Guided deep network for depth map super-resolution: How much can color help?

Abstract

Since the quality of depth maps produced by Time-of-Flight (TOF) cameras is low, color-guided recovery methods have been proposed to increase spatial resolution and suppress unwanted noise. Despite successful applications of deep neural networks in color image super-resolution (SR), their potential for depth map SR is largely unknown. In this paper, we present a deep neural network architecture to learn the end-to-end mapping between low-resolution and high-resolution depth maps. Furthermore, we introduce a novel color-guided deep Fully Convolutional Network (FCN) and propose to jointly learn two nonlinear mapping functions (color-to-depth and LR-to-HR) in the presence of noise. Experimental results on several benchmark data sets show that our method outperforms several existing state-of-the-art depth SR algorithms. Moreover, this work attempts to partially shed some light onto the fundamental question in color-guided depth recovery - how much can color help in depth SR?