Compressive Structured Light for Recovering Inhomogeneous Participating Media

Abstract

Abstract-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.

Compressed sensing for practical optical imaging systems: a tutorial

“…In related work, objects are illuminated by light sources with tunable spectra using spatial light modulators to facilitate compressive spectral image acquisition. 46 Finally, we mention more recent examples of compressive spectral imagers, such as compressive structured light codes where each camera pixel measures light from points along the line of sight within a volume density, 47 and cameras that use dispersers for imaging piecewise "macropixel" objects (e.g., biochip microarrays in biochemistry). …”

“…Other recent works include the application of CS theory to radar imaging 51 and the recovery of volumetric densities associated with translucent media (e.g., smoke, clouds, etc.). 52 Still, other CS optical systems have also been proposed in a variety of applications including DNA microarrays, 53,54 magnetic resonance imaging (MRI), 55 ground penetrating radar, 56 confocal microscopy, 57 and astronomical imaging. …”

Illumination Recovery From Image With Cast Shadows Via Sparse Representation

“…Recent studies [5], [9] show that, under very flexible conditions, the 0 minimization can be reduced to 1 minimization that further results in a convex optimization, which can be solved efficiently. The results from compressed sensing have been applied to different computer vision tasks [45] for problems such as face recognition [46], background subtraction [15], [6], media recovery [10], visual tracking [21], texture segmentation and feature selection [19]. In this work, we show that the number of directional sources needed to approximate the lighting is greatly compressible and the illumination recovery can be cast as an 1 -regularized least squares problem.…”

Sparse reconstruction of visual appearance for computer graphics and vision

“…This is indeed the case in applications like appearance acquisition, where light is obtained from a projector, and an arbitrary pattern can be used. 29,30 However, the application domains discussed in the previous section admit many different cost metrics. In Monte Carlo rendering and its application to precompute light transport matrices, the cost is usually per ray, or for each rendering sample.…”