ABSTRACT
In this paper, we address the problem of 3D object categorization for point cloud data. With the availability of inexpensive scanning devices and powerful computational resources, there is a rapid growth of point cloud data. This necessitates efficient classification techniques which form the basis for analysis and processing of 3D point cloud data. In order to address the classification problem, we propose a 3D object categorization framework for both rigid and non-rigid objects. Initially, the proposed approach extracts the feature descriptors using improved wave kernel signature by approximating Laplace-Beltrami operator on point cloud data for non-rigid objects. For rigid objects, our approach uses the geometric features, namely, metric tensor and Christoffel symbols by modifying the geodesic distance computation. These feature descriptors are then represented using bag-of-features and improved Fisher vector encoding techniques. Finally, the support vector machine classifies the 3D objects into predefined set of classes. We also provide an exhaustive performance evaluation of the proposed 3D object categorization framework on state-of-the-art datasets, namely, SHREC'10, SHREC'11, SHREC'12, SHREC'15 and Princeton Shape Benchmark. The evaluation results reveal that the proposed approach outperforms the existing object categorization methods for both rigid and non-rigid 3D objects.
- M. Aubry, U. Schlickewei, and D. Cremers. 2011. The wave kernel signature: A quantum mechanical approach to shape analysis. In ICCV. 1626--1633.Google Scholar
- Mikhail Belkin, Jian Sun, and Yusu Wang. 2009. Constructing Laplace Operator from Point Clouds in Rd (SODA). 1031--1040.Google Scholar
- Yizhak Ben-Shabat, Michael Lindenbaum, and Anath Fischer. 2017. 3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks. CoRR (2017).Google Scholar
- Alexander M. Bronstein, Michael M. Bronstein, Alfred M. Bruckstein, and Ron Kimmel. 2009. Partial Similarity of Objects, or How to Compare a Centaur to a Horse. Int. J. Comput. Vision 84 (2009), 163--183.Google ScholarDigital Library
- M. M. Bronstein and I. Kokkinos. 2010. Scale-invariant heat kernel signatures for non-rigid shape recognition. In CVPR. 1704--1711.Google Scholar
- S. Bu, Z. Liu, J. Han, J. Wu, and R. Ji. 2014. Learning High-Level Feature by Deep Belief Networks for 3-D Model Retrieval and Recognition. IEEE Transactions on Multimedia 16 (2014), 2154--2167.Google ScholarCross Ref
- Angel X. Chang, Thomas A. Funkhouser, Leonidas J. Guibas, Pat Hanrahan, Qi-Xing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. 2015. ShapeNet: An Information-Rich 3D Model Repository. CoRR (2015).Google Scholar
- Chih Chung Chang and Chih Jen Lin. 2011. LIBSVM: A Library for Support Vector Machines. ACM Trans. Intell. Syst. Technol. (2011), 27:1--27:27.Google ScholarDigital Library
- Ken Chatfield, Victor S. Lempitsky, Andrea Vedaldi, and Andrew Zisserman. 2011. The devil is in the details: an evaluation of recent feature encoding methods. In BMVC, Vol. 2. 76.1--76.12.Google Scholar
- Gabriela Csurka and Florent Perronnin. 2011. Fisher Vectors: Beyond Bag-of-Visual-Words Image Representations. In Computer Vision, Imaging and Computer Graphics. Theory and Applications. 28--42.Google Scholar
- T. Deselaers, L. Pimenidis, and H. Ney. 2008. Bag-of-visual-words models for adult image classification and filtering. In ICPR. 1--4.Google Scholar
- Andrea Frome, Daniel Huber, Ravi Kolluri, Thomas Bülow, and Jitendra Malik. 2004. Recognizing Objects in Range Data Using Regional Point Descriptors. In ECCV. 224--237.Google Scholar
- Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, and Uma Mudenagudi. 2014. 3D Object Super Resolution Using Metric Tensor and Christoffel Symbols (ICVGIP). 87:1--87:8.Google Scholar
- Syed Altaf Ganihar, Shreyas Joshi, Shankar Setty, and Uma Mudenagudi. 2014. Metric tensor and Christoffel symbols based 3D object categorization. In Computer Vision-ACCV Workshops. Springer, 138--151.Google ScholarDigital Library
- Y. Guo, M. Bennamoun, F. Sohel, M. Lu, and J. Wan. 2014. 3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey. TPAMI 36 (2014), 2270--2287.Google ScholarCross Ref
- Y. Guo, F. A. Sohel, M. Bennamoun, J. Wan, and M. Lu. 2013. RoPS: A local feature descriptor for 3D rigid objects based on rotational projection statistics. In ICCSPA. 1--6.Google Scholar
- Jungong Han, Ling Shao, Dong Xu, and Jamie Shotton. 2013. Enhanced Computer Vision with Microsoft Kinect Sensor: A Review. IEEE Trans. Cybernetics 43 (2013).Google Scholar
- M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf. 1998. Support vector machines. IEEE Intelligent Systems and their Applications 13 (1998), 18--28.Google Scholar
- Chih-Wei Hsu and Chih-Jen Lin. 2002. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13 (2002), 415--425.Google ScholarDigital Library
- Andrew Johnson and Martial Hebert. 1999. Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes. 21 (1999), 433--449.Google Scholar
- T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu. 2002. An efficient k-means clustering algorithm: analysis and implementation. TPAMI 24 (2002), 881--892.Google ScholarDigital Library
- Oliver Kreylos, Gerald W.Bawden, and Louise H. Kellogg. 2008. Immersive Visualization and Analysis of LiDAR Data (ISVC).Google Scholar
- B. Li, A. Godil, M. Aono, X. Bai, T. Furuya, L. Li, R. López-Sastre, H. Johan, R. Ohbuchi, C. Redondo-Cabrera, A. Tatsuma, T. Yanagimachi, and S. Zhang. SHREC'12 Track: Generic 3D Shape Retrieval (EG 3DOR). 119--126.Google Scholar
- Xinju Li and Igor Guskov. 2005. Multi-scale Features for Approximate Alignment of Point-based Surfaces. In Proceedings of the Third Eurographics Symposium on Geometry Processing (SGP).Google ScholarDigital Library
- Z. Lian, A. Godil, B. Bustos, M. Daoudi, J. Hermans, S. Kawamura, Y. Kurita, G. Lavoué, H. V. Nguyen, R. Ohbuchi, Y. Ohkita, Y. Ohishi, F. Porikli, M. Reuter, I. Sipiran, D. Smeets, P. Suetens, H. Tabia, and D. Vandermeulen. 2011. SHREC'11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes (3DOR). 79--88.Google Scholar
- Z. Lian, A. Godil, T. Fabry, T. Furuya, J. Hermans, R. Ohbuchi, C. Shu, D. Smeets, P. Suetens, D. Vandermeulen, and S. Wuhrer. 2010. SHREC'10 Track: Non-rigid 3D Shape Retrieval. In Eurographics Workshop on 3D Object Retrieval. The Eurographics Association.Google Scholar
- Z. Lian, J. Zhang, S. Choi, H. ElNaghy, J. El-Sana, T. Furuya, A. Giachetti, R. A. Guler, L. Lai, C. Li, H. Li, F. A. Limberger, R. Martin, R. U. Nakanishi, A. P. Neto, L. G. Nonato, R. Ohbuchi, K. Pevzner, D. Pickup, P. Rosin, A. Sharf, L. Sun, X. Sun, S. Tari, G. Unal, and R. C. Wilson. 2015. SHREC'15 Track: Non-rigid 3D Shape Retrieval. In Eurographics Workshop on 3D Object Retrieval. The Eurographics Association.Google Scholar
- Frederico A. Limberger and Richard C. Wilson. 2015. Feature Encoding of Spectral Signatures for 3D Non-Rigid Shape Retrieval. In Proceedings of the BMVC. 56.1--56.13.Google Scholar
- R. J. López-Sastre, A. García-Fuertes, C. Redondo-Cabrera, F.J. Acevedo-Rodríguez, and S. Maldonado-Bascón. 2013. Evaluating 3D Spatial Pyramids for Classifying 3D Shapes. Comput. Graph. 37 (2013), 473--483.Google ScholarDigital Library
- Lorenzo Luciano and A. Ben Hamza. 2018. Deep Learning with Geodesic Moments for 3D Shape Classification. Pattern Recogn. Lett. 105 (2018), 182--190.Google ScholarDigital Library
- D. Maturana and S. Scherer. 2015. VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition. In IROS.Google Scholar
- G. McLachlan and D. Peel.2000. Finite mixture models. Wiley Series in Probability and Statistics.Google ScholarCross Ref
- F. Perronnin and C. Dance. 2007. Fisher Kernels on Visual Vocabularies for Image Categorization. In CVPR. 1--8.Google Scholar
- Florent Perronnin, Jorge Sánchez, and Thomas Mensink. 2010. Improving the Fisher Kernel for Large-scale Image Classification (ECCV). Springer-Verlag, 143--156.Google Scholar
- Douglas A. Reynolds. 2009. Gaussian Mixture Models. In Encyclopedia of Biometrics.Google Scholar
- E. Rublee, V. Rabaud, K. Konolige, and G. Bradski. 2011. ORB: An efficient alternative to SIFT or SURF. In ICCV. 2564--2571.Google Scholar
- Radu Bogdan Rusu, Nico Blodow, and Michael Beetz. 2009. Fast Point Feature Histograms (FPFH) for 3D Registration (ICRA). 1848--1853.Google Scholar
- Michalis A. Savelonas, Ioannis Pratikakis, and Konstantinos Sfikas. 2016. Fisher Encoding of Differential Fast Point Feature Histograms for Partial 3D Object Retrieval. Pattern Recogn. 55 (2016), 114--124.Google ScholarDigital Library
- Paul Scovanner, Saad Ali, and Mubarak Shah. 2007. A 3-dimensional Sift Descriptor and Its Application to Action Recognition. ACM, 357--360.Google Scholar
- P. Shilane, P. Min, M. Kazhdan, and T. Funkhouser. 2004. The Princeton Shape Benchmark. In Proceedings Shape Modeling Applications. 167--178.Google Scholar
- Bastian Steder, Radu Bogdan Rusu, Kurt Konolige, and Wolfram Burgard. 2010. NARF: 3D Range Image Features for Object Recognition. In Workshop on Defining and Solving Realistic Perception Problems in Personal Robotics at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS).Google Scholar
- Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller. 2015. Multi-view Convolutional Neural Networks for 3D Shape Recognition (ICCV). 945--953.Google Scholar
- Jian Sun, Maks Ovsjanikov, and Leonidas Guibas. 2009. A Concise and Provably Informative Multi-scale Signature Based on Heat Diffusion (SGP). Eurographics Association, 1383--1392.Google Scholar
- Roberto Toldo, Umberto Castellani, and Andrea Fusiello. 2009. A Bag of Words Approach for 3D Object Categorization. In MIRAGE.Google Scholar
- A. Vedaldi and B. Fulkerson. 2008. VLFeat: An Open and Portable Library of Computer Vision Algorithms. http://www.vlfeat.org/. (2008).Google Scholar
- J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for image classification. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 3360--3367.Google Scholar
- Xi Zhou, Kai Yu, Tong Zhang, and Thomas S. Huang. 2010. Image Classification Using Super-vector Coding of Local Image Descriptors (ECCV). 141--154.Google ScholarDigital Library
Index Terms
- Evaluation of Point Cloud Categorization for Rigid and Non-Rigid 3D Objects
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