3D Shape Descriptor Extractor Using Encoder-Decoder Training
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Solution Overview
Problem
Current techniques struggle to accurately identify three-dimensional shapes using three-dimensional shape data due to difficulties in representing the relationships between shape components in a fixed format, which hinders data compression and processing.
Innovation Solution
A method for fabricating a three-dimensional shape descriptor extractor involves collecting surface data from three-dimensional shapes, training a set of encoder and decoder using this data, and adjusting them to minimize the difference between the encoded and decoded states, allowing for the extraction of descriptors with or without size information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If three-dimensional shape data is processed using conventional two-dimensional image recognition techniques, then the processing can be simplified, but the accuracy of three-dimensional shape identification deteriorates
Solution Approach 1:
The patent creates a two-dimensional projected image (copy) of the three-dimensional shape that preserves essential geometric features. This projected image serves as a simplified representation that can be processed using conventional two-dimensional image recognition techniques while maintaining the ability to accurately identify the original three-dimensional shape through feature point correspondence and projective transformation matrices.
Solution Approach 2:
The patent transforms the three-dimensional shape data into a two-dimensional projected image space, enabling the use of two-dimensional processing techniques. By establishing mathematical relationships (projective transformation matrices) between the two-dimensional projected image and the original three-dimensional shape, the system achieves accurate three-dimensional shape identification through two-dimensional processing.
2Productivity
If three-dimensional shape data is compressed to a fixed format, then data processing efficiency improves, but the ability to maintain shape features deteriorates
Solution Approach 1:
The patent extracts key feature points from the three-dimensional shape and its two-dimensional projected image, along with the mathematical relationships between them. This extraction creates a compact data structure that preserves essential shape information in a fixed format, enabling efficient processing while maintaining the ability to reconstruct and identify the original shape features through the stored feature point correspondences and transformation matrices.
Data Source
AI summary
A three-dimensional shape descriptor extractor is implemented by collecting surface data from a three-dimensional shape as learning data and training a set of a prescribed encoder and a prescribed decoder by using the learning data, wherein the training extracts a three-dimensional shape descriptor from the learning data by using the encoder, undoes the three-dimensional shape descriptor by using the decoder, evaluates a difference between a state before using the encoding and a state after using the decoding, and adjusts the encoder and the decoder so as to reduce the difference, and mesh data is collected from the three-dimensional shape as the surface data and made to have a prescribed scale, so as to train the encoder and the decoder.