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Compressive Structured Light for Recovering Inhomogeneous Participating Media
March 2013 (vol. 35 no. 3)
pp. 1
Jinwei Gu, Center for Imaging Sci., Rochester Inst. of Technol., Rochester, NY, USA
S. K. Nayar, Columbia Univ., New York, NY, USA
E. Grinspun, Columbia Univ., New York, NY, USA
P. N. Belhumeur, Columbia Univ., New York, NY, USA
R. Ramamoorthi, Univ. of California, Berkeley, Berkeley, CA, USA
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.
Index Terms:
Image reconstruction,Cameras,Media,Atmospheric measurements,Particle measurements,Volume measurement,Spatial resolution,Image Representation,Image reconstruction,Cameras,Media,Atmospheric measurements,Particle measurements,Volume measurement,Spatial resolution,Volumetric,Computing Methodologies,Image Processing and Computer Vision,Scene Analysis,Photometry,Artificial Intelligence,Applications and Expert Knowledge-Intensive Systems,Computer vision,Vision and Scene Understanding,Modeling and recovery of physical attributes
Citation:
Jinwei Gu, S. K. Nayar, E. Grinspun, P. N. Belhumeur, R. Ramamoorthi, "Compressive Structured Light for Recovering Inhomogeneous Participating Media," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 3, pp. 1, March 2013, doi:10.1109/TPAMI.2012.130
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