by U Klank, MZ Zia and M Beetz
Abstract:
We propose a new method for automatically accessing an internet database of 3D models that are searchable only by their user-annotated labels, for using them for vision and robotic manipulation purposes. Instead of having only a local database containing already seen objects, we want to use shared databases available over the internet. This approach while having the potential to dramatically increase the visual recognition capability of robots, also poses certain problems, like wrong annotation due to the open nature of the database, or overwhelming amounts of data (many 3D models) or the lack of relevant data (no models matching a specified label). To solve those problems we propose the following: First, we present an outlier/inlier classification method for reducing the number of results and discarding invalid 3D models that do not match our query. Second, we utilize an approach from computer graphics, the so called 'morphing', to this application to specialize the models, in order to describe more objects. Third, we search for 3D models using a restricted search space, as obtained from our knowledge of the environment. We show our classification and matching results and finally show how we can recover the correct scaling with the stereo setup of our robot.
Reference:
3D Model Selection from an Internet Database for Robotic Vision (U Klank, MZ Zia and M Beetz), In International Conference on Robotics and Automation (ICRA), 2009.
Bibtex Entry:
@inproceedings{klank_3d_2009,
author = {U Klank and MZ Zia and M Beetz},
title = {{3D} Model Selection from an Internet Database for Robotic Vision},
booktitle = {International Conference on Robotics and Automation ({ICRA)}},
year = {2009},
pages = {2406–2411},
abstract = {We propose a new method for automatically accessing an internet database
of {3D} models that are searchable only by their user-annotated labels,
for using them for vision and robotic manipulation purposes. Instead
of having only a local database containing already seen objects,
we want to use shared databases available over the internet. This
approach while having the potential to dramatically increase the
visual recognition capability of robots, also poses certain problems,
like wrong annotation due to the open nature of the database, or
overwhelming amounts of data (many {3D} models) or the lack of relevant
data (no models matching a specified label). To solve those problems
we propose the following: First, we present an outlier/inlier classification
method for reducing the number of results and discarding invalid
{3D} models that do not match our query. Second, we utilize an approach
from computer graphics, the so called 'morphing', to this application
to specialize the models, in order to describe more objects. Third,
we search for {3D} models using a restricted search space, as obtained
from our knowledge of the environment. We show our classification
and matching results and finally show how we can recover the correct
scaling with the stereo setup of our robot.},
}