D. H. Ballard, Generalizing the Hough transform to detect arbitrary shapes, Pattern Recognition, vol.13, issue.2, pp.111-122, 1981.
DOI : 10.1016/0031-3203(81)90009-1

H. Bay, A. Ess, T. Tuytelaars, and L. Van-gool, Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding, vol.110, issue.3, pp.346-359, 2008.
DOI : 10.1016/j.cviu.2007.09.014

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.738

P. J. Besl and N. D. Mckay, A method for registration of 3-D shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.2, pp.239-256, 1992.
DOI : 10.1109/34.121791

M. Calonder, V. Lepetit, M. Ozuysal, T. Trzcinski, C. Strecha et al., BRIEF: Computing a Local Binary Descriptor Very Fast, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.7, pp.1281-1298, 2012.
DOI : 10.1109/TPAMI.2011.222

O. Chum and J. Matas, Matching with PROSAC ??? Progressive Sample Consensus, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.220-226, 2005.
DOI : 10.1109/CVPR.2005.221

URL : https://dspace.cvut.cz/bitstream/10467/9496/1/2005-Matching-with-PROSAC-progressive-sample-consensus.pdf

S. Filipe and L. A. , A Comparative Evaluation of 3D Keypoint Detectors in a RGB-D Object Dataset, VISAPP 2014 -Proc. of the 9th International Conference on Computer Vision Theory and Applications, pp.476-483, 2014.

M. A. Fischler and R. C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, vol.24, issue.6, pp.381-395, 1981.
DOI : 10.1145/358669.358692

S. Gold, A. Rangarajan, C. Lu, S. Pappu, and E. Mjolsness, New algorithms for 2D and 3D point matching, Pattern Recognition, vol.31, issue.8, pp.311019-1031, 1998.
DOI : 10.1016/S0031-3203(98)80010-1

J. Herling and W. Broll, Random model variation for universal feature tracking, Proceedings of the 18th ACM symposium on Virtual reality software and technology, VRST '12, pp.169-176, 2012.
DOI : 10.1145/2407336.2407368

D. Q. Huynh, Metrics for 3D Rotations: Comparison and Analysis, Journal of Mathematical Imaging and Vision, vol.19, issue.3, pp.155-164, 2009.
DOI : 10.1007/s10851-009-0161-2

Y. Lamdan, J. T. Schwartz, and H. J. Wolfson, Affine invariant model-based object recognition, IEEE Transactions on Robotics and Automation, vol.6, issue.5, pp.578-589, 1990.
DOI : 10.1109/70.62047

D. G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.91-110, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.4931

P. Mcilroy, S. Izadi, and A. Fitzgibbon, Kinectrack: Agile 6-DoF tracking using a projected dot pattern, 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp.23-29, 2012.
DOI : 10.1109/ISMAR.2012.6402533

D. M. Mount, N. S. Netanyahu, and J. L. Moigne, Efficient algorithms for robust feature matching, Pattern Recognition, vol.32, issue.1, pp.17-38, 1999.
DOI : 10.1016/S0031-3203(98)00086-7

T. Nakai, K. Kise, and M. Iwamura, Use of Affine Invariants in Locally Likely Arrangement Hashing for Camera-Based Document Image Retrieval, Proc. of the 7th International Conference on Document Analysis Systems, DAS'06, pp.541-552, 2006.
DOI : 10.1007/11669487_48

C. F. Olson, Efficient Pose Clustering Using a Randomized Algorithm, International Journal of Computer Vision, vol.23, issue.2, pp.131-147, 1997.
DOI : 10.1023/A:1007906812782

T. Pham, M. Delalandre, S. Barrat, and J. , Accurate junction detection and characterization in line-drawing images, Pattern Recognition, vol.47, issue.1, pp.282-295, 2014.
DOI : 10.1016/j.patcog.2013.06.027

R. Raguram, O. Chum, M. Pollefeys, J. Matas, and J. Frahm, USAC: A Universal Framework for Random Sample Consensus, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.2022-2038, 2013.
DOI : 10.1109/TPAMI.2012.257

E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, ORB: An efficient alternative to SIFT or SURF, 2011 International Conference on Computer Vision, pp.2564-2571, 2011.
DOI : 10.1109/ICCV.2011.6126544

M. F. Schilling, The surprising predictability of long runs, Math. Mag, vol.85, issue.2, pp.141-149, 2012.

J. Shi and C. Tomasi, Good Features to Track, Proc. of the 1994 IEEE Conference on Computer Vision and Pattern Recognition, CVPR '94, pp.593-600, 1994.

F. Tombari, S. Salti, and L. D. Stefano, Performance Evaluation of 3D Keypoint Detectors, International Journal of Computer Vision, vol.89, issue.2???3, pp.198-220, 2013.
DOI : 10.1007/s11263-012-0545-4

Y. Tsin and T. Kanade, A Correlation-Based Approach to Robust Point Set Registration, Proc. of the 8th European Conference on Computer Vision, pp.558-569, 2004.
DOI : 10.1007/978-3-540-24672-5_44

H. Uchiyama and E. Marchand, Toward augmenting everything: Detecting and tracking geometrical features on planar objects, 2011 10th IEEE International Symposium on Mixed and Augmented Reality, pp.17-25, 2011.
DOI : 10.1109/ISMAR.2011.6092366

URL : https://hal.archives-ouvertes.fr/hal-00639704

H. Uchiyama and H. Saito, Random Dot Markers, Proc. of the 2011 IEEE Virtual Reality Conference, pp.271-272, 2011.
DOI : 10.1109/vr.2011.5759433

URL : https://hal.archives-ouvertes.fr/hal-00639703

H. Uchiyama, H. Saito, M. Servì, and G. Moreau, Camera tracking by online learning of keypoint arrangements using LLAH in augmented reality applications, Virtual Reality, vol.27, issue.2-3, pp.109-117, 2011.
DOI : 10.1007/s10055-010-0173-7

S. Umeyama, Least-squares estimation of transformation parameters between two point patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.4, pp.376-380, 1991.
DOI : 10.1109/34.88573

H. J. Wolfson and I. Rigoutsos, Geometric hashing: an overview, IEEE Computational Science and Engineering, vol.4, issue.4, pp.10-21, 1997.
DOI : 10.1109/99.641604

L. Yang, J. Normand, and G. Moreau, Robust random dot markers, Proceedings of the 20th ACM Symposium on Virtual Reality Software and Technology, VRST '14
DOI : 10.1145/2671015.2671022

Y. Zhong, Intrinsic shape signatures: A shape descriptor for 3D object recognition, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp.689-696, 2009.
DOI : 10.1109/ICCVW.2009.5457637