Abstract
This paper proposes an adaptive color-guided auto-regressive (AR) model for high quality depth recovery from low quality measure- ments captured by depth cameras. We formulate the depth recovery task into a minimization of AR prediction errors sub ject to measurement con- sistency. The AR predictor for each pixel is constructed according to both the local correlation in the initial depth map and the nonlocal similarity in the accompanied high quality color image. Experimental results show that our method outperforms existing state-of-the-art schemes, and is versatile for both mainstream depth sensors: ToF camera and Kinect.