3D Shape Determination Using Neural Network Pattern Recognition
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Solution Overview
Problem
Current methods for determining three-dimensional shapes of objects using pattern decoding are inefficient due to high computational requirements and the need for detailed recognition of figures, especially when dealing with repetitive patterns.
Innovation Solution
A method involving the projection of a figure pattern with a repeating region onto an object, using a trained neural network to recognize figures in the recorded image, which allows for rapid determination of the three-dimensional shape by leveraging area indices and relative positions, reducing computational effort through epipolar geometry and calibration information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional pattern decoding method is used to determine three-dimensional shape, then the shape can be determined, but high computational effort and time are required
Solution Approach 1:
The figure pattern is divided into multiple areas, each containing a subset of figures. The neural network processes each area separately and independently, breaking down the complex task of recognizing all figures in the entire pattern into smaller, parallelizable sub-tasks. This segmentation enables faster processing while maintaining accurate three-dimensional shape determination.
Solution Approach 2:
Instead of requiring the neural network to recognize all figures in the complete figure pattern, the method uses only a subset of figures from selected areas. This partial action approach provides sufficient information for accurate three-dimensional shape reconstruction while significantly reducing computational complexity and processing time.
2Measurement precision
If detailed recognition of all figures in the pattern is performed, then accurate shape determination is achieved, but computational complexity increases
Solution Approach 1:
The figure pattern is divided into multiple areas, each containing a subset of figures. The neural network processes each area separately and independently, breaking down the complex task of recognizing all figures in the entire pattern into smaller, parallelizable sub-tasks. This segmentation enables faster processing while maintaining accurate three-dimensional shape determination.
Solution Approach 2:
Instead of requiring the neural network to recognize all figures in the complete figure pattern, the method uses only a subset of figures from selected areas. This partial action approach provides sufficient information for accurate three-dimensional shape reconstruction while significantly reducing computational complexity and processing time.
Data Source
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AI summary
The invention relates to a method for determining a three-dimensional shape (FO) of at least one part (ST) of a body (KO), wherein the method comprises the steps: a) projecting a figure pattern (PM) onto at least the part (ST) of the body (KO), wherein the unprojected figure pattern (PM) has a region (BE) of recognizable figures (PU) repeating in one dimension (y), b) capturing an image (BI) of the projected figure pattern (PM) onto at least the part (ST) of the body (KO), c) determining figures (PU) in the captured image (BI) by means of recognition using a neural network (NN), wherein the neural network (NN) is trained by means of training images (TBI) of differently deformed sections (AS) of the figure pattern (PM) to recognize the figures (PU) in the region (BE), and by means of a region index (BEI) of the corresponding region (BE).and d) Determining the three-dimensional shape (FO) of at least the piece (ST) of the body (KO) based on the determined figures (PU).