Compressive Structured Light for Recovering Inhomogeneous Participating Media

  • Jinwei Gu
  • Shree Nayar
  • Eitan Grinspun
  • Peter Belhumeur
  • Ravi Ramamoorthi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)


We propose a new method named compressive structured light for recovering inhomogeneous participating media. Whereas conventional structured light methods emit coded light patterns onto the surface of an opaque object to establish correspondence for triangulation, compressive structured light projects patterns into a volume of participating medium to produce images which are integral measurements of the volume density along the line of sight. For a typical participating medium encountered in the real world, the integral nature of the acquired images enables the use of compressive sensing techniques that can recover the entire volume density from only a few measurements. This makes the acquisition process more efficient and enables reconstruction of dynamic volumetric phenomena. Moreover, our method requires the projection of multiplexed coded illumination, which has the added advantage of increasing the signal-to-noise ratio of the acquisition. Finally, we propose an iterative algorithm to correct for the attenuation of the participating medium during the reconstruction process. We show the effectiveness of our method with simulations as well as experiments on the volumetric recovery of multiple translucent layers, 3D point clouds etched in glass, and the dynamic process of milk drops dissolving in water.


Attenuation Correction Compressive Sensing Volume Density Measurement Cost Spatial Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jinwei Gu
    • 1
  • Shree Nayar
    • 1
  • Eitan Grinspun
    • 1
  • Peter Belhumeur
    • 1
  • Ravi Ramamoorthi
    • 1
  1. 1.Columbia UniversityNew YorkUSA

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