Glass object localization by joint inference of boundary and depth

We address the problem of localizing glass objects with a multimodal RGB-D camera. Our method integrates the intensity and depth information from a single view point, and builds a Markov Random Field that predicts glass boundary and region jointly. Based on the localization, we also reconstruct the depth of the scene and fill in the missing depth values. The efficacy of our algorithm is validated on a new RGB-D Glass dataset of 43 distinct glass objects.

[1]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[2]  Jean Ponce,et al.  A Geodesic Active Contour Framework for Finding Glass , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  Jitendra Malik,et al.  Contour and Texture Analysis for Image Segmentation , 2001, International Journal of Computer Vision.

[4]  Dieter Fox,et al.  Sparse distance learning for object recognition combining RGB and depth information , 2011, 2011 IEEE International Conference on Robotics and Automation.

[5]  Dieter Fox,et al.  A large-scale hierarchical multi-view RGB-D object dataset , 2011, 2011 IEEE International Conference on Robotics and Automation.

[6]  David W. Jacobs,et al.  Using specularities for recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[7]  David A. Forsyth,et al.  Finding glass , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Trevor Darrell,et al.  An Additive Latent Feature Model for Transparent Object Recognition , 2009, NIPS.

[9]  Andrew M. Wallace,et al.  3D Imaging of Transparent Objects , 2000, BMVC.

[10]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Wolfram Burgard,et al.  Learning the elasticity parameters of deformable objects with a manipulation robot , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Hiroshi Murase.  Surface shape reconstruction of an undulating transparent object , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[13]  Edward H. Adelson,et al.  Ordinal characteristics of transparency. , 1990 .

[14]  Michael Beetz,et al.  Transparent object detection and reconstruction on a mobile platform , 2011, 2011 IEEE International Conference on Robotics and Automation.