BRDF Parameter Extraction from Lighted Image Sequences
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Solution Overview
Problem
Current methods for generating 3D models from 2D images are inefficient, requiring complex scanning devices and excessive data storage for rendering objects under various lighting conditions, especially when dealing with objects having many shiny components.
Innovation Solution
A method that determines a third estimate of the Bidirectional Reflectance Distribution Function (BRDF) for a designated pixel by combining first and second estimates obtained from different lighting models, allowing for efficient storage and rendering of 3D models using multiple images, and utilizing diffuse and specular lighting models to capture the geometry and optical properties of objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex scanning devices and excessive data storage are used to generate 3D models, then rendering accuracy under various lighting conditions is improved, but device complexity and data storage requirements increase
Solution Approach 1:
The patent uses multiple 2D images as copies of the object from different viewpoints to reconstruct the 3D model, replacing the need for complex physical scanning devices. The 2D images serve as sufficient data sources to capture object geometry and optical properties without requiring specialized scanning equipment.
Solution Approach 2:
The patent extracts only the essential BRDF parameters (normal vector, roughness, metalness, diffuse color, specular color) from the images, storing only this extracted data rather than storing complete image datasets. This extraction approach reduces data storage requirements while maintaining rendering accuracy.
2Measurement precision
If multiple lighting models are applied to determine BRDF estimates, then accuracy for objects with shiny components is improved, but computational complexity increases
Solution Approach 1:
The patent segments the lighting model application into two distinct parts: a first lighting model for initial BRDF estimation and a second lighting model for refinement. This segmentation allows each model to be optimized for specific lighting conditions, improving accuracy for different material types while keeping computational requirements manageable through division of labor.
Solution Approach 2:
The patent changes lighting parameters between the first and second lighting models to capture different aspects of light-object interaction. By varying the lighting conditions and model parameters, the system accurately captures both diffuse and specular components of BRDF, particularly for shiny objects, without requiring a single overly complex model.
3Productivity
If traditional single lighting model is used, then computational speed is maintained, but accuracy for objects with varying surface properties deteriorates
Solution Approach 1:
The patent implements a dynamic approach where the system adaptively applies different lighting models based on the object's surface properties. The first lighting model handles general cases efficiently, while the second lighting model dynamically engages for regions requiring higher accuracy, particularly for shiny or specular surfaces. This dynamic model selection maintains computational speed while improving accuracy where needed.
Data Source
AI summary
Methods and systems are provided that use images to determine lighting information for an object. A computing device can receive an image of the object. For a pixel of the image, the computing device can: apply a first lighting model to determine a first estimate of a bi-directional lighting function (BRDF) for the object at the pixel, apply a second lighting model to determine a second estimate of the BRDF for the object at the pixel, determine a third estimate of the BRDF based on the first and second estimates, and store the third estimate of the BRDF in lighting-storage data. The computing device can provide the lighting-storage data. The BRDF can utilize a number of lighting parameters, such as a normal vector and albedo, reflectivity, and roughness values.