摘要： This paper presents a novel Time-of-Flight (ToF) depth recovery algorithm minimizing a new quadratic energy function utilizing depth and infrared data. The proposed energy function consists of a filtering term and a reconstruction term to remove noise and fill holes simultaneously in a depth image. In the filtering term, a new multilateral weight is introduced by fully using available spatial, depth, and infrared information. In the reconstruction term, a Poisson equation for reconstructing a depth image from its gradients is used whereas the depth gradients inside hole regions are interpolated with the proposed infrared-guided moving least squares. The recovered depth data can be obtained by solving a sparse linear system derived from minimizing the proposed energy function. Experimental results demonstrate that the proposed algorithm provides good depth recovery performance without introducing texture copy or blur artifacts compared to the existing methods.