Digital Image Coating Simulation With Automatic Surface Segmentation
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Solution Overview
Problem
Existing systems struggle to realistically simulate the application of coatings on digital images, particularly due to challenges in determining pixel boundaries when objects partially block the view and the need for manual techniques to maintain shadow and highlight effects.
Innovation Solution
A system comprising a mask generator, color analyzer, and renderer that uses trained models to identify objects and surfaces in an image, determine dominant colors, and apply recommended coatings, generating a painted image with user interface controls for selection and display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual techniques are used to select pixels and apply coloration, then realism of coating simulation is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs automatic object recognition, surface identification, and color analysis without requiring manual pixel selection. The trained models automatically segment surfaces, identify objects, and determine dominant colors, allowing the system to serve itself in the tasks that previously required manual intervention while maintaining realistic coating simulation.
Solution Approach 2:
Manual pixel selection and color analysis techniques are replaced with automated machine learning models. The system uses trained neural networks for object recognition, image segmentation, and color determination, substituting mechanical manual operations with automated computational processes that achieve similar or superior realism.
2Ease of operation
If automated techniques are used to identify surfaces, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system introduces multiple intermediary processing stages between automated surface identification and final coating application. Trained models serve as intermediaries that refine surface segmentation, and the system incorporates user feedback mechanisms that allow correction of automatically identified boundaries, thereby improving pixel boundary determination precision while maintaining ease of operation.
3Productivity
If painted pixels are determined based on unrecognized pixels, then productivity is improved, but reliability deteriorates
Solution Approach 1:
The system performs preliminary recognition and segmentation of objects and surfaces before determining which pixels should be painted. Trained models pre-identify paintable surfaces and generate masks that guide subsequent coating application, ensuring that only appropriate pixels are modified while maintaining high processing speed through efficient pre-computation.
Data Source
AI summary
A coating product selection system and method. Recognized objects in an input image can be used to determine one or more dominant colors for determining recommended coating products. An image augmentation system and method for simulating the application of a coating to a surface of the image in a scene. A scene record can store data records related to visualization of a scene such that multiple scene visualization clients can present painted images augmented based on assigned coatings.