Submitted by bradski on Thu, 09/16/2010 - 18:42
| Title | Depth-Encoded Hough Voting for Joint Object Detection and Shape Recovery |
| Publication Type | Conference Paper |
| Year of Publication | 2010 |
| Authors | Sun, Min., Xu, Bing-Xin., Bradski, Gary., and Savarese, Silvio |
| Conference Name | ECCV |
| Date Published | 09/2010 |
| Conference Location | Crete, Greece |
| Keywords | 3D shape, AI, computer vision, perception |
| Abstract | Detecting objects, estimating their pose and recovering 3D
shape information is a critical problem in many vision and robotics ap-
plications. This paper addresses the above needs by proposing a new
method called DEHV - Depth-Encoded Hough Voting detection scheme.
Inspired by the Hough voting scheme introduced in [13], DEHV incor-
porates depth information into the process of learning distributions of
image features (patches) representing an object category. DEHV takes
advantage of the interplay between the scale of each object patch in the
image and its distance (depth) from the corresponding physical patch
attached to the 3D object. In training, we use various views of an object
using a 2D image and its associated depth map (which we assume is avail-
able in learning). In testing, DEHV jointly detects objects, infers their
categories, estimates their pose, and infers/decodes objects depth maps
from either a single image (when no depth maps are available in testing)
or a single image augmented with depth map (when this is available in
testing). Extensive quantitative and qualitative experimental analysis on
existing datasets [6,9,22] and a newly proposed 3D table-top object cat-
egory dataset shows that our DEHV scheme obtains competitive detec-
tion and pose estimation results on all the dataset. Most importantly, we
demonstrate (with quantitative and qualitative evaluation) that DEHV
is capable to reconstruct the 3D shape of the object from just one single
uncalibrated image. Finally, we demonstrate that our technique can be
successfully employed as a key building block in two application scenar-
ios (highly accurate 6 degree of freedom (6 DOF) pose estimation and
3D object modeling).
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| URL | http://www.ics.forth.gr/eccv2010/program.php |
