G. Medioni, M. Lee, and C. Tang, A computational framework for segmentation and grouping, 2000.

M. Pilu and R. Fisher, Part segmentation from 2D edge images by the MDL criterion, Image and Vision Computing, vol.15, issue.8, pp.563-573, 1997.
DOI : 10.1016/S0262-8856(97)00013-9

M. Wani and B. Batchelor, Edge-region-based segmentation of range images Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.16, issue.3, pp.314-319, 1994.

D. Zhao and X. Zhang, Range-data-based object surface segmentation via edges and critical points, IEEE Transactions on Image Processing, vol.6, issue.6, pp.826-830, 1997.
DOI : 10.1109/83.585233

Y. Alshawabkeh, N. Haala, and D. Fritsch, Range image segmentation using the numerical description of the mean curvature values, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. ISPRS Congress, p.533, 2008.

H. Iddamsetty, Segmentation of range images for modeling of large outdoor scenes, 2003.

K. Pulli and M. Pietikäinen, Range image segmentation based on decomposition of surface normals, Proceedings of the Scandinavian conference on image analysis, pp.893-893, 1993.

A. Leonardis, A. Gupta, and R. Bajcsy, Segmentation of range images as the search for geometric parametric models, International Journal of Computer Vision, vol.5, issue.4, pp.253-277, 1995.
DOI : 10.1007/BF01679685

G. Yu, M. Grossberg, G. Wolberg, and I. Stamos, Think globally, cluster locally: A unified framework for range segmentation, International Symposium on 3D Data Processing, Visualization and Transmission, 2008.

L. Faivishevsky and J. Goldberger, A nonparametric information theoretic clustering algorithm, ICML. Citeseer, pp.351-358, 2010.

T. Melzer, Non-parametric segmentation of ALS point clouds using mean shift, Journal of Applied Geodesy, vol.1, issue.3, p.159, 2007.
DOI : 10.1515/jag.2007.018

J. Roca-pardiñas, H. Lorenzo, P. Arias, and J. Armesto, From laser point clouds to surfaces: Statistical nonparametric methods for three-dimensional reconstruction, Computer-Aided Design, vol.40, issue.5, pp.646-652, 2008.
DOI : 10.1016/j.cad.2008.03.002

P. Felzenszwalb and D. Huttenlocher, Efficient Graph-Based Image Segmentation, International Journal of Computer Vision, vol.59, issue.2, pp.167-181, 2004.
DOI : 10.1023/B:VISI.0000022288.19776.77

M. Johnson-roberson, J. Bohg, M. Björkman, and D. Kragic, Attention-based active 3D point cloud segmentation, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010.
DOI : 10.1109/IROS.2010.5649872

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

H. Woo, E. Kang, S. Wang, and K. Lee, A new segmentation method for point cloud data, International Journal of Machine Tools and Manufacture, vol.42, issue.2, pp.167-178, 2002.
DOI : 10.1016/S0890-6955(01)00120-1

B. Douillard, J. Underwood, N. Kuntz, V. Vlaskine, A. Quadros et al., On the segmentation of 3D LIDAR point clouds, 2011 IEEE International Conference on Robotics and Automation, pp.2798-2805, 2011.
DOI : 10.1109/ICRA.2011.5979818

A. Golovinskiy and T. Funkhouser, Min-cut based segmentation of point clouds, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp.39-46, 2009.
DOI : 10.1109/ICCVW.2009.5457721

E. Castillo and H. Zhao, Point cloud segmentation via constrained nonlinear least squares surface normal estimates, 2009.
DOI : 10.1007/978-3-642-34141-0_13

D. Holz, S. Holzer, R. Rusu, and S. Behnke, Real-Time Plane Segmentation Using RGB-D Cameras, Proc. of the 15th RoboCup International Symposium, 2011.
DOI : 10.1007/978-3-642-32060-6_26

C. Teutsch, E. Trostmann, and D. Berndt, A parallel point cloud clustering algorithm for subset segmentation and outlier detection, Videometrics, Range Imaging, and Applications XI, p.8, 2011.
DOI : 10.1117/12.888654

K. Klasing, D. Wollherr, and M. Buss, Realtime segmentation of range data using continuous nearest neighbors, 2009 IEEE International Conference on Robotics and Automation, pp.2431-2436, 2009.
DOI : 10.1109/ROBOT.2009.5152498

K. Klasing, D. Althoff, D. Wollherr, and M. Buss, Comparison of surface normal estimation methods for range sensing applications, 2009 IEEE International Conference on Robotics and Automation, pp.3206-3211, 2009.
DOI : 10.1109/ROBOT.2009.5152493

Y. Dumortier, I. Herlin, and A. Ducrot, 4-D Tensor Voting motion segmentation for obstacle detection in autonomous guided vehicle, 2008 IEEE Intelligent Vehicles Symposium, pp.379-384, 2008.
DOI : 10.1109/IVS.2008.4621203

URL : https://hal.archives-ouvertes.fr/inria-00292702

J. Jia and C. Tang, Inference of segmented color and texture description by tensor voting Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.26, issue.6, pp.771-786, 2004.

H. Schuster, Segmentation of lidar data using the tensor voting framework, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.35, pp.1073-1078, 2004.

B. King, Range data analysis by free-space modeling and tensor voting, 2009.

C. Min and G. Medioni, Tensor voting accelerated by graphics processing units (gpu), " in Pattern Recognition, 18th International Conference on, pp.1103-1106, 2006.

R. B. Rusu and S. Cousins, 3D is here: Point Cloud Library (PCL), 2011 IEEE International Conference on Robotics and Automation, 2011.
DOI : 10.1109/ICRA.2011.5980567

P. Mordohai and G. Medioni, Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning, Synthesis Lectures on Image, Video, and Multimedia Processing, pp.1-136, 2006.
DOI : 10.2200/S00049ED1V01Y200609IVM008

J. Glosup, Statistical Modelling With Quantile Functions, Technometrics, vol.43, issue.4, pp.488-489, 2001.
DOI : 10.1198/tech.2001.s45

N. Advanced and C. Webinar, Memory Optimizations Available: {http://developer.download.nvidia.com/CUDA/ training/NVIDIA GPU Computing Webinars CUDA Memory Optimization

S. Robertson, CUDA atomics: a practical analysis Available: {http://strobe.cc

A. Edelman, T. Arias, and S. Smith, The Geometry of Algorithms with Orthogonality Constraints, SIAM Journal on Matrix Analysis and Applications, vol.20, issue.2, 1998.
DOI : 10.1137/S0895479895290954

E. Zürich, ASL Datasets Repository