FINDDD: A Fast 3D Descriptor to Characterize Textiles for Robot Manipulation
Arnau Ramisa, Guillem AlenyĆ , Francesc Moreno-Noguer and Carme Torras
Most current depth sensors provide 2.5D range images in
which depth values are assigned to a rectangular 2D
array. With the Fast Integral Normal 3D (FINDDD) descriptor
we take advantage of this structured information to build an
efficient shape descriptor which is about two orders of
magnitude faster than competing approaches, while showing
similar performance in several tasks involving deformable
object recognition. Given a 2D patch surrounding a point and
its associated depth values, we build the descriptor for
that point, based on the cumulative distances between their
normals and a discrete set of normal directions. This
processing is made very efficient using integral images,
even allowing to densely compute descriptors for every point
in the image in a few seconds.
The steps to compute FINDDD descriptors can be summarized as follows:
- The normal vector of every point in the input
structured point cloud is computed. This step can be done
very fast using integral images.
- For each normal computed in the previous step, we
construct a vector of size B with the votes based on the
distance between the normal and the neighboring
orientation bin centers.
- To compute a descriptor, all the previously computed
vectors within a sub-region are added into an orientation
histogram. Then, the orientation histograms of all
sub-regions in the support area are concatenated to form
the final descriptor vector.
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Finally, each descriptor is normalized using the L1 norm
to make it robust to different densities in the number of
points (i.e. NaN points, caused by occlusions or noisy
information, are discarded). We found it beneficial to
keep local density information by only normalizing at the
global level.
Example application: detection of lapel
points. FINDDD Descriptors are extracted for all points
within the collar area (red bounding box, detected using a
previous method) and a logistic regression model is
used to rank the points according to their probability
of belonging to the lapel of the collar. The small
figure inside each picture shows the lapel probability
map, with edges superimposed for clarity.
Presentation
There is a short video explaining this work:
Reference
- FINDDD: A fast 3D descriptor to characterize textiles for robot manipulation , A. Ramisa, G. Alenya, F. Moreno-Noguer and C. Torras; In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 824-830, 2013
- Determining Where to Grasp Cloth Using Depth Information, A. Ramisa, G. Alenya, F. Moreno-Noguer and C. Torras; In Frontiers in Artificial Intelligence and Applications: Artificial Intelligence Research and Development , vol. 232, pp. 199-207, 2011
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