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.
CITATION STYLE
Hur, J., Lim, H., & Ahn, S. C. (2014). 3D deformable spatial pyramid for dense 3D motion flow of deformable object. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8887, pp. 118–127). Springer Verlag. https://doi.org/10.1007/978-3-319-14249-4_12
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