3D Model Generation from 2D Material Images
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Solution Overview
Problem
Conventional methods for generating 3D object models and texture maps are time-consuming and costly, requiring manual labor and specialized equipment, and often result in unrealistic renderings due to ineffective application of 2D texture maps.
Innovation Solution
A model generation system that uses machine-learning models to create 3D object models from 2D images by generating object skeletons, determining shape parameters, and producing texture maps based on material images, while also reusing part models and leveraging CAD data to generate high-quality tessellations with improved texture application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods are used to generate 3D object models, then the models can be created with reasonable accuracy, but the process is time-consuming and costly requiring manual labor and specialized equipment
Solution Approach 1:
The patent replaces manual mechanical processes (manual drawing, manual texture mapping) with automated machine learning systems. The neural network automatically generates 3D models from 2D images and automatically creates texture maps, eliminating the need for manual labor while maintaining or improving model quality and generation speed.
Solution Approach 2:
The patent uses 2D images as copies or representations of 3D objects to generate 3D models. Instead of requiring direct measurement or manual creation of 3D structures, the system copies visual information from 2D images and transforms it into 3D representations through machine learning, significantly reducing the complexity and time required.
2Measurement precision
If depth sensors or laser scanners are used to generate 3D models, then 3D structure data can be captured, but the equipment is expensive and requires significant time for each scan
Solution Approach 1:
The patent uses 2D images as simplified copies of 3D objects to generate 3D models. Instead of requiring complex depth sensors or laser scanners, the system takes standard 2D images and uses machine learning to infer and generate the corresponding 3D structure, eliminating expensive specialized equipment while maintaining measurement precision.
Solution Approach 2:
The patent substitutes physical scanning mechanisms (depth sensors, laser scanners) with a computational approach using machine learning neural networks. The system processes 2D images through learned transformations to generate 3D models, replacing complex hardware with software-based inference.
3Manufacturing precision
If conventional methods are used to generate texture maps, then texture information can be applied to 3D models, but the process is expensive and time-consuming requiring manual creation
Solution Approach 1:
The patent replaces manual texture map creation with automated machine learning. The neural network automatically processes material images and generates corresponding texture maps, eliminating manual labor while maintaining high texture quality and significantly increasing generation speed.
Solution Approach 2:
The patent uses material images as 2D representations to generate texture maps. Instead of requiring manual painting or texturing processes, the system copies visual material information from 2D images and transforms it into properly formatted texture maps through machine learning, improving both quality and efficiency.
4Ease of manufacture
If conventional systems apply 2D texture maps to 3D object models, then textures can be applied, but the texture maps are stretched or torn resulting in unrealistic renderings
Solution Approach 1:
The patent substitutes conventional texture mapping algorithms with machine learning-based texture synthesis. The neural network learns optimal texture-to-3D mappings from training data, automatically adjusting and warping textures to fit complex 3D geometries without stretching or tearing, producing realistic renderings while maintaining ease of application.
Data Source
AI summary
The model generation system may generate texture maps for a texture based on a material image. A material image is an image of a physical material that describes the color (e.g., red-green-blue (RGB) color system model) of the physical material. The model generation system may identify a material class for the physical material depicted in the material image by applying a machine-learning model to the material image. The model generation system may then identify a texture map model that generates texture maps for the physical material based on the material image. The texture map model is a machine-learning model that is trained to generate texture maps for material images of a particular material class. The texture maps generated by the texture map model may include texture maps of standard texture values, such as metalness and roughness.