Global Illumination Compensation for Spatially Augmented Reality

Yu Sheng, Theodore C. Yapo, and Barbara Cutler
To appear in Computer Graphics Forum, Eurographics 2010
paper (pdf, 34M)     video (mpg format, 17M)

Abstract: When projectors are used to display images on complex, non-planar surface geometry, indirect illumination between the surfaces will disrupt the final appearance of this imagery, generally increasing brightness, decreasing contrast, and washing out colors. In this paper we predict through global illumination simulation this unintentional indirect component and solve for the optimal compensated projection imagery that will minimize the difference between the desired imagery and the actual total illumination in the resulting physical scene. Our method makes use of quadratic programming to minimize this error within the constraints of the physical system, namely, that negative light is physically impossible. We demonstrate our compensation optimization in both computer simulation and physical validation within a table-top spatially augmented reality system. We present an application of these results for visualization of interior architectural illumination. To facilitate interactive modifications to the scene geometry and desired appearance, our system is accelerated with a CUDA implementation of the QP optimization method.

Geometry & materials Desired appearance Uncompensated Exact solution(+)
Exact solution(-) Clamped simulation Our solution Our simulation


Desired Clamped inverse Least squares Our result L*a*b*

Comparison of different algorithms. Our result is definitely better than the clamped inverse and least squares method, and comparable with non-linear optimization in L*a*b* color space

We also validated our algorithm with in our SAR daylighting simulation system, here are some of the results.

Desired appearance Uncompensated Clamped inverse Our method

See also:
RPI Computer Graphics Group
Research Project: Architectural Daylighting
Research Project: Dynamic Projection Surfaces in EMPAC

This material is based upon work supported by the National Science Foundation under Grant No. CMMI 0841319 and Grant No. IIS 0845401. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

This work was also supported by a grant from IBM.