Depth Super-Resolution with Deep Edge-Inference Network and Edge-Guided Depth Filling

In this paper, we propose a novel depth super-resolution framework with deep edge-inference network and edge-guided depth filling. We first construct a convolutional neural network (CNN) architecture to learn a binary map of depth edge location from low resolution depth map and corresponding color image. Then, a fast edge-guided depth filling strategy is proposed to interpolate the missing depth constrained by the acquired edges to prevent predicting across the depth boundaries. Experimental results show that our method outperforms the state-of-art methods in both the edges inference and the final results of depth super-resolution, and generalizes well for handling depth data captured in different scenes.