Abstract
We present a technique for relighting an image such that different areas of the image are illuminated with different combinations of lighting directions. The approach is to capture a 4D reflectance field using a light stage, calculate the radial basis function interpolation of light constraints specified by users, and render the calculated illumination result in real-time using the GPU.
FAQs
AI
What are the key advantages of using GPU over traditional methods in relighting?
The study demonstrates that leveraging GPU computing allows for real-time rendering of complex lighting conditions, achieving a 480 × 270 pixel result within seconds. This is enhanced through multi-threading, which simultaneously processes user inputs and renders updates.
How is the reflectance field captured and what are its dimensions?
A stationary high-speed camera captures data through a synchronized lighting system, with illumination from 480 lighting directions derived from 32 longitudinal and 15 latitude strobing directions. This captures detailed reflectance data essential for accurate lighting effects.
What user-defined constraints can be applied during lighting manipulation?
Users can define specific lighting constraints by selecting directions, colors, and intensities at designated image locations. This customization enables the creation of intricate lighting effects, allowing for artistic manipulations not typically achievable with conventional methods.
How does the radial basis function interpolation enhance lighting calculations?
The application uses Gaussian radial basis function interpolation for combining user constraints, providing smoother transitions between lighting states. This method allows for advanced visualization, as users can see interpolated coefficients represented as a falsecolor image for enhanced interaction.
What is the maximum resolution achieved in final rendering, and how is it optimized?
The application can render high-resolution outputs at 1920 × 1080 pixels in three to four minutes by loading a 3GB reflectance field. Optimization occurs through a dual-resolution approach, processing lower-resolution outputs in real-time while rendering high-resolution updates in the background.
References (2)
- AKERS, D., LOSASSO, F., KLINGNER, J., AGRAWALA, M., RICK, J., AND HANRAHAN, P. 2003. Conveying shape and features with image- based relighting. In VIS '03: Proceedings of the 14th IEEE Visualization 2003 (VIS'03), IEEE Computer Society, Washington, DC, USA, 46.
- HAWKINS, T., WENGER, A., TCHOU, C., GARDNER, A., G ÖRANSSON, F., AND DEBEVEC, P. 2004. Animatable facial reflectance fields. In Rendering Techniques 2004: 15th Eurographics Workshop on Render- ing, 309-320.