1,608 research outputs found
Semi-Global Stereo Matching with Surface Orientation Priors
Semi-Global Matching (SGM) is a widely-used efficient stereo matching
technique. It works well for textured scenes, but fails on untextured slanted
surfaces due to its fronto-parallel smoothness assumption. To remedy this
problem, we propose a simple extension, termed SGM-P, to utilize precomputed
surface orientation priors. Such priors favor different surface slants in
different 2D image regions or 3D scene regions and can be derived in various
ways. In this paper we evaluate plane orientation priors derived from stereo
matching at a coarser resolution and show that such priors can yield
significant performance gains for difficult weakly-textured scenes. We also
explore surface normal priors derived from Manhattan-world assumptions, and we
analyze the potential performance gains using oracle priors derived from
ground-truth data. SGM-P only adds a minor computational overhead to SGM and is
an attractive alternative to more complex methods employing higher-order
smoothness terms.Comment: extended draft of 3DV 2017 (spotlight) pape
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives
The importance of depth perception in the interactions that humans have
within their nearby space is a well established fact. Consequently, it is also
well known that the possibility of exploiting good stereo information would
ease and, in many cases, enable, a large variety of attentional and interactive
behaviors on humanoid robotic platforms. However, the difficulty of computing
real-time and robust binocular disparity maps from moving stereo cameras often
prevents from relying on this kind of cue to visually guide robots' attention
and actions in real-world scenarios. The contribution of this paper is
two-fold: first, we show that the Efficient Large-scale Stereo Matching
algorithm (ELAS) by A. Geiger et al. 2010 for computation of the disparity map
is well suited to be used on a humanoid robotic platform as the iCub robot;
second, we show how, provided with a fast and reliable stereo system,
implementing relatively challenging visual behaviors in natural settings can
require much less effort. As a case of study we consider the common situation
where the robot is asked to focus the attention on one object close in the
scene, showing how a simple but effective disparity-based segmentation solves
the problem in this case. Indeed this example paves the way to a variety of
other similar applications
GASP : Geometric Association with Surface Patches
A fundamental challenge to sensory processing tasks in perception and
robotics is the problem of obtaining data associations across views. We present
a robust solution for ascertaining potentially dense surface patch (superpixel)
associations, requiring just range information. Our approach involves
decomposition of a view into regularized surface patches. We represent them as
sequences expressing geometry invariantly over their superpixel neighborhoods,
as uniquely consistent partial orderings. We match these representations
through an optimal sequence comparison metric based on the Damerau-Levenshtein
distance - enabling robust association with quadratic complexity (in contrast
to hitherto employed joint matching formulations which are NP-complete). The
approach is able to perform under wide baselines, heavy rotations, partial
overlaps, significant occlusions and sensor noise.
The technique does not require any priors -- motion or otherwise, and does
not make restrictive assumptions on scene structure and sensor movement. It
does not require appearance -- is hence more widely applicable than appearance
reliant methods, and invulnerable to related ambiguities such as textureless or
aliased content. We present promising qualitative and quantitative results
under diverse settings, along with comparatives with popular approaches based
on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201
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