NDLI: Single image 3D object detection and pose estimation for grasping

Content Provider	IEEE Xplore Digital Library
Author	Menglong Zhu Derpanis, K.G. Yinfei Yang Brahmbhatt, S. Zhang, M. Phillips, C. Lecce, M. Daniilidis, K.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., Ryerson Univ., Toronto, ON, Canada (Derpanis, K.G.) \|\| Dept. of Comput. Inf. Sci., Univ. of Pennsylvania, Philadelphia, PA, USA (Menglong Zhu; Yinfei Yang; Brahmbhatt, S.; Zhang, M.; Phillips, C.; Lecce, M.; Daniilidis, K.)
Abstract	We present a novel approach for detecting objects and estimating their 3D pose in single images of cluttered scenes. Objects are given in terms of 3D models without accompanying texture cues. A deformable parts-based model is trained on clusters of silhouettes of similar poses and produces hypotheses about possible object locations at test time. Objects are simultaneously segmented and verified inside each hypothesis bounding region by selecting the set of superpixels whose collective shape matches the model silhouette. A final iteration on the 6-DOF object pose minimizes the distance between the selected image contours and the actual projection of the 3D model. We demonstrate successful grasps using our detection and pose estimate with a PR2 robot. Extensive evaluation with a novel ground truth dataset shows the considerable benefit of using shape-driven cues for detecting objects in heavily cluttered scenes.
Starting Page	3936
Ending Page	3943
File Size	2847137
Page Count	8
File Format	PDF
ISBN	9781479936854
DOI	10.1109/ICRA.2014.6907430
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Three-dimensional displays Solid modeling Shape Computational modeling Image edge detection Feature extraction Image segmentation
Content Type	Text
Resource Type	Article

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