153 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
Depth Super-Resolution Meets Uncalibrated Photometric Stereo
A novel depth super-resolution approach for RGB-D sensors is presented. It
disambiguates depth super-resolution through high-resolution photometric clues
and, symmetrically, it disambiguates uncalibrated photometric stereo through
low-resolution depth cues. To this end, an RGB-D sequence is acquired from the
same viewing angle, while illuminating the scene from various uncalibrated
directions. This sequence is handled by a variational framework which fits
high-resolution shape and reflectance, as well as lighting, to both the
low-resolution depth measurements and the high-resolution RGB ones. The key
novelty consists in a new PDE-based photometric stereo regularizer which
implicitly ensures surface regularity. This allows to carry out depth
super-resolution in a purely data-driven manner, without the need for any
ad-hoc prior or material calibration. Real-world experiments are carried out
using an out-of-the-box RGB-D sensor and a hand-held LED light source.Comment: International Conference on Computer Vision (ICCV) Workshop, 201
Unifying diffuse and specular reflections for the photometric stereo problem
This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/WACV.2016.7477643After thirty years of researching, the photometric stereo technique for 3D shape recovery still does not provide reliable results if it is not constrained into very well-controlled scenarios. In fact, dealing with realistic materials and lightings yields a non-linear bidirectional reflectance distribution function which is primarily difficult to parametrize and then arduous to solve. With the aim to let the photometric stereo approach face more realistic assumptions, in this work we firstly introduce a unified irradiance equation describing both diffuse and specular reflection components in a general lighting setting. After that, we define a new equation we call unifying due to its basic features modeling the photometric stereo problem for heterogeneous materials. It is provided by making the ratio of irradiance equations holding both diffuse and specular reflections as well as non-linear light propagation features simultaneously. Performing a wide range of experiments, we show that this new approach overcomes state-of-the-art since it leads to a system of unifying equations which can be solved in a very robust manner using an efficient variational approach.Experimental setups were provided by Toulouse Tech Transfer, and this collaboration was funded by CNRS GdR 2286 (MIA)
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