Integration of Probabilistic Pose Estimates from Multiple Views

We propose an approach to multi-view object detection and pose estimation that considers combinations of single-view estimates. It can be used with most existing single-view pose estimation systems, and can produce improved results even if the individual pose estimates are incoherent. The method is introduced in the context of an existing, probabilistic, view-based detection and pose estimation method (PAPE), which we here extend to incorporate diverse attributes of the scene. We tested the multiview approach with RGB-D cameras in different environments containing several cluttered test scenes and various textured and textureless objects. The results show that the accuracies of object detection and pose estimation increase significantly over single-view PAPE and over other multiple-view integration methods.

[1]  Vincent Lepetit,et al.  Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes , 2011, 2011 International Conference on Computer Vision.

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Luc Van Gool,et al.  The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.

[4]  Bernt Schiele,et al.  3D Object Detection with Multiple Kinects , 2012, ECCV Workshops.

[5]  Federico Tombari,et al.  Unique Signatures of Histograms for Local Surface Description , 2010, ECCV.

[6]  James J. Little,et al.  Multiple Viewpoint Recognition and Localization , 2010, ACCV.

[7]  Pascal Fua,et al.  Conditional Random Fields for multi-camera object detection , 2011, 2011 International Conference on Computer Vision.

[8]  Markus Vincze,et al.  Automation of “ground truth” annotation for multi-view RGB-D object instance recognition datasets , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Justus H. Piater,et al.  Multiview feature distributions for object detection and continuous pose estimation , 2014, Comput. Vis. Image Underst..

[10]  Vincent Lepetit,et al.  Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes , 2012, ACCV.

[11]  Vincent Lepetit,et al.  Learning descriptors for object recognition and 3D pose estimation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Christian Perwass,et al.  Increasing pose estimation performance using multi-cue integration , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[13]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Eric Brachmann,et al.  Learning 6D Object Pose Estimation Using 3D Object Coordinates , 2014, ECCV.

[15]  Luc Van Gool,et al.  Hough Forests for Object Detection, Tracking, and Action Recognition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[17]  Stefan Roth,et al.  Object Detection in Multi-view X-Ray Images , 2012, DAGM/OAGM Symposium.

[18]  Tae-Kyun Kim,et al.  Latent-Class Hough Forests for 3D Object Detection and Pose Estimation , 2014, ECCV.

[19]  Rama Chellappa,et al.  Fast directional chamfer matching , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Subhransu Maji,et al.  Multiple-View Object Recognition in Smart Camera Networks , 2011 .

[21]  Darius Burschka,et al.  An Efficient RANSAC for 3D Object Recognition in Noisy and Occluded Scenes , 2010, ACCV.

[22]  Andrew W. Fitzgibbon,et al.  KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera , 2011, UIST.

[23]  Gary R. Bradski,et al.  Fast 3D recognition and pose using the Viewpoint Feature Histogram , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[24]  Andrew Y. Ng,et al.  Multi-camera object detection for robotics , 2010, 2010 IEEE International Conference on Robotics and Automation.

[25]  Norbert Krüger,et al.  Multi-view object recognition using view-point invariant shape relations and appearance information , 2013, 2013 IEEE International Conference on Robotics and Automation.

[26]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .