Abstract
Classification of urban range data into different object classes offers several challenges due to certain properties of the data such as density variation, inconsistencies due to holes and the large data size which requires heavy computation and large memory. A method to classify urban scenes based on a super-voxel segmentation of sparse 3D data obtained from Lidar sensors is presented. The 3D point cloud is first segmented into voxels which are then characterized by several attributes transforming them into super-voxels. These are joined together by using a link-chain method rather than the usual region growing algorithm to create objects. These objects are then classified using geometrical models and local descriptors. In order to evaluate the results, a new metrics is presented which combines both segmentation and classification results simultaneously. The proposed method is evaluated on standard datasets using three different evaluation metrics.
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
D. Anguelov, B. Taskar, V. Chatalbashev, D. Koller, D. Gupta, G. Heitz, A. Ng, Discriminative learning of Markov random fields for segmentation of 3D scan data, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2 (IEEE Computer Society, Los Alamitos, CA, USA, 2005), pp. 169–176
B. Douillard, A. Brooks, F. Ramos, A 3D laser and vision based classifier, in 5th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Melbourne, Australia (2009), p. 6
B Douillard, J. Underwood, N. Kuntz, V. Vlaskine, A. Quadros, P. Morton, A. Frenkel, On the segmentation of 3D LIDAR point clouds, in IEEE International Conferene on Robotics and Automation (ICRA), Shanghai, China (2011), p. 8
P. Felzenszwalb, D. Huttenlocher, Efficient graph-based image segmentation. Int. J. Comput. Vision 59, 167–181 (2004)
S. Friedman, I. Stamos, Real time detection of repeated Structures in point clouds of urban scenes, in textitThe First Joint 3DIM/3DPVT (3DIMPVT) Conference, Hangzhou, China (2011), p. 8
B.C.M. Fung, K. Wang, M. Ester, Hierarchical document clustering using frequent itemsets, chap. I, in Proceedings of the Third SIAM International Conference on Data Mining. SIAM, San Francisco, CA (2003), pp. 59–70
A. Golovinskiy, T Funkhouser, Min-cut based segmentation of Point clouds, in IEEE Workshop on Search in 3D and Video (S3DV) at ICCV, (2009), pp. 39–46
A. Halma, F. ter Haar, E. Bovenkamp, P. Eendebak, A. van Eekeren, Single spin image-ICP matching for efficient 3D object recognition, in Proceedings of the ACM workshop on 3D object retrieval, 3DOR ’10, NY, USA, New York (2010), pp. 21–26
A. Johnson, Spin-images: a representation for 3-D surface matching. Ph.D. thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 1997
M. Kazhdan, T. Funkhouser, S. Rusinkiewicz, Rotation invariant spherical harmonic representation of 3D shape descriptors, in Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing, SGP ’03 (Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 2003), pp. 156–164
K. Klasing, D. Althoff, D. Wollherr, M. Buss, Comparison of surface normal estimation methods for range sensing applications, in IEEE International Conference on Robotics and Automation, Kobe, Japan (2009), pp. 3206–3211
J. Knopp, M. Prasad, L.V. Gool, Orientation invariant 3D object classification using hough transform based methods, in Proceedings of the ACM workshop on 3D object retrieval, 3DOR ’10 (ACM, New York, NY, USA, 2010), pp. 15–20
J. Lam, K. Kusevic, P. Mrstik, R. Harrap, M. Greenspan, Urban scene extraction from mobile ground based LiDAR data, in International Symposium on 3D Data Processing Visualization and Transmission, Paris, France (2010), p. 8
E. Lim, D. Suter, Conditional random field for 3D point clouds with adaptive data reduction, in International Conference on Cyberworlds, Hannover (2007), pp. 404–408
E.H. Lim, D. Suter, Multi-scale conditional random fields for over-segmented irregular 3D point clouds classification, in Computer Vision and Pattern Recognition Workshop (IEEE Computer Society, Anchorage, AK, USA, 2008), pp. 1–7
Y. Liu, H. Zha, H. Qin, Shape topics—a compact representation and new algorithms for 3D partial shape retrieval, in IEEE Conference on Computer Vision and Pattern Recognition, vol. 2 (IEEE Computer Society, New York, NY, USA (2006), pp. 2025–2032
F. Moosmann, O. Pink, C. Stiller, Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion, in Proceedings of the IEEE Intelligent Vehicles Symposium (IV) (Nashville, Tennessee, USA, 2009), pp. 215–220
D. Munoz, N. Vandapel, M. Hebert, Onboard contextual classification of 3-D point clouds with learned high-order Markov random fields, in IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan (2009), pp. 2009–2016
R. Osada, T. Funkhouser, B. Chazelle, D. Dobkin, Shape distributions. ACM Trans. Graph. 21, 807–832 (2002)
F. Pauling, M. Bosse, R. Zlot, Automatic segmentation of 3D laser point clouds by ellipsoidal region growing, in Australasian Conference on Robotics and Automation, Sydney, Australia (2009), p. 10
S. Pu, G. Vosselman, Building facade reconstruction by fusing terrestrial laser points and images. Sensors 9(6), 4525–4542 (2009)
A. Rosenberg, J. Hirschberg, V-measure: a conditional entropy-based external cluster evaluation measure, in Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (2007), pp. 410–420
R. Rusu, G. Bradski, R. Thibaux, J. Hsu, Fast 3D recognition and pose using the viewpoint feature histogram, in IEEE/RSJ International Conference on Intelligence Robots and Systems (IROS), Taipei, Taiwan (2010), pp. 2155–2162
J. Schoenberg, A. Nathan, M. Campbell, Segmentation of dense range information in complex urban scenes, in IEEE/RSJ International Conference on Intelligence Robots and Systems (IROS), Taipei, Taiwan (2010), pp. 2033–2038
J. Strom, A. Richardson, E. Olson, Graph-based segmentation for colored 3D laser point clouds, in Proceedings of the IEEE/RSJ International Conference on Intelligence Robots and Systems (IROS) (2010), pp. 2131–2136
R. Triebel, J. Shin, R. Siegwart, Segmentation and unsupervised part-based discovery of repetitive objects, in Proceedings of Robotics: Science and Systems, Zaragoza, Spain (2010), p. 8
M. Vieira, K. Shimada, Surface mesh segmentation and smooth surface extraction through region growing. Comput. Aided Geom. Des. 22(8), 771–792 (2005)
G. Vosselman, P. Kessels, B. Gorte, The utilisation of airborne laser scanning for mapping. Int. J. Appl. Earth Obs. Geoinf. 6(3–4), 177–186 (2005)
Acknowledgments
This work is supported by the Agence Nationale de la Recherche (ANR - the French national research agency) (ANR CONTINT iSpace & Time – ANR-10-CONT-23) and by “le Conseil Général de l’Allier”. The authors would like to thank Pierre Bonnet and all the other members of Institut Pascal who contributed to this project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Aijazi, A.K., Checchin, P., Trassoudaine, L. (2014). Super-Voxel Based Segmentation and Classification of 3D Urban Landscapes with Evaluation and Comparison. In: Yoshida, K., Tadokoro, S. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 92. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40686-7_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-40686-7_34
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40685-0
Online ISBN: 978-3-642-40686-7
eBook Packages: EngineeringEngineering (R0)