Abstract
The need for robots to search the 3D data they have saved is becoming more apparent. We present an approach for finding structures in 3D models such as those built by robots of their environment. The method extracts geometric primitives from point cloud data. An attributed graph over these primitives forms our representation of the surface structures. Recurring substructures are found with frequent graph mining techniques. We investigate if a model invariant to changes in size and reflection using only the geometric information of and between primitives can be discriminative enough for practical use. Experiments confirm that it can be used to support queries of 3D models.
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
References
Schnabel, R., Wahl, R., Klein, R.: Efficient ransac for point-cloud shape detection. Comput. Graph. Forum 26, 214–226 (2007)
Yan, X., Han, J.: gspan: Graph-based substructure pattern mining. In: Proceedings of the 2002 IEEE International Conference on Data Mining, ICDM 2002, pp. 721–729. IEEE Computer Society, Washington, DC, USA (2002)
Nowozin, S., et al.: Weighted substructure mining for image analysis. In: Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)
Jiang, M.C., Coenen, F.: Graph-based image classification by weighting scheme. In: Allen, T., Ellis, R., Petridis, M. (eds.) Applications and Innovations in Intelligent Systems XVI, pp. 63–76. Springer, London (2009)
Cheng, H., Yan, X., Han, J., Hsu, C.-W.: Discriminative frequent pattern analysis for effective classification. In: IEEE 23rd International Conference on Data Engineering, ICDE 2007, pp. 716–725. IEEE (2007)
Aydemir, A., Jensfelt, P., Folkesson, J.: What can we learn from 38,000 rooms? reasoning about unexplored space in indoor environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4675–4682 (2012)
Anand, A., et al.: Contextually guided semantic labeling and search for three-dimensional point clouds. Int. J. Robot. Res. 32(1), 19–34 (2013)
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012)
Nüchter, A., Hertzberg, J.: Towards semantic maps for mobile robots. Robot. Auton. Syst. 56(11), 915–926 (2008)
Farid, R., Sammut, C.: A relational approach to plane-based object categorization. In Robotics Science and Systems Workshop on RGB-D Cameras (2012)
Shin, J., Triebel, R., Siegwart, R.: Unsupervised discovery of repetitive objects. In: IEEE International Conference on Robotics and Automation, ICRA 2010, Anchorage, Alaska, USA, pp. 5041–5046. IEEE, 3–7 May 2010
Mattausch, O., Panozzo, D., Mura, C., Sorkine-Hornung, O., Pajarola, R.: Object detection and classification from large-scale cluttered indoor scans. Comput. Graph. Forum 33, 11–21 (2014)
Biederman, I.: Recognition-by-components: a theory of human image understanding. Psychol. Rev. 94(2), 115 (1987)
Verma, V., Kumar, R., Hsu, S.: 3d building detection and modeling from aerial lidar data. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2213–2220. IEEE (2006)
Schnabel, R., Wessel, R., Wahl, R., Klein, R.: Shape recognition in 3d point-clouds. In: Proceedings of Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, vol. 2. Citeseer (2008)
Jahn, K., Kramer, S.: Optimizing gspan for molecular datasets. In: Proceedings of the Third International Workshop on Mining Graphs, Trees and Sequences (MGTS-2005), pp. 77–89 (2005)
Kudo, T., et al.: An application of boosting to graph classification. Adv. Neural Inf. Process. Syst. 17, 729–736 (2004)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Fox, D., Burgard, W., Dellaert, F., Thrun, S.: Monte carlo localization: efficient position estimation for mobile robots. AAAI/IAAI 1999, 343–349 (1999)
Acknowledgments
The work presented in this papers has been funded by the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement No 600623 (“STRANDS”), VR project “XPLOIT”, and the Swedish Foundation for Strategic Research (SSF) through its Centre for Autonomous Systems.
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
Bore, N., Jensfelt, P., Folkesson, J. (2015). Querying 3D Data by Adjacency Graphs. 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_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-20904-3_23
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)