Covariance based point cloud descriptors for object detection and recognition

We introduce a covariance-based feature descriptor for object classification.The descriptor is compact (low dimensionality) and computationally fast.Adding new descriptor features amounts to the addition of a new row and column.There is no need to tune parameters such as bin size or number.The descriptor is naturally discriminative and subtracts out common data features. Processing 3D point cloud data is of primary interest in many areas of computer vision, including object grasping, robot navigation, and object recognition. The introduction of affordable RGB-D sensors has created a great interest in the computer vision community towards developing efficient algorithms for point cloud processing. Previously, capturing a point cloud required expensive specialized sensors such as lasers or dedicated range imaging devices; now, range data is readily available from low-cost sensors that provide easily extractable point clouds from a depth map. From here, an interesting challenge is to find different objects in the point cloud. Various descriptors have been introduced to match features in a point cloud. Cheap sensors are not necessarily designed to produce precise measurements, which means that the data is not as accurate as a point cloud provided from a laser or a dedicated range finder. Although some feature descriptors have been shown to be successful in recognizing objects from point clouds, there still exists opportunities for improvement. The aim of this paper is to introduce techniques from other fields, such as image processing, into 3D point cloud processing in order to improve rendering, classification, and recognition. Covariances have proven to be a success not only in image processing, but in other domains as well. This work develops the application of covariances in conjunction with 3D point cloud data.

[1]  Barry R. Masters,et al.  Digital Image Processing, Third Edition , 2009 .

[2]  Nico Blodow,et al.  Fast Point Feature Histograms (FPFH) for 3D registration , 2009, 2009 IEEE International Conference on Robotics and Automation.

[3]  Afzal Godil,et al.  3D Part identification based on local shape descriptors , 2008, PerMIS.

[4]  Fatih Murat Porikli,et al.  Covariance Tracking using Model Update Based on Lie Algebra , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[5]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.

[6]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Nikolaos Papanikolopoulos,et al.  Using a laser range finder mounted on a MicroVision robot to estimate environmental parameters , 2009, Defense + Commercial Sensing.

[8]  Guillermo Sapiro,et al.  Classification and clustering via dictionary learning with structured incoherence and shared features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Dieter Fox,et al.  Object recognition with hierarchical kernel descriptors , 2011, CVPR 2011.

[10]  Roland Siegwart,et al.  A state-of-the-art 3D sensor for robot navigation , 2004 .

[11]  W. Förstner,et al.  A Metric for Covariance Matrices , 2003 .

[12]  Martin A. Riedmiller,et al.  A learned feature descriptor for object recognition in RGB-D data , 2012, 2012 IEEE International Conference on Robotics and Automation.

[13]  Martial Hebert,et al.  Large data sets and confusing scenes in 3-D surface matching and recognition , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

[14]  Chun Chen,et al.  Speech Emotion Classification on a Riemannian Manifold , 2008, PCM.

[15]  Richard K. Beatson,et al.  Reconstruction and representation of 3D objects with radial basis functions , 2001, SIGGRAPH.

[16]  Takashi Masuko,et al.  Covariance clustering on Riemannian manifolds for acoustic model compression , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[17]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Dieter Fox,et al.  Unsupervised Feature Learning for RGB-D Based Object Recognition , 2012, ISER.

[19]  Xuelong Li,et al.  Gabor-Based Region Covariance Matrices for Face Recognition , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[20]  Ioannis Stamos,et al.  3D modeling of historic sites using range and image data , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[21]  Nikolaos Papanikolopoulos,et al.  Impact orientation invariant robot design: an approach to projectile deployed robotic platforms , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[22]  Satyandra K. Gupta,et al.  Retrieving Matching CAD Models by Using Partial 3D Point Clouds , 2007 .

[23]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[24]  Stephen J. Maybank,et al.  Human Action Recognition under Log-Euclidean Riemannian Metric , 2009, ACCV.

[25]  Alberto Del Bimbo,et al.  Content-Based Retrieval of 3-D Objects Using Spin Image Signatures , 2007, IEEE Transactions on Multimedia.

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

[27]  Thomas S. Huang,et al.  Emotion Recognition from Arbitrary View Facial Images , 2010, ECCV.

[28]  William J. Beksi,et al.  The Microvision Robot and its Capabilities , 2015 .

[29]  Michael Elad,et al.  Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.

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

[31]  Pieter Abbeel,et al.  A textured object recognition pipeline for color and depth image data , 2012, 2012 IEEE International Conference on Robotics and Automation.

[32]  Alexander M. Bronstein,et al.  Numerical Geometry of Non-Rigid Shapes , 2009, Monographs in Computer Science.

[33]  Dieter Fox,et al.  Depth kernel descriptors for object recognition , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[34]  N. Ayache,et al.  Log‐Euclidean metrics for fast and simple calculus on diffusion tensors , 2006, Magnetic resonance in medicine.

[35]  Remco C. Veltkamp,et al.  A Survey of Content Based 3D Shape Retrieval Methods , 2004, SMI.

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

[37]  Hans-Gerd Maas,et al.  3D BUILDING MODEL GENERATION FROM AIRBORNE LASER SCANNER DATA USING 2D GIS DATA AND ORTHOGONAL POINT CLOUD PROJECTIONS , 2005 .

[38]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[39]  Chandrajit L. Bajaj,et al.  Automatic reconstruction of surfaces and scalar fields from 3D scans , 1995, SIGGRAPH.

[40]  Nikolaos Papanikolopoulos,et al.  The Design and Evolution of the eROSI Robot , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.