - Document ID: US12223579B2 - Status: Granted - Kind: B2 - Application number: 17/736,382 - Filed: 2022-05-04 - Published: 2023-11-09 - Granted: 2025-02-11 - Earliest claimed: 2022-05-04 - Assignees: City University of Hong Kong - Applicants: City University of Hong Kong - Inventors: Hongyu Liu; Wing Cheong Daniel Ho - Web page: https://patentgrep.com/patent/US12223579B2 ## CPC - G01B 11/24: for measuring contours or curvatures - G06T 15/00: Three-dimensional [3D] image rendering - G01S 7/417: {involving the use of neural networks} - G01S 13/003: {Bistatic radar systems; Multistatic radar systems} - G01S 13/89: for mapping or imaging - G06T 7/60: Analysis of geometric attributes - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30196: Human being; Person - G01B: MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES ## Abstract 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. ## Claims ### Claim 1 A method for generating a 3D image of a body shape, implemented by a processor, comprising the steps of: A. obtaining a set of far-field patterns of electromagnetic waves from a given body shape of a human or animal by collecting electromagnetic far-field patterns radiated from physical means, the physical means comprising a body shape model; B. establishing a one-to-one correspondence between the body shape and the set of far-field patterns of electromagnetic waves; C. representing the far-field patterns as a shape generating vector; D. mapping a vector of characteristic parameters to the shape generating vector; E. 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; F. 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; G. obtaining far-field patterns corresponding to the shape generating vector of the unknown geometric body shape; and H. reconstructing the unknown geometric body shape based on the obtained far-field patterns by an inverse scattering algorithm governed by a Maxwell system, wherein the body shape comprises a stationary or moving body shape, wherein, if the body shape is the moving body shape, the Maxwell system is taken to be time-dependent with a moving scatterer, and wherein the set of far-field patterns of electromagnetic waves comprises electromagnetic spectral data which encodes a color of a scattering object. ### Claim 2 (depends on 1) The method of claim 1, wherein step B comprises establishing the one-to-one correspondence between the body shape and the set of far-field patterns of electromagnetic waves by treating the body shape as a scattering object. ### Claim 3 (depends on 1) The method of claim 1, wherein step B comprises representing the far-field patterns through the use of vectorial spherical harmonics. ### Claim 4 (depends on 3) The method of claim 3, wherein step B is based on Fourier theory, and the shape generating vector consists of corresponding Fourier coefficients of the far field patterns. ### Claim 5 (depends on 1) The method of claim 1, wherein step B comprises establishing the one-to-one correspondence between the body shape and the set of far-field patterns of electromagnetic waves by treating the body shape as a perfect conductor. ### Claim 6 (depends on 1) The method of claim 1, wherein step E further comprises training the body generation model using the training dataset by an artificial neural network. ### Claim 7 (depends on 6) The method of claim 6, wherein the artificial neural network comprises a convolution neural network (CNN). ### Claim 8 (depends on 1) The method of claim 1, wherein if the body shape is the stationary body shape, the Maxwell system is taken to be time-harmonic with frequencies to be a visible electromagnetic spectrum. ### Claim 9 (depends on 1) The method of claim 1, wherein the characteristic parameters comprise gender, height, weight, arm span, waist girth, neck girth, abdomen girth, body mass index (BMI), muscle degree, and/or motion trajectory. ### Claim 10 (depends on 1) A processor configured to implement the method of claim 1. ### Claim 11 (depends on 1) A system for generating a 3D image of a body shape, comprising: a processor configured to implement the method of claim 1; an input unit configured to input characteristic parameters of a body shape; and a display unit configured to present the 3D image of the body shape reconstructed by the processor.