Abstract
One of the key steps in 3D object recognition is the matching between an input cloud and a cloud in a database of known objects. This is usually done using a distance function between sets of descriptors. In this paper we propose to study how several distance functions (some already available and other new proposals) behave experimentally using a large freely available household object database containing 1421 point clouds from 48 objects and 10 categories. We present experiments illustrating the accuracy of the distances both for object and category recognition and find that simple distances give competitive results both in terms of accuracy and speed.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
We acknowledge the financial support of project PEst-OE/EEI/LA0008/2013.
Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Grauman, K., Darrell, T.: The pyramid match kernel: Efficient learning with sets of features. Journal of Machine Learning Research 8, 725–760 (2007)
Rusu, R., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China (2011)
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), Vilamoura, Portugal (2012)
Rusu, R., Blodow, N., Marton, Z., Beetz, M.: Aligning point cloud views using persistent feature histograms. In: International Conference on Intelligent Robots and Systems (IROS), Nice, France (2008)
Tombari, F., Salti, S., Di Stefano, L.: A combined texture-shape descriptor for enhanced 3D feature matching. In: IEEE International Conference on Image Processing (2011)
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)
Lai, K., Bo, L., Ren, X., Fox, D.: A Large-Scale hierarchical Multi-View RGB-D object dataset. In: Proc. of the IEEE International Conference on Robotics & Automation, ICRA (2011)
Lee, J.J.: Libpmk: A pyramid match toolkit. Technical Report MIT-CSAIL-TR-2008-17, MIT Computer Science and Artificial Intelligence Laboratory (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alexandre, L.A. (2013). Set Distance Functions for 3D Object Recognition. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41822-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-41822-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41821-1
Online ISBN: 978-3-642-41822-8
eBook Packages: Computer ScienceComputer Science (R0)