3D Model Generation from Single 2D Images via Structural Invariants
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Solution Overview
Problem
Existing methods for generating 3D models of objects are inefficient and resource-intensive, requiring separate scanning and digitization of each object, making the process time-consuming and costly.
Innovation Solution
A system and method for generating three-dimensional models from single two-dimensional images using a receiver, segment extractor, skeleton cue extractor, contour generator, and 3D model generator, which extract structural invariants and texture information to create unlimited-resolution 3D models, compatible with various data sources and capable of working on limited computational devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If depth cameras are used to scan physical objects to generate 3D models, then the quality and accuracy of 3D models can be achieved, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent uses a single 2D image as a copy of the physical object to generate the 3D model, eliminating the need for physical scanning. The system extracts structural invariants and texture information from the 2D image copy to reconstruct the 3D model, significantly reducing time while maintaining accuracy
Solution Approach 2:
The patent replaces the mechanical scanning process with computational image processing. Instead of using depth cameras to physically scan objects, the system uses algorithms to extract geometric and texture information from a single 2D image, substituting mechanical scanning with computational methods
2Reliability
If multiple objects are scanned and digitized separately into 3D models, then each object can be accurately represented, but the overall process becomes inefficient and resource-intensive
Solution Approach 1:
The patent creates a universal system that can process any single 2D image of an object to generate its corresponding 3D model. The methodology is applicable across different objects and industries, providing a multi-functional solution that maintains accuracy while improving efficiency through a standardized approach
Solution Approach 2:
The patent transforms the input parameters from multiple images or scanned data to a single 2D image. By changing the input parameter set to just one image and using computational extraction of structural invariants, the system achieves both accuracy and efficiency across multiple objects
3Measurement precision
If traditional scanning methods are used to generate 3D models, then detailed and accurate models can be created, but the process requires significant computational resources and time
Solution Approach 1:
The patent extracts only the essential structural invariants and texture information from the single 2D image that are necessary to reconstruct the 3D model. This selective extraction approach reduces computational overhead while maintaining the accuracy needed for detailed model representation
Solution Approach 2:
The patent performs preliminary extraction of structural invariants and geometric features from the 2D image before full 3D reconstruction. This preliminary action organizes the data in advance, reducing the computational burden during the actual model generation process
Data Source
AI summary
An example system for generating a three dimensional (3D) model includes a receiver to receive a single two dimensional (2D) image of an object to be modeled. The system includes a segment extractor to extract a binary segment, a textured segment, and a segment characterization based on the single 2D image. The system further includes a skeleton cue extractor to generate a medial-axis transform (MAT) approximation based on the binary segment and the segment characterization and extract a skeleton cue and a regression cue from the MAT approximation. The system also includes a contour generator to generate a contour based on the binary segment and the regression cue. The system can also further include a 3D model generator to generate a 3D model based on the contour and the skeleton cue.