Abstract
Many successful feature detectors and descriptors exist for 2D intensity images. However, obtaining the same effectiveness in the domain of 3D objects has proven to be a more elusive goal. In fact, the smoothness often found in surfaces and the lack of texture information on the range images produced by conventional 3D scanners hinder both the localization of interesting points and the distinctiveness of their characterization in terms of descriptors. To overcome these limitations several approaches have been suggested, ranging from the simple enlargement of the area over which the descriptors are computed to the reliance on external texture information. In this paper we offer a change in perspective, where a game-theoretic matching technique that exploits global geometric consistency allows to obtain an extremely robust surface registration even when coupled with simple surface features exhibiting very low distinctiveness. In order to assess the performance of the whole approach we compare it with state-of-the-art alignment pipelines. Furthermore, we show that using the novel feature points with well-known alternative non-global matching techniques leads to poorer results.
Chapter PDF
Similar content being viewed by others
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.
References
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. Fourth Alvey Vision Conference, pp. 147–151 (1988)
Marr, D., Hildreth, E.: Theory of edge detection. Royal Soc. of London Proc. Series B 207, 187–217 (1980)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing 22, 761–767 (2004); British Machine Vision Computing 2002 (2002)
Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 20, 91–110 (2003)
Herbert Bay, T.T., Gool, L.V.: Surf: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)
Chua, C.S., Jarvis, R.: Point signatures: A new representation for 3d object recognition. International Journal of Computer Vision 25, 63–85 (1997)
Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21, 433–449 (1999)
Pottmann, H., Wallner, J., Huang, Q.X., Yang, Y.L.: Integral invariants for robust geometry processing. Comput. Aided Geom. Des. 26, 37–60 (2009)
Zaharescu, A., Boyer, E., Varanasi, K., Horaud, R.P.: Surface feature detection and description with applications to mesh matching. In: CVPR (2009)
Albarelli, A., Rota Bulò, S., Torsello, A., Pelillo, M.: Matching as a non-cooperative Game. In: ICCV. IEEE Computer Society, Los Alamitos (2009)
Weibull, J.: Evolutionary Game Theory. MIT Press, Cambridge (1995)
Albarelli, A., Rodolà, E., Torsello, A.: A game-theoretic approach to fine surface registration without initial motion estimation. In: CVPR (2010)
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, 381–395 (1981)
Chum, O., Matas, J.: Matching with prosac - progressive sample consensus. In: CVPR, Washington, DC, USA, pp. 220–226. IEEE Computer Society, Los Alamitos (2005)
Chen, C.S., Hung, Y.P., Cheng, J.B.: Ransac-based darces: A new approach to fast automatic registration of partially overlapping range images. IEEE Trans. Pattern Anal. Mach. Intell. 21, 1229–1234 (1999)
Turk, G., Levoy, M.: Zippered polygon meshes from range images. In: SIGGRAPH 1994: Proc. of the 21st annual conference on Computer graphics and interactive techniques, pp. 311–318. ACM, New York (1994)
Rusinkiewicz, S., Levoy, M.: Efficient variants of the icp algorithm. In: Proceedings of the Third Intl. Conf. on 3D Digital Imaging and Modeling, pp. 145–152 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Albarelli, A., Rodolà, E., Torsello, A. (2010). Loosely Distinctive Features for Robust Surface Alignment. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15555-0_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-15555-0_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15554-3
Online ISBN: 978-3-642-15555-0
eBook Packages: Computer ScienceComputer Science (R0)