We present a unified energy minimization framework for model fitting problems in depth cameras. 3D level-set embedding functions are used to implicitly represent object models 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 application. 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.
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