Abstract
Perception of object categories is a key functionality towards more versatile autonomous robots. Object categorization enables robots to understand their environments even if certain instances of objects have never been seen before. In this paper we present the novel descriptor Surface-Approximation Polynomials (SAP) that directly computes a global description on point cloud surfaces of objects based on polynomial approximations of surface cuts. This descriptor is directly applicable to point clouds captured with time-of-flight or other depth sensors without any data preprocessing or normal computation. Hence, it is generated very fast. Together with a preceding pose normalization, SAP is invariant to scale and partially invariant to rotations. We demonstrate experiments in which SAP categorizes 78 % of test objects correctly while needing only 57 ms for the computation. This way SAP is superior to GFPFH, GRSD and VFH according to both criteria.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rusu, R.B., Holzbach, A., Beetz, M., Bradski, G.: Detecting and segmenting objects for mobile manipulation. In: ICCV, S3DV Workshop (2009)
Marton, Z.C., Pangercic, D., Blodow, N., Beetz, M.: Combined 2D-3D Categorization and Classification for Multimodal Perception Systems. The International Journal of Robotics Research 30(11), 1378–1402 (2011)
Rusu, R.B., Bradski, G., Thibaux, R., Hsu, J.: Fast 3D recognition and pose using the viewpoint feature histogram. In: Proceedings of the International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan (2010)
Galleguillos, C., Belongie, S.: Context based object categorization: A critical survey. Computer Vision and Image Understanding (CVIU) 114, 712–722 (2010)
Zhang, H., Berg, A.C., Maire, M., Malik, J.: SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2126–2136 (2006)
Deng, J., Berg, A.C., Li, K., Fei-Fei, L.: What Does Classifying More Than 10,000 Image Categories Tell Us? In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 71–84. Springer, Heidelberg (2010)
Johnson, A., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Trans. PAMI 21(1), 433–449 (1999)
Koenderink, J.J., van Doorn, A.J.: Surface shape and curvature scales. Image Vision Computing 10, 557–565 (1992)
Knopp, J., Prasad, M., Willems, G., Timofte, R., Van Gool, L.: Hough Transform and 3D SURF for Robust Three Dimensional Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 589–602. Springer, Heidelberg (2010)
Tombari, F., Salti, S., Di Stefano, L.: Unique Signatures of Histograms for Local Surface Description. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 356–369. Springer, Heidelberg (2010)
Knopp, J., Prasad, M., Van Gool, L.: Orientation invariant 3D object classification using hough transform based methods. In: Proc. of the ACM Workshop on 3D Object Retrieval, pp. 15–20 (2010)
Toldo, R., Castellani, U., Fusiello, A.: A bag of words approach for 3D object categorization. In: Proc. of Int. Conference on Computer Vision, pp. 116–127 (2009)
Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Shape distributions. ACM Tr. on Graphics 21(4), 807–832 (2002)
Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3D shape descriptors. In: Symposium on Geometry Processing (June 2003)
Pu, J., Yi, L., Guyu, X., Hongbin, Z., Weibin, L., Uehara, Y.: 3D model retrieval based on 2D slice similarity measurements. In: Proceedings of the 3D Data Processing, Visualization, and Transmission, pp. 95–101 (2004)
Endres, F., Plagemann, C., Stachniss, C., Burgard, W.: Unsupervised discovery of object classes from range data using latent dirichlet allocation. In: Proc. of Robotics: Science and Systems (2009)
Bo, L., Ren, X., Fox, D.: Depth Kernel Descriptors for Object Recognition. In: IROS (September 2011)
Wahl, E., Hillenbrand, U., Hirzinger, G.: Surflet-pair-relation histograms: A statistical 3D-shape representation for rapid classification. In: 3-D Digital Imaging and Modeling, pp. 474–481 (2003)
Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: Proc. of Int. Conference on Robotics and Automation (ICRA), Shanghai, China (2011)
Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)
Marton, Z.C., Rusu, R.B., Jain, D., Klank, U., Beetz, M.: Probabilistic Categorization of Kitchen Objects in Table Settings with a Composite Sensor. In: Proc. of the Int. Conf. on Intelligent Robots and Systems, St. Louis, MO, USA (2009)
Collet Romea, A., Srinivasa, S., Hebert, M.: Structure discovery in multi-modal data: a region-based approach. In: Proceedings of ICRA (2011)
Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)
Burges, C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Annals of Statistics 28, 2000 (1998)
Mozos, O.M., Burgard, W.: Supervised learning of topological maps using semantic information extracted from range data. In: IROS, pp. 2772–2777 (2006)
Browatzki, B., Fischer, J., Graf, B., Bülthoff, H., Wallraven, C.: Going into depth: Evaluating 2D and 3D cues for object classification on a new, large-scale object dataset. In: Proc. of Int. Conf. Computer Vision Workshop on CD4CV, pp. 1–7 (2011)
Bradski, G., Kaehler, A.: Learning opencv: Computer vision with the opencv library (2008)
Chang, C., Lin, C.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bormann, R., Fischer, J., Arbeiter, G., Verl, A. (2012). Efficient Object Categorization with the Surface-Approximation Polynomials Descriptor. In: Stachniss, C., Schill, K., Uttal, D. (eds) Spatial Cognition VIII. Spatial Cognition 2012. Lecture Notes in Computer Science(), vol 7463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32732-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-32732-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32731-5
Online ISBN: 978-3-642-32732-2
eBook Packages: Computer ScienceComputer Science (R0)