Neural Network Visual Property Estimation From Two-Shot Images
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing representations of physical objects in virtual worlds lack realism due to insufficient inclusion of accurate visual properties, such as color, reflectivity, and roughness, which are not captured in images used for rendering.
Innovation Solution
A cascaded neural network approach that utilizes two-stage convolutional neural networks to estimate geometry, illumination, and material properties from two-shot images, leveraging a differentiable renderer for optimization, allowing for accurate representation of objects with complex specular properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If images are captured of physical objects for use in rendering virtual representations, then the objects can be represented in virtual worlds, but the representations lack realism because accurate visual properties are not captured
Solution Approach 1:
A neural network system acts as an intermediary between captured images and virtual representations. The neural network processes input images and infers accurate visual properties (reflectivity, roughness, color) that are not directly observable, thereby bridging the information gap and enabling realistic virtual representations without requiring direct measurement of all visual properties
Solution Approach 2:
The patent replaces traditional mechanical/optical measurement systems with a computational approach using neural networks. Instead of using complex physical sensors to directly measure visual properties, the system uses image processing and machine learning to infer these properties, substituting physical measurement mechanisms with information processing mechanisms
2Measurement precision
If a cascaded neural network approach is used to estimate geometry, illumination, and material properties, then accurate visual representations can be achieved, but processing resources and computational complexity increase
Solution Approach 1:
The neural network system is segmented into multiple specialized networks, each responsible for estimating specific visual properties (geometry, illumination, material properties). This modular architecture allows each network to be optimized for its specific task, improving overall accuracy while enabling independent training and deployment of individual components, thereby managing system complexity
3Measurement precision
If two-shot images are used to capture objects under different lighting conditions, then accurate material properties can be estimated, but the capture process becomes more complex and time-consuming
Solution Approach 1:
The system performs preliminary processing by capturing images under different lighting conditions (two-shot imaging) and using a neural network to separate illumination effects from material properties in advance. This preliminary action of decomposing the imaging problem allows for accurate material property estimation while the actual capture process remains simple and quick, as the complex computational separation is done automatically by the trained neural network
Data Source
AI summary
Apparatuses, systems, and techniques are presented to determine visual properties of an object. In at least one embodiment, shape and visual properties of an object can be determined using differently illuminated images fed to a pipeline of neural networks.