Abstract
This paper presents an algorithm for finding the dense motion flow of deformable objects from RGB-D images. We introduce a 3D deformable spatial pyramid model by reformulating the previous 2D deformable spatial pyramid model [1] with depth information. Our algorithm recasts the problem of estimating 3D motion of deformable objects as a problem of estimating 2D motions of a set of grid cells where each pixel contains a viewpoint-invariant feature vector. These grid cells are controlled by a pyramid graph model. Our approach significantly reduces the computational cost through a 2D correspondence search and efficiently handles even large deformations with the pyramid graph model. As demonstrated in the experimental results, the proposed algorithm shows robustness in various deformation scenarios.
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References
Kim, J., Liu, C., Sha, F., Grauman, K.: Deformable spatial pyramid matching for fast dense correspondences. In: CVPR (2013)
Li, K., Dai, Q., Xu, W.: Markerless shape and motion capture from multiview video sequences. IEEE Trans. CSVT 21, 320–334 (2011)
Geiger, A., Lauer, M., Wojek, C., Stiller, C., Urtasun, R.: 3d traffic scene understanding from movable platforms. IEEE Trans. PAMI (2014)
Lenz, P., Ziegler, J., Geiger, A., Roser, M.: Sparse scene flow segmentation for moving object detection in urban environments. In: Intelligent Vehicles Symposium (IV) (2011)
Herbst, E., Ren, X., Fox, D.: Rgb-d flow: Dense 3-d motion estimation using color and depth. In: ICRA (2013)
Quiroga, J., Devernay, F., Crowley, J.L.: Local/Global Scene Flow Estimation. In: ICIP (2013)
Hornacek, M., Fitzgibbon, A., Rother, C.: Sphereflow: 6 dof scene flow from rgb-d pairs. In: CVPR (2013)
Shaji, A., Varol, A., Torresani, L., Fua, P.: Simultaneous point matching and 3d deformable surface reconstruction. In: CVPR (2010)
Schulman, J., Lee, A., Ho, J., Abbeel, P.: Tracking deformable objects with point clouds. In: ICRA (2013)
Gossow, D., Weikersdorfer, D., Beetz, M.: Distinctive texture features from perspective-invariant keypoints. In: ICPR (2012)
Diebel, J., Thrun, S.: An application of markov random fields to range sensing. In: Proceedings of Conference on Neural Information Processing Systems (NIPS). MIT Press, Cambridge (2005)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110, 346–359 (2008)
Li, S.Z., Singh, S.: Markov random field modeling in image analysis, vol. 26. Springer (2009)
Murphy, K.P., Weiss, Y., Jordan, M.I.: Loopy belief propagation for approximate inference: An empirical study. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, UAI 1999 (1999)
Weiss, Y.: Correctness of local probability propagation in graphical models with loops. Neural. Comput. 12, 1–41 (2000)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient belief propagation for early vision. International Journal of Computer Vision 70, 41–54 (2006)
Liu, C., Yuen, J., Torralba, A.: Sift flow: Dense correspondence across scenes and its applications. IEEE Trans. PAMI 33, 978–994 (2011)
Liu, L., Low, K.L., Lin, W.Y.: Dense image correspondence under large appearance variations. In: ICIP, pp. 770–774 (2013)
Rother, C., Kolmogorov, V., Blake, A.: “grabcut”: Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23, 309–314 (2004)
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Hur, J., Lim, H., Ahn, S.C. (2014). 3D Deformable Spatial Pyramid for Dense 3D Motion Flow of Deformable Object. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_12
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DOI: https://doi.org/10.1007/978-3-319-14249-4_12
Publisher Name: Springer, Cham
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