Environment Light Map Selection for Image Simulation
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Solution Overview
Problem
Preparing an environment light map to accurately simulate the color and gloss of an article at an observation location is time-consuming and impractical, making it difficult to reproduce how an article would look in different illumination conditions.
Innovation Solution
An information processing apparatus that acquires a feature amount related to brightness distribution from a captured image at an observation location, selects a suitable environment light map from pre-prepared maps, and controls the expression of a second image using the selected light map to simulate the article's appearance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an environment light map is prepared in advance to simulate article appearance, then the accuracy of color and gloss simulation is improved, but the time and effort required to prepare the light map increases
Solution Approach 1:
Multiple environment light maps are prepared in advance and stored in a database, covering various illumination conditions. This preliminary preparation eliminates the need to create light maps on-demand, resolving the contradiction by having accurate simulation data ready beforehand without spending time during actual simulation tasks
Solution Approach 2:
The system automatically selects from pre-prepared light maps based on brightness distribution parameters extracted from input images. By changing selection criteria from manual choice to automated parameter-based selection, the system maintains high simulation accuracy while eliminating time-consuming manual light map preparation
2Reliability
If an environment light map is prepared in advance, then the quality of illumination simulation is improved, but the complexity of the process increases
Solution Approach 1:
The system automatically extracts brightness distribution features from input images and selects appropriate environment light maps without manual intervention. This self-service approach maintains high simulation quality while reducing process complexity by eliminating manual light map selection steps
Solution Approach 2:
The system uses feedback from brightness distribution analysis to automatically select the most appropriate pre-prepared light map. This closed-loop approach ensures high simulation quality while simplifying the process through automated decision-making based on image characteristics
3Adaptability or versatility
If environment light maps are prepared for various observation locations, then the versatility of simulation is improved, but the time required to prepare maps for each location increases
Solution Approach 1:
Environment light maps for multiple observation locations and illumination conditions are prepared in advance and stored in a database. This preliminary action enables the system to handle diverse simulation requests without time-consuming on-site light map creation, resolving the contradiction between versatility and preparation efficiency
Solution Approach 2:
A single database stores multiple environment light maps that can serve various observation locations and simulation needs. This universal repository allows the system to adapt to different scenarios without requiring separate preparation processes for each location, maintaining both versatility and efficiency
Data Source
AI summary
An information processing apparatus includes a processor configured to: acquire a feature amount related to brightness distribution from a first image captured at an observation location; select an environment light map that is similar to the feature amount, from among plural environment light maps prepared in advance; and control expression of a second image corresponding to an article observed at the observation location using the selected environment light map.