4-points congruent sets for robust pairwise surface registration

SIGGRAPH'08: International Conference on Computer Graphics and Interactive Techniques, ACM SIGGRAPH 2008 Papers 2008

Volumn , Issue , 2008, Pages

  Aiger, Dror ^a   Mitra, Niloy J ^b   Cohen Or, Daniel ^c  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

^b INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

^c TEL AVIV UNIVERSITY   (Israel)

Author keywords

Affine invariant ratio; Computational geometry; Largest common pointset(LCP) measure; Pairwise surface registration; Partial shape matching; Scan alignment

Indexed keywords

ALIGNMENT; AUTOMOBILE SEATS; COMPUTER GRAPHICS; FOURIER TRANSFORMS; INTERACTIVE COMPUTER GRAPHICS; THREE DIMENSIONAL;

AFFINE INVARIANT RATIO; LARGEST COMMON POINTSET(LCP) MEASURE; PAIRWISE SURFACE REGISTRATION; PARTIAL SHAPE MATCHING; SCAN ALIGNMENT;

COMPUTATIONAL GEOMETRY;

EID: 57649094651     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. AGARWAL, P. K., AND SHARIR, M. 2002. The number of congruent simplices in a point set. In Discrete Comput. Geometry, vol.28, 123-150.
2. ARYA, S., MOUNT, D. M., NETANYAHU, N. S., SILVERMAN, R., AND WU, A. Y. 1998. An optimal algorithm for approximate nearest neighbor searching fixed dimensions. ACM 45, 6, 891-923.
3. BALLARD, D. H. 1987. Generalizing the hough transform to detect arbitrary shapes. Readings in computer vision: issues, problems, principles, and paradigms, 714-725.
4. BESL, P. J., AND MCKAY, N. D. 1992. A method for registration of 3-d shapes. IEEE Trans, on Pattern Analysis and Machine Intelligence 14, 2, 239-256.
5. BROWN, B. J., AND RUSINKIEWICZ, S. 2007. Global non-rigid alignment of 3-d scans. ACM Trans. Graph. 26, 3, #21.
6. CALLIERI, M., FASANO, A., IMPOCO, G., CIGNONI, P., SCOPIGNO, R., PARRINI, G., AND BIAGINI, G. 2004. Ro-boscan: An automatic system for accurate and unattended 3d scanning. In 3DPVT, 805-812.
7. CHEN, Y., AND MEDIONI, G. G. 1992. Object modelling by registration of multiple range images. In Image and Vis. Compt., vol. 10, 145-155.
8. CHEN, C.-S., HUNG, Y.-P., AND CHENG, J.-B. 1999. RANSAC-based DARCES: A new approach to fast automatic registration of partially overlapping range images. IEEE Trans, on Pattern Analysis and Machine Intelligence 21, II, 1229-1234.
9. FISCHLER, M. A., AND BOLLES, R. C. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 6, 381-395.
10. FROME, A., HUBER, D., KOLLURI, R., BULOW, T., AND MALIK, J. 2004. Recognizing objects in range data using regional point descriptors. In Proceedings of the European Conference on Computer Vision (ECCV).
11. GAL, R., AND COHEN-OR, D. 2006. Salient geometric features for partial shape matching and similarity. ACM Trans. Gr. 25, 1, 130-150.
12. GELFAND, N., AND GUIBAS, L. J. 2004. Shape segmentation using local slippage analysis. In Proc. Symp. Geometry Processing, 214-223.
13. GELFAND, N., MITRA, N. J., GUIBAS, L. J., AND POTTMANN, H. 2005. Robust global registration. In Proc. Symp. Geometry Processing, 197-206.
14. GERMAIN, R. S., CALIFANO, A., AND COLVILLE, S. 1997. Fingerprint matching using transformation parameter clustering. IEEE Comput. Sci. Eng. 4, 4, 42-49.
15. GOODRICH, M. T., MITCHELL, J. S. B., AND ORLETSKY, M. W. 1994. Practical methods for approximate geometric pattern matching under rigid motions. In Symp. on Computational Geometry, 103-112.
16. HORN, B. K. 1987. Closed-form solution of absolute orientation using unit quaternions. In Journal of the Optical Society of America, vol. 4, 629-642.
17. HUTTENLOCHER, D. P., AND ULLMAN, S. 1990. Recognizing solid objects by alignment with an image. Int. J. Comput. Vision 5, 2, 195-212.
18. HUTTENLOCHER, D. P. 1991. Fast afflne point matching: An output-sensitive method. Tech. rep., Cornell, CS department.
19. INDYK, P., MOTWANI, R., AND VENKATASUBRAMANIAN, S. 1999. Geometric matching under noise: combinatorial bounds and algorithms. In Symposium on Discrete algorithms, 457-465.
20. IRANI, S., AND RAGHAVAN, P. 1996. Combinatorial and experimental results for randomized point matching algorithms. In Symp. on Computational Geometry, 68-77.
21. JOHNSON, A. 1997. Spin-Images: A Representation for 3-D Surface Matching. PhD thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.
22. LI, X., AND GUSKOV, I. 2005. Multi-scale features for approximate alignment of point-based surfaces. In Proc. Symp. Geometry Processing, 217-226.
23. MITRA, N. J., GELFAND, N., POTTMANN, H., AND GUIBAS, L. 2004. Registration of point cloud data from a geometric optimization perspective. In Proc. Symp. Geometry Processing, 23-31.
24. MITRA, N. J., GUIBAS, L., GLESEN, J., AND PAULY, M. 2006. Probabilistic fingerprints for shapes. In Proc. Symp. Geometry Processing, 121-130.
25. MORI, M.-G., BELONGIE, M.-S., AND MALIK, S. M.-J. 2005. Efficient shape matching using shape contexts. IEEE Trans, on Pattern Analysis and Machine Intelligence 27, 11, 1832-1837.
26. PAULY, M., MITRA, N. J., GIESEN, J., GROSS, M., AND GUIBAS, L. 2005. Example-based 3d scan completion. In Proc. Symp. Geometry Processing, 23-32.
27. POTTMANN, H., WALLNER, J., YANG, Y.-L., LAI, Y.-K., AND HU, S.-M. 2007. Principal curvatures from the integral invariant viewpoint. Comput. Aided Geom. Des. 24, 8-9, 428-442.
28. RUSINKIEWICZ, S., AND LEVOY, M. 2001. Efficient variants of the ICP algorithm. In Proc. of 3DIM, 145-152.
29. WOLFSON, H. J., AND RIGOUTSOS, I. 1997. Geometric hashing: An overview. IEEE Comput. Sci. Eng. 4, 4, 10-21.