Class-specific grasping of 3D objects from a single 2D image
Author(s)
Chiu, Han-Pang; Liu, Huan; Kaelbling, Leslie P.; Lozano-Perez, Tomas
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Our goal is to grasp 3D objects given a single image, by using prior 3D shape models of object classes. The shape models, defined as a collection of oriented primitive shapes centered at fixed 3D positions, can be learned from a few labeled images for each class. The 3D class model can then be used to estimate the 3D shape of a detected object, including occluded parts, from a single image. The estimated 3D shape is used as to select one of the target grasps for the object. We show that our 3D shape estimation is sufficiently accurate for a robot to successfully grasp the object, even in situations where the part to be grasped is not visible in the input image.
Date issued
2010-12Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
Proceedings fo the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
Institute of Electrical and Electronics Engineers
Citation
Han-Pang Chiu et al. “Class-specific grasping of 3D objects from a single 2D image.” Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on. 2010. 579-585. © Copyright 2010 IEEE
Version: Final published version
Other identifiers
INSPEC Accession Number: 11689223
ISBN
978-1-4244-6674-0
ISSN
2153-0858