6D pose estimation of textureless shiny objects using random ferns for bin-picking

We address the problem of 6D pose estimation of a textureless and shiny object from single-view 2D images, for a bin-picking task. For a textureless object like a mechanical part, conventional visual feature matching usually fails due to the absence of rich texture features. Hierarchical template matching assumes that few templates can cover all object appearances. However, the appearance of a shiny object largely depends on its pose and illumination. Furthermore, in a bin-picking task, we must cope with partial occlusions, shadows, and inter-reflections.

[1]  Vincent Lepetit,et al.  A fast local descriptor for dense matching , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Joshua D. Schwartz,et al.  Hierarchical Matching of Deformable Shapes , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Marcus A. Magnor,et al.  A graphics hardware implementation of the generalized Hough transform for fast object recognition, scale, and 3D pose detection , 2003, 12th International Conference on Image Analysis and Processing, 2003.Proceedings..

[4]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[5]  Rama Chellappa,et al.  Pose estimation in heavy clutter using a multi-flash camera , 2010, 2010 IEEE International Conference on Robotics and Automation.

[6]  Cordelia Schmid,et al.  Bandit Algorithms for Tree Search , 2007, UAI.

[7]  Pushmeet Kohli,et al.  On Detection of Multiple Object Instances Using Hough Transforms , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Roberto Cipolla,et al.  Real-Time Visual Tracking of Complex Structures , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Vincent Lepetit,et al.  Fast Keypoint Recognition Using Random Ferns , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Juergen Gall,et al.  Class-specific Hough forests for object detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Youngbae Hwang,et al.  Sensor noise modeling using the Skellam distribution: Application to the color edge detection , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[13]  David G. Lowe,et al.  Three-Dimensional Object Recognition from Single Two-Dimensional Images , 1987, Artif. Intell..

[14]  Philip David,et al.  Object recognition in high clutter images using line features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[15]  Vincent Lepetit,et al.  Dominant orientation templates for real-time detection of texture-less objects , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  C. Steger.  OCCLUSION , CLUTTER , AND ILLUMINATION INVARIANT OBJECT RECOGNITION , 2002 .

[17]  Carsten Steger,et al.  Similarity Measures for Occlusion, Clutter, and Illumination Invariant Object Recognition , 2001, DAGM-Symposium.

[18]  Vincent Lepetit,et al.  Keypoint recognition using randomized trees , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  FuaPascal,et al.  Gradient Response Maps for Real-Time Detection of Textureless Objects , 2012 .

[20]  Markus Ulrich,et al.  CAD-based recognition of 3D objects in monocular images , 2009, 2009 IEEE International Conference on Robotics and Automation.