3D Model Generation from Single 2D Image via Deep Learning
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Solution Overview
Problem
Current methods for generating three-dimensional models from two-dimensional images are limited, requiring multiple images from different angles and unable to create models from a single viewpoint, making them impractical for various applications.
Innovation Solution
An apparatus and method using a deep learning module, specifically a Graph Convolutional Networks (GCN)-based module, to convert two-dimensional skeleton information into three-dimensional skeleton information, allowing for the generation of accurate three-dimensional models from a single two-dimensional image by extracting and decoding feature data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple two-dimensional images from different rotation angles are used to create a three-dimensional model, then the completeness of the three-dimensional model is improved, but the complexity of the operation process worsens
Solution Approach 1:
The patent transforms the problem from capturing multiple 2D images at different rotation angles to capturing a single 2D image and synthesizing 3D information through computational methods. This dimensionality change in the input data approach eliminates the need for physical camera rotation while achieving complete 3D model reconstruction.
Solution Approach 2:
The patent introduces an intermediary computational process that acts as a mediator between a single 2D image input and the final 3D model output. This intermediary processing layer performs synthesis and inference to generate complete 3D information without requiring multiple physical images, thereby simplifying the operation process.
2Manufacturing precision
If multiple two-dimensional images are required to generate a three-dimensional model, then the accuracy of the three-dimensional model is improved, but the adaptability of the method worsens
Solution Approach 1:
The patent creates a universal method that can generate accurate 3D models from a single 2D image, making the system adaptable to various fields such as medicine, entertainment, and e-commerce. This single-image approach removes the constraint of requiring multiple images from different angles, enabling widespread application across different domains.
Solution Approach 2:
The patent changes the input parameter requirements from multiple images to a single image, fundamentally altering the method's adaptability. By modifying this critical parameter, the system becomes versatile enough to be applied in various fields where single-image 3D reconstruction is needed, such as medical imaging and online shopping.
3Speed
If two-dimensional skeleton information is directly converted to three-dimensional skeleton information without deep learning, then the processing speed is improved, but the conversion accuracy worsens
Solution Approach 1:
The patent applies preliminary action by pre-training deep learning models with large amounts of skeleton data before actual conversion. This preprocessing step enables the model to learn accurate 2D-to-3D skeleton relationships in advance, ensuring high conversion accuracy during actual operation without sacrificing processing speed during deployment.
Solution Approach 2:
The patent replaces simple mechanical or algorithmic conversion methods with deep learning-based intelligent conversion. This substitution uses neural networks to automatically learn complex 2D-to-3D skeleton relationships, achieving high conversion accuracy while maintaining efficient processing speed through the trained model's inference capabilities.
Data Source
AI summary
Disclosed are an apparatus for generating a 3-dimensional object model and a method thereof. An apparatus for generating a 3-dimensional object model according to some embodiments of the present disclosure can acquire two-dimensional skeleton information extracted from a two-dimensional image of a target object, convert the two-dimensional skeleton information into three-dimensional skeleton information through a deep learning module, and generate a three-dimensional model for the target object based on the converted three-dimensional skeleton information. Therefore, a three-dimensional model for a target object can be accurately generated from a two-dimensional image.