123 research outputs found
Single-image RGB Photometric Stereo With Spatially-varying Albedo
We present a single-shot system to recover surface geometry of objects with
spatially-varying albedos, from images captured under a calibrated RGB
photometric stereo setup---with three light directions multiplexed across
different color channels in the observed RGB image. Since the problem is
ill-posed point-wise, we assume that the albedo map can be modeled as
piece-wise constant with a restricted number of distinct albedo values. We show
that under ideal conditions, the shape of a non-degenerate local constant
albedo surface patch can theoretically be recovered exactly. Moreover, we
present a practical and efficient algorithm that uses this model to robustly
recover shape from real images. Our method first reasons about shape locally in
a dense set of patches in the observed image, producing shape distributions for
every patch. These local distributions are then combined to produce a single
consistent surface normal map. We demonstrate the efficacy of the approach
through experiments on both synthetic renderings as well as real captured
images.Comment: 3DV 2016. Project page at http://www.ttic.edu/chakrabarti/rgbps
Learning Privacy Preserving Encodings through Adversarial Training
We present a framework to learn privacy-preserving encodings of images that
inhibit inference of chosen private attributes, while allowing recovery of
other desirable information. Rather than simply inhibiting a given fixed
pre-trained estimator, our goal is that an estimator be unable to learn to
accurately predict the private attributes even with knowledge of the encoding
function. We use a natural adversarial optimization-based formulation for
this---training the encoding function against a classifier for the private
attribute, with both modeled as deep neural networks. The key contribution of
our work is a stable and convergent optimization approach that is successful at
learning an encoder with our desired properties---maintaining utility while
inhibiting inference of private attributes, not just within the adversarial
optimization, but also by classifiers that are trained after the encoder is
fixed. We adopt a rigorous experimental protocol for verification wherein
classifiers are trained exhaustively till saturation on the fixed encoders. We
evaluate our approach on tasks of real-world complexity---learning
high-dimensional encodings that inhibit detection of different scene
categories---and find that it yields encoders that are resilient at maintaining
privacy.Comment: To appear in WACV 201
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Depth and Deblurring from a Spectrally-varying Depth-of-Field
We propose modifying the aperture of a conventional color camera so that the effective aperture size for one color channel is smaller than that for the other two. This produces an image where different color channels have different depths-of-field, and from this we can computationally recover scene depth, reconstruct an all-focus image and achieve synthetic re-focusing, all from a single shot. These capabilities are enabled by a spatio-spectral image model that encodes the statistical relationship between gradient profiles across color channels. This approach substantially improves depth accuracy over alternative single-shot coded-aperture designs, and since it avoids introducing additional spatial distortions and is light efficient, it allows high-quality deblurring and lower exposure times. We demonstrate these benefits with comparisons on synthetic data, as well as results on images captured with a prototype lens.Engineering and Applied Science
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