Single-Image 3D Model Generation With Split Geometry and Texture Networks
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Solution Overview
Problem
Existing methods for generating a 3D model from a single image have unsatisfactory results.
Innovation Solution
The storage device is configured to generate a model from a single image using a first network model and a second network model, where the first network model and the second network model are network models using different stem networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single network model is used to generate both geometric and texture information, then the device complexity is reduced, but the manufacturing precision and quality of the generated 3D model deteriorates
Solution Approach 1:
The patent divides the single network model into two separate network models: a first network model dedicated to generating geometric information and a second network model dedicated to generating texture information. This segmentation allows each model to specialize in its specific task, improving the overall quality and precision of the generated 3D model while maintaining reasonable system complexity through modular architecture.
2Adaptability or versatility
If different stem networks are used for geometric and texture generation, then the adaptability and robustness in complex scenarios are improved, but the device complexity increases
Solution Approach 1:
The patent applies different stem networks to different functional components of the system: the first network model uses a stem network optimized for geometric feature extraction, while the second network model uses a stem network optimized for texture feature extraction. This local quality approach ensures that each part of the system has the specific properties needed for its function, enhancing adaptability and robustness in complex scenarios.
Data Source
AI summary
A model generation method and apparatus, an electronic device, and a storage medium are disclosed. The model generation method, includes: acquiring a first image displaying a target object; generating geometric information of the target object based on the first image by using a first network model, generating texture information of the target object based on the first image by using a second network model; and generating a model for the target object based on the geometric information of the target object and the texture information of the target object; wherein the first network model and the second network model are network models using different stem networks.