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Image-based Lighting Design

Explore how to illuminate and enhance real-life objects by painting light on photographs using advanced image-based lighting techniques. This innovative approach involves utilizing high dynamic range (HDR) images and a meticulous process to design and optimize lighting effects. Learn how to simulate realistic light intensities and create visually stunning compositions. Stay ahead in lighting design with this cutting-edge method.

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Image-based Lighting Design

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  1. Image-based Lighting Design 8 Sept. 2004 Frederik Anrys Philip Dutré Computer Graphics Group

  2. Light intensities are determined by using the photograph. Overview Paint a design on a photograph of a real life object.

  3. Previous Work • Lighting Design • Sketching highlights and shadows to position lights (Poulin ‘97) • Painting with Light (Schoeneman ‘93) • Image-based Relighting • A lighting reproduction approach to live-action composition (Debevec ‘02)

  4. Overview

  5. Overview

  6. Overview

  7. Overview

  8. Light Stage (Debevec) • 40 IColor MR RGB Led Lights • Simulate the visible spectrum once calibrated Acquisition: Lights

  9. Acquisition: Camera • Canon EOS D30 digital Camera • High Dynamic Range (HDR) Images • Luminance values are represented in full floating point values instead of 8-bit RGB values used for display. • No compression using a response curve. • Usually constructed by taking a series of photographs

  10. Overview

  11. Basis images • For each light source, construct a HDR image. • Linearize each HDR image into a vector . • Store each vector into matrix A.

  12. Design Specification • Start painting desired lighting design using Adobe Photoshop. • Adjust color, contrast, add shadows, … • Linearize it into vector Y.

  13. Overview

  14. Optimization Find x such that y A is minimal

  15. Optimization • Ax resides in luminance space • y resides in display space • F converts Ax to display space • Why? The L2 norm is well defined and fast method for comparing images in display space. • F is standard Gamma correction function

  16. Optimization • Objective function is non-linear. • Constrained in • added for favoring regions/pixels

  17. Optimization • The problem is solved by Sequential Quadratic Programming (SQP). • Gradient is computed analytically. • Optimization takes 15 seconds to complete: • 40 lightsources (3 channels). • P3-1.2 Ghz computer.

  18. Target Result Target Result Target Result Results

  19. Results

  20. Result Result Target Target Results

  21. Future Work • Determining light positions/directions. • Multiple camera viewpoints. • Give designer interactive feedback.

  22. Thank you all. Questions? Acknowledgements • Graphics group from K.U.Leuven. • Artists for lending out their work.

  23. Virtual Applying configuration to Light Stage: Reality check Target Real

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