In this paper we present a unified energy minimization framework for model fitting and pose recovery problems in depth cameras. 3D level-set embedding functions are used to represent object models implicitly and a novel 3D chamfer matching based energy function is minimized by adjusting the generic projection matrix, which could be parameterized differently according to specific applications. Our proposed energy function takes the advantage of the gradient of 3D level-set embedding function and can be efficiently solved by gradients-based optimization methods. We show various real-world applications, including real-time 3D tracking in depth, simultaneous calibration and tracking, and 3D point cloud modeling. We perform experiments on both real data and synthetic data to show the superior performance of our method for all the applications above. © 2012 Springer-Verlag.
CITATION STYLE
Ren, C. Y., & Reid, I. (2012). A unified energy minimization framework for model fitting in depth. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7584 LNCS, pp. 72–82). Springer Verlag. https://doi.org/10.1007/978-3-642-33868-7_8
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