A Unified Energy Minimization Framework for Model Fitting in Depth

Abstract

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.