Non-parametric Acquisition of near-Dirac Pixel Correspondences

Many computer vision and graphics applications require the acquisition of correspondences between the pixels of a 2D illumination pattern and those of captured 2D photographs. Trivial cases with only one-to-one correspondences require only a few measurements. In more general scenes containing complex inter-reflections, capturing the full reflection field requires more extensive sampling and complex processing schemes. We present a method that addresses the middle-ground: scenes where each pixel maps to a small, compact set of pixels that cannot easily be modeled parametrically. The coding method is based on optically-constructed Bloom filters and frequency coding. It is non-adaptive, allowing fast acquisition, robust to measurement noise, and can be decoded with only moderate computational power. It requires fewer measurements and scales up to higher resolutions more efficiently than previous methods.

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Non-parametric Acquisition of near-Dirac Pixel Correspondences

Semantic Scholar · Computer Science · 2012

Abstract

Many computer vision and graphics applications require the acquisition of correspondences between the pixels of a 2D illumination pattern and those of captured 2D photographs. Trivial cases with only one-to-one correspondences require only a few measurements. In more general scenes containing complex inter-reflections, capturing the full reflection field requires more extensive sampling and complex processing schemes. We present a method that addresses the middle-ground: scenes where each pixel maps to a small, compact set of pixels that cannot easily be modeled parametrically. The coding method is based on optically-constructed Bloom filters and frequency coding. It is non-adaptive, allowing fast acquisition, robust to measurement noise, and can be decoded with only moderate computational power. It requires fewer measurements and scales up to higher resolutions more efficiently than previous methods.

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