Neural 3D Body-Surface Reconstruction for Real-Time Device Guidance
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Solution Overview
Problem
Existing methods for reconstructing three-dimensional surfaces are not suitable for real-time, dynamic control of medical devices near the skin surface, requiring significant computational resources and often necessitating human assistance due to complexity and approximation.
Innovation Solution
A method involving the generation of a three-dimensional working surface using a neural network trained on parametric models, incorporating shape descriptors and attributes from a point cloud, enabling real-time enslavement of medical devices with minimal acquisition systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to reconstruct three-dimensional surfaces, then measurement precision is improved, but device complexity and computational resources increase significantly
Solution Approach 1:
The patent creates a digital 3D model (copy) of the body surface that can be manipulated and analyzed without requiring complex physical acquisition systems. The neural network generates this digital replica from simplified input data, replacing the need for multiple complex sensors and processors while maintaining measurement precision.
Solution Approach 2:
The patent replaces traditional mechanical/image processing systems with a neural network-based computational system. Instead of using complex image processing algorithms and multiple acquisition systems, the invention uses a trained neural network that processes simplified input data to generate accurate 3D surface models, significantly reducing device complexity.
2Measurement precision
If traditional image processing methods are used to reconstruct three-dimensional surfaces, then measurement precision is improved, but processing time increases
Solution Approach 1:
The neural network is trained in advance on a large dataset of 3D surface information. This preliminary training phase allows the network to learn complex surface patterns and relationships, so that during actual operation, it can rapidly generate accurate 3D models from simplified input data without requiring time-consuming traditional image processing.
Solution Approach 2:
By creating a pre-trained neural network model that serves as a computational template, the system can rapidly generate 3D surface models by applying this pre-learned knowledge to new input data, significantly reducing processing time while maintaining precision.
3Manufacturing precision
If traditional surface reconstruction methods are used, then manufacturing precision is improved, but ease of operation deteriorates due to requiring human assistance
Solution Approach 1:
The neural network system performs automatic 3D surface reconstruction without requiring human intervention. The system takes input data, processes it through the trained neural network, and generates accurate 3D models autonomously, eliminating the need for operators to perform complex manual processing or make judgment calls.
Solution Approach 2:
The patent replaces manual human operations with an automated neural network system. The network automatically processes input data, identifies surface features, and generates accurate 3D models without human assistance, improving ease of operation while maintaining manufacturing precision.
4Adaptability or versatility
If complex processing algorithms are used to handle body deformations, then adaptability is improved, but device complexity increases
Solution Approach 1:
The neural network is designed to dynamically adapt to changing body surfaces. It processes sequential input data and generates updated 3D models that automatically adjust to deformations and movements, providing real-time adaptability without requiring complex dynamic processing algorithms.
Solution Approach 2:
The system maintains a dynamic digital copy of the body surface that automatically updates as the body deforms. The neural network continuously processes new input data and regenerates the 3D model to reflect current surface conditions, providing adaptability through simplified iterative processing rather than complex algorithms.
Data Source
AI summary
A method for generating a three-dimensional working surface of a human body, includes receiving input data corresponding to geometric data; generating a first point cloud from the input data; generating partial views of parameterised body models corresponding to a parametric body model parameterised with different parameterisation configurations, wherein the parametric body model models the human body in which a set of articulations are predefined; calculating a set of geometric parameters, and determining another parameterised model from the set of geometric parameters to generate the human body model of the human body including a first meshing.