Depth recovery using an adaptive color-guided auto-regressive model

Abstract

This paper proposes an adaptive color-guided auto-regressive (AR) model for high quality depth recovery from low quality measurements captured by depth cameras. We formulate the depth recovery task into a minimization of AR prediction errors subject to measurement consistency. 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. © 2012 Springer-Verlag.