Instance-based object recognition in 3D point clouds using discriminative shape primitives

Abstract3D local shapes are a critical cue for object recognition in 3D point clouds. This paper presents an instance-based 3D object recognition method via informative and discriminative shape primitives. We propose a shape primitive model that measures geometrical informativity and discriminativity of 3D local shapes of an object. Discriminative shape primitives of the object are extracted automatically by model parameter optimization. We achieve object recognition from 2.5/3D scenes via shape primitive classification and recover the 3D poses of the identified objects simultaneously. The effectiveness and the robustness of the proposed method were verified on popular instance-based 3D object recognition datasets. The experimental results show that the proposed method outperforms some existing instance-based 3D object recognition pipelines in the presence of noise, varying resolutions, clutter and occlusion.

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

[2]  Martial Hebert,et al.  Data-Driven 3D Primitives for Single Image Understanding , 2013, 2013 IEEE International Conference on Computer Vision.

[3]  Mohammed Bennamoun,et al.  On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes , 2009, International Journal of Computer Vision.

[4]  Mathieu Aubry,et al.  Painting-to-3D model alignment via discriminative visual elements , 2014, TOGS.

[5]  Larry S. Davis,et al.  Representing Videos Using Mid-level Discriminative Patches , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Slobodan Ilic,et al.  Point Pair Features Based Object Detection and Pose Estimation Revisited , 2015, 2015 International Conference on 3D Vision.

[7]  Soon Myoung Chung,et al.  Orthogonal moment-based descriptors for pose shape query on 3D point cloud patches , 2016, Pattern Recognit..

[8]  Alexei A. Efros,et al.  Mid-level Visual Element Discovery as Discriminative Mode Seeking , 2013, NIPS.

[9]  Siddhartha Chaudhuri,et al.  A probabilistic model for component-based shape synthesis , 2012, ACM Trans. Graph..

[10]  Bernt Schiele,et al.  3D object recognition from range images using local feature histograms , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[11]  Jean Ponce,et al.  Learning Discriminative Part Detectors for Image Classification and Cosegmentation , 2013, 2013 IEEE International Conference on Computer Vision.

[12]  Alexei A. Efros,et al.  Ensemble of exemplar-SVMs for object detection and beyond , 2011, 2011 International Conference on Computer Vision.

[13]  Mohammad Ali Zare Chahooki,et al.  Learning the shape manifold to improve object recognition , 2011, Machine Vision and Applications.

[14]  Iasonas Kokkinos,et al.  Discovering discriminative action parts from mid-level video representations , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Ko Nishino,et al.  3D Geometric Scale Variability in Range Images: Features and Descriptors , 2012, International Journal of Computer Vision.

[16]  Babak Taati,et al.  Local shape descriptor selection for object recognition in range data , 2011, Comput. Vis. Image Underst..

[17]  Jianxiong Xiao,et al.  3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Babak Taati,et al.  Variable Dimensional Local Shape Descriptors for Object Recognition in Range Data , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[19]  David A. Forsyth,et al.  Generalizing motion edits with Gaussian processes , 2009, ACM Trans. Graph..

[20]  David P. Dobkin,et al.  A search engine for 3D models , 2003, TOGS.

[21]  C. V. Jawahar,et al.  Blocks That Shout: Distinctive Parts for Scene Classification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Alexei A. Efros,et al.  Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Models , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Tinne Tuytelaars,et al.  Mining Mid-level Features for Image Classification , 2014, International Journal of Computer Vision.

[24]  Federico Tombari,et al.  SHOT: Unique signatures of histograms for surface and texture description , 2014, Comput. Vis. Image Underst..

[25]  Nassir Navab,et al.  Model globally, match locally: Efficient and robust 3D object recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  Yandong Tang,et al.  Object detection based on scale-invariant partial shape matching , 2015, Machine Vision and Applications.

[27]  Mohammed Bennamoun,et al.  A Comprehensive Performance Evaluation of 3D Local Feature Descriptors , 2015, International Journal of Computer Vision.

[28]  Luca Lucchese,et al.  A Frequency Domain Technique for Range Data Registration , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Tomasz Malisiewicz,et al.  A Gaussian Approximation of Feature Space for Fast Image Similarity , 2012 .

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

[31]  Alexei A. Efros,et al.  Unsupervised Discovery of Mid-Level Discriminative Patches , 2012, ECCV.

[32]  Roberto Cipolla,et al.  A Performance Evaluation of Volumetric 3D Interest Point Detectors , 2013, International Journal of Computer Vision.

[33]  Mohammed Bennamoun,et al.  Rotational Projection Statistics for 3D Local Surface Description and Object Recognition , 2013, International Journal of Computer Vision.

[34]  Jianxiong Xiao,et al.  Sliding Shapes for 3D Object Detection in Depth Images , 2014, ECCV.

[35]  Zhuowen Tu,et al.  Harvesting Mid-level Visual Concepts from Large-Scale Internet Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.