Abstract
Dynamic scene modeling is a challenging problem in computer vision. Many techniques have been developed in the past to address such a problem but most of them focus on achieving accurate reconstructions in controlled environments, where the background and the lighting are known and the cameras are fixed and calibrated. Recent approaches have relaxed these requirements by applying these techniques to outdoor scenarios. The problem however becomes even harder when the cameras are allowed to move during the recording since no background color model can be easily inferred.
In this paper we propose a new approach to model dynamic scenes captured in outdoor environments with moving cameras. A probabilistic framework is proposed to deal with such a scenario and to provide a volumetric reconstruction of all the dynamic elements of the scene.
The proposed algorithm was tested on a publicly available dataset filmed outdoors with six moving cameras. A quantitative evaluation of the method was also performed on synthetic data. The obtained results demonstrated the effectiveness of the approach considering the complexity of the problem.
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References
Kim, H., Sarim, M., Takai, T., Guillemaut, J.Y., Hilton, A.: Dynamic 3d scene reconstruction in outdoor environments. In: 3DPVT (2010)
Guan, L., Franco, J.S., Pollefeys, M.: Multi-object shape estimation and tracking from silhouette cues. In: CVPR (2008)
Franco, J.S., Boyer, E.: Fusion of multi-view silhouette cues using a space occupancy grid. In: ICCV, pp. 1747–1753 (2005)
Furukawa, Y., Ponce, J.: Dense 3d motion capture for human faces. In: CVPR, pp. 1674–1681 (2009)
Tung, T., Nobuhara, S., Matsuyama, T.: Complete multi-view reconstruction of dynamic scenes from probabilistic fusion of narrow and wide baseline stereo. In: ICCV (2009)
Seitz, S., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: CVPR (2006)
Seitz, S.M., Dyer, C.R.: Photorealistic scene reconstruction by voxel coloring. In: CVPR, p. 1067 (1997)
Vlasic, D., Peers, P., Baran, I., Debevec, P., Popović, J., Rusinkiewicz, S., Matusik, W.: Dynamic shape capture using multi-view photometric stereo. In: SIGGRAPH Asia (2009)
Ahmed, N., Theobalt, C., Dobrev, P., Seidel, H.P., Thrun, S.: Robust fusion of dynamic shape and normal capture for high-quality reconstruction of time-varying geometry. In: CVPR (2008)
Vedula, S., Baker, S., Seitz, S., Kanade, T.: Shape and motion carving in 6d. In: CVPR (2000)
Matusik, W., Buehler, C., Raskar, R., Gortler, S.J., McMillan, L.: Image-based visual hulls. In: SIGGRAPH, pp. 369–374. ACM Press, New York (2000)
Goldlucke, B., Ihrke, I., Linz, C., Magnor, M.: Weighted minimal hypersurface reconstruction. PAMI, 1194–1208 (2007)
Hilton, A., Starck, J.: Multiple view reconstruction of people. In: 3DPVT (2004)
Sinha, S.N., Pollefeys, M.: Multi-view reconstruction using photo-consistency and exact silhouette constraints: A maximum-flow formulation. In: ICCV, pp. 349–356 (2005)
Hasler, N., Rosenhahn, B., Thormahlen, T., Wand, M., Gall, J., Seidel, H.P.: Markerless motion capture with unsynchronized moving cameras. In: CVPR (2009)
Ballan, L., Brostow, G.J., Puwein, J., Pollefeys, M.: Unstructured video-based rendering: Interactive exploration of casually captured videos. SIGGRAPH (2010)
Baumberg, A., Hogg, D.: An efficient method for contour tracking using active shape models. In: Motion of Non-Rigid and Articulated Objects, pp. 194–199 (1994)
Leibe, B., Cornelis, N., Cornelis, K., Gool, L.V.: Dynamic 3d scene analysis from a moving vehicle. In: CVPR (2007)
Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE 90, 1151–1163 (2002)
Sheikh, Y., Javed, O., Kanade, T.: Background subtraction for freely moving cameras. In: ICCV (2009)
Ivanov, Y., Bobick, A., Liu, J.: Fast lighting independent background subtraction. International Journal of Computer Vision 37, 199–207 (2000)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004) ISBN: 0521540518
Pollefeys, M., Van Gool, L., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J., Koch, R.: Visual modeling with a hand-held camera. IJCV 59, 207–232 (2004)
Zach, C., Pock, T., Bischof, H.: A globally optimal algorithm for robust TV-L 1 range image integration. In: ICCV (2007)
Zhang, Z.: A flexible new technique for camera calibration. PAMI 22 (2000)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)
Haralick, R.M., Lee, C.N., Ottenberg, K., Nölle, M.: Review and analysis of solutions of the three point perspective pose estimation problem. IJCV 13 (1994)
Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. In: Computer Graphics and Applications, vol. 21, pp. 34–41. IEEE, Los Alamitos (2001)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. PAMI 23, 1222–1239 (2001)
Kolmogorov, V., Zabin, R.: What energy functions can be minimized via graph cuts? PAMI 26, 147–159 (2004)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. PAMI 26, 1124–1137 (2004)
Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3d surface construction algorithm. SIGGRAPH 21, 163–169 (1987)
Kim, S., Frahm, J., Pollefeys, M.: Radiometric calibration with illumination change for outdoor scene analysis. In: CVPR, pp. 1–8 (2008)
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Taneja, A., Ballan, L., Pollefeys, M. (2011). Modeling Dynamic Scenes Recorded with Freely Moving Cameras. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_48
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DOI: https://doi.org/10.1007/978-3-642-19318-7_48
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