Abstract
This paper presents an approach for object pose estimation using a combination of multiple feature descriptors. We propose to use a combination of three feature descriptors, capturing both surface and edge information. Those descriptors individually perform well for different object classes. We use scenes from an established RGB-D dataset and our own recorded scenes to justify the claim that by combining multiple features, we in general achieve better performance. We present quantitative results for descriptor matching and object detection for both datasets.
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
Notes
- 1.
Indeed, any edge point is allowed to belong to multiple categories at once, e.g. canny and occluding, meaning an edge point detected by appearance, but also found to occlude parts of the scene from the depth image.
References
Aldoma, A., Marton, Z.C., Tombari, F., Wohlkinger, W., Potthast, C., Zeisl, B., Rusu, R., Gedikli, S., Vincze, M.: Tutorial: point cloud library: three-dimensional object recognition and 6 dof pose estimation. IEEE Robot. Autom. Mag. 19(3), 80–91 (2012)
Alexandre, L.A.: 3D descriptors for object and category recognition: a comparative evaluation. In: Workshop on Color-Depth Camera Fusion in Robotics at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012)
Besl, P., McKay, N.D.: A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)
Choi, C., Trevor, A.J., Christensen, H.I.: RGB-D edge detection and edge-based registration. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1568–1575. IEEE (2013)
Hinterstoisser, S., Cagniart, C., Ilic, S., Sturm, P., Navab, N., Fua, P., Lepetit, V.: Gradient response maps for real-time detection of textureless objects. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 876–888 (2012)
Jeong, W., Lee, S., Kim, Y.: Statistical feature selection model for robust 3D object recognition. In: 2011 15th International Conference on Advanced Robotics (ICAR), pp. 402–408, June 2011
Jørgensen, T.B., Buch, A.G., Kraft, D.: Geometric edge description and classification in point cloud data with application to 3D object recognition. In: International Conference on Computer Vision Theory and Applications (VISAPP) (2015)
Kiforenko, L., Buch, A.G., Bodenhagen, L., Kruger, N.: Object detection using categorised 3D edges. In: SPIE, vol. 9445 (2015)
Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view RGB-D object dataset. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1817–1824 (2011)
Lv, T., Liu, G., bin Huang, S., Wang, Z.X.: Selective feature combination and automatic shape categorization of 3D models. In: Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009, FSKD 2009, vol. 5, pp. 447–451, August 2009
Ohbuchi, R., Furuya, T.: Distance metric learning and feature combination for shape-based 3D model retrieval. In: Proceedings of the ACM Workshop on 3D Object Retrieval, 3DOR 2010, pp. 63–68. ACM, New York (2010)
Rusu, R.B., Cousins, S.: 3D is here: point cloud library (pcl). In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1–4 (2011)
Salti, S., Tombari, F., Di Stefano, L.: Shot: unique signatures of histograms for surface and texture description. Comput. Vis. Image Underst. 125, 251–264 (2014)
Sotelo, M., Parra, I., Fernandez, D., Naranjo, E.: Pedestrian detection using svm and multi-feature combination. In: Intelligent Transportation Systems Conference, 2006, ITSC 2006, pp. 103–108. IEEE, September 2006
Tombari, F., Salti, S., Di Stefano, L.: A combined texture-shape descriptor for enhanced 3D feature matching. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 809–812, September 2011
Tombari, F., Franchi, A., Di Stefano, L.: Bold features to detect texture-less objects. In: The IEEE International Conference on Computer Vision (ICCV), December 2013
Acknowledgments
The research leading to these results has received funding from the European Communitys Seventh Framework Programme FP7/2007-2013 (Programme and Theme: ICT-2011.2.1, Cognitive Systems and Robotics) under grant agreement no. 600578, ACAT and by Danish Agency for Science, Technology and Innovation, project CARMEN.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Kiforenko, L., Buch, A.G., Krüger, N. (2015). Object Detection Using a Combination of Multiple 3D Feature Descriptors. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-20904-3_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20903-6
Online ISBN: 978-3-319-20904-3
eBook Packages: Computer ScienceComputer Science (R0)