Robotics: Science and Systems XII

Seeing Glassware: from Edge Detection to Pose Estimation and Shape Recovery

Cody J. Phillips, Matthieu Lecce, Kostas Daniilidis

Abstract:

Perception of transparent objects has been an open challenge in robotics despite advances in sensors and data- driven learning approaches. In this paper, we introduce a new approach that combines recent advances in learnt object detectors with perceptual grouping in 2D, and projective geometry of apparent contours in 3D. We train a state of the art structured edge detector on an annotated set of foreground glassware. We assume that we deal with surfaces of revolution (SOR) and apply perceptual symmetry grouping in a 2D spherical transformation of the image to obtain a 2D detection of the glassware object and a hypothesis about its 2D axis. Rather than stopping at a single view detection, we ultimately want to reconstruct the 3D shape of the object and its 3D pose to allow for a robot to grasp it. Using two views allows us to decouple the 3D axis localization from the shape estimation. We develop a parametrization that uniquely relates the shape reconstruction of SOR to given a set of contour points and tangents. Finally, we provide the first annotated dataset for 2D detection, 3D pose and 3D shape of glassware and we show results comparable to category-based detection and localization of opaque objects without any training on the object shape.

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Bibtex:

  
@INPROCEEDINGS{Phillips-RSS-16, 
    AUTHOR    = {Cody J. Phillips AND Matthieu Lecce AND Kostas Daniilidis}, 
    TITLE     = {Seeing Glassware: from Edge Detection to Pose Estimation and Shape Recovery}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2016}, 
    ADDRESS   = {AnnArbor, Michigan}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2016.XII.021} 
}