3D Mesh to Planar Conversion for Efficient Machine Learning
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Solution Overview
Problem
Existing machine learning methods face challenges when applied to three-dimensional objects due to their non-uniform nature and large data formats, which require significant processing power and lack of operations suitable for 3D data, such as convolutions in convolutional neural networks.
Innovation Solution
A computer-implemented method that transforms a three-dimensional mesh into a planar mesh, samples it to generate a sampled matrix, and represents it in a uniform format, allowing for the application of two-dimensional machine learning methods and reducing data size for processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If three-dimensional objects are processed using existing machine learning methods, then the analysis can be performed on 3D data, but the processing requires significant computational power and time due to large data formats and non-uniform structure
Solution Approach 1:
The patent transforms the representation parameters of 3D objects from complex mesh formats (vertices, edges, faces with varying connectivity) to fixed-size 2D image arrays. This parameter change allows standard CNN architectures to process 3D data efficiently, reducing computational complexity while maintaining the ability to perform machine learning analysis.
Solution Approach 2:
The patent creates a 2D copy or projection of the 3D object surface onto an image plane. By rendering the 3D mesh as a 2D image with pixel values representing surface properties, the system enables the use of optimized 2D image processing algorithms instead of computationally intensive 3D data processing methods.
2Reliability
If three-dimensional objects are processed using existing machine learning methods, then the analysis can be performed on 3D data, but the large data formats require significant processing power
Solution Approach 1:
The patent changes the data structure parameters from variable-size 3D mesh representations to fixed-size 2D image arrays. This transformation reduces memory requirements and enables the use of highly optimized matrix operations in CNNs, significantly lowering computational power and energy consumption while preserving the essential geometric information.
Solution Approach 2:
By creating a 2D copy of the 3D object surface, the patent enables processing with efficient 2D convolution operations instead of computationally expensive 3D operations. The 2D image representation maintains sufficient information for many analysis tasks while requiring far less processing power.
3Reliability
If three-dimensional objects are processed using existing machine learning methods, then the analysis can be performed on 3D data, but standard operations like convolutions in convolutional neural networks do not exist for 3D data formats
Solution Approach 1:
The patent creates a 2D image copy of the 3D object that is compatible with standard CNN operations. By representing 3D surface data as 2D pixel arrays, the system enables direct application of well-established convolution operations without requiring complex 3D convolution algorithms, significantly simplifying the computational approach.
Solution Approach 2:
The patent transforms the data from three-dimensional space to two-dimensional space, enabling the use of standard 2D image processing operations. This dimensionality reduction allows convolutional neural networks to process 3D object data using their native 2D convolution operations, avoiding the need to develop and implement complex 3D convolution algorithms.
Data Source
AI summary
A computer-implemented method for generating a corresponding 3D mesh representing a 3D object includes transforming an initial three-dimensional mesh into a planar mesh, wherein each vertex or edge of the planar mesh is a transformation of a vertex or edge from the initial three-dimensional mesh; and sampling the planar mesh to generate a plurality of samples such that each sample comprises a three-dimensional coordinate representing a point in a three-dimensional space derived and/or taken directly from the initial three-dimensional mesh, and a coordinate representing a position of the sample relative to other samples; and generating the sampled matrix based on the plurality of samples; and representing the sampled matrix as a corresponding 3D mesh.