Abstract
This paper addresses the problem of space-time stereo with active illumination and presents a formulation of this problem in the variational framework. Variational problems of this scale are computationally expensive to solve directly. We overcome this challenge by showing that speed-improving techniques, as the full-multi-grid and the multi-level-adaptation techniques, can be applied. We evaluate the performance of our method on 3 ground-truth datasets. The experimental results for synthetic and real datasets show that the combination of active illumination and variational space-time stereo improves the quality of the reconstruction on average by up to 3.1 times compared to a reconstruction from a single passive stereo image pair without active illumination.
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References
Ross, W.P.: A practical stereo vision system. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 148–153 (1993)
Kolmogorov, V., Zabih, R.: Computing visual correspondence with occlusions via graph cuts. In: International Conference on Computer Vision, pp. 508–515 (2001)
Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: Proc. Computer Vision and Pattern Recognition, vol. I, pp. 195–202 (2003), http://vision.middlebury.edu/stereo/
Mémin, E., Pérez, P.: Dense estimation & object-based segmentation of the optical flow with robust techniques. IEEE Trans. on Image Processing 7, 703–719 (1998)
Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)
Bruhn, A., Weickert, J., Kohlberger, T., Schnörr, C.: A multigrid platform for real-time motion computation with discontinuity-preserving variational methods. Int. J. Comput. Vision 70, 257–277 (2006)
Fedorenko, R.: Relaxation method for solving elliptic differential equations. Journal of Computational Mathematics and Mathematical Phisics 1, 922–927 (1961)
Valgaerts, L., Bruhn, A., Zimmer, H., Weickert, J., Stoll, C., Theobalt, C.: Joint estimation of motion, structure and geometry from stereo sequences. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 568–581. Springer, Heidelberg (2010)
Kosov, S., Thormählen, T., Seidel, H.-P.: Accurate real-time disparity estimation with variational methods. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Wang, J.-X., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009. LNCS, vol. 5875, pp. 796–807. Springer, Heidelberg (2009)
Brandt, A.: Multi-level adaptive technique (MLAT) for fast numerical solution to boundary value problems. Lecture Notes in Physics 18, 82–89 (1973)
Zhang, L., Curless, B., Seitz, S.: Spacetime stereo: Shape recovery for dynamic scenes. In: IEEE Conf. on Comp. Vision and Pattern Recognition, pp. 367–374 (2003)
Kang, S.B., Webb, J., Zitnick, C., Kanade, T.: A multibaseline stereo system with active illumination and real-time image acquisition. In: Proceedings of the Fifth International Conference on Computer Vision (ICCV 1995), pp. 88–93 (1995)
Frueh, C., Zakhor, A.: Capturing 2\(\frac{1}{2}\)d depth and texture of time-varying scenes using structured infrared light. In: Proc. 3DIM, pp. 318–325 (2005)
Ristivojevic, M., Konrad, J.: Space-time image sequence analysis: Object tunnels and occlusion volumes. IEEE Transactions on Image Processing 15, 364–376 (2006)
Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)
Charbonnier, P., Aubert, G., Blanc-Ferraud, M., Barlaud, M.: Two deterministic half-quadratic regularization algorithms for computed imaging. In: International Conference on Image Processing, vol. 2, pp. 168–172 (1994)
Cohen, I.: Nonlinear variational method for optical flow computation. In: Eighth Scandinavian Conference on Image Analysis, vol. 1, pp. 523–530 (1993)
Cheng, A., Cheng, D.T.: Heritage and early history of the boundary element method. Engineering Analysis with Boundary Elements 29, 268–302 (2005)
Kosov, S.: 3D map reconstruction with variational methods. Master thesis, Saarland University (2008)
Blake, A., McCowen, D., Lo, H.R., Lindsey, P.J.: Trinocular active range-sensing. IEEE Trans. Pattern Anal. Mach. Intell. 15, 477–483 (1993)
Koninckx, T., Gool, L.V.: Real-time range acquisition by adaptive structured light. IEEE Transac. on Pattern Analysis & Machine Intelligence 28, 432–445 (2006)
Horn, E., Kiryati, N.: Toward optimal structured light patterns. In: Proc. 3DIM, p. 28. IEEE Computer Society, Washington, DC, USA (1997)
Koschan, A., Rodehorst, V., Spiller, K.: Color stereo vision using hierarchical block matching and active color illumination. In: ICPR 1996, vol. I, pp. 835–839 (1996)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense stereo correspondence algorithms. International Journal of Computer Vision 47, 7–42 (2001)
Sizintsev, M., Wildes, R.P.: Spatiotemporal stereo via spatiotemporal quadric element (stequel) matching. In: CVPR, pp. 493–500 (2009)
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Kosov, S., Thormählen, T., Seidel, HP. (2011). Using Active Illumination for Accurate Variational Space-Time Stereo. In: Heyden, A., Kahl, F. (eds) Image Analysis. SCIA 2011. Lecture Notes in Computer Science, vol 6688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21227-7_70
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DOI: https://doi.org/10.1007/978-3-642-21227-7_70
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