High Quality Depth Estimation from Monocular Images Based on Depth Prediction and Enhancement Sub-Networks

ABSTRACT

This paper addresses the problem of depth estimation from a single RGB image. Previous methods mainly focus on the problems of depth prediction accuracy and output depth resolution, but seldom of them can tackle these two problems well. Here, we present a novel depth estimation framework based on deep convolutional neural network (CNN) to learn the mapping between monocular images and depth maps. The proposed architecture can be divided into two components, i.e., depth prediction and depth enhancement sub-networks. We first design a depth prediction network based on the ResNet architecture to infer the scene depth from color image. Then, a depth enhancement network is concatenated to the end of the depth prediction network to obtain a high resolution depth map. Experimental results show that the proposed method outperforms other methods on benchmark RGB-D datasets and achieves state-of-the-art performance. 

Index Terms— Depth Estimation, CNN, Depth Prediction, Depth Enhancement, Monocular

SOURCE CODE 

Opening soon. The source code is only for the non-commercial use.

PUBLICATIONS

[1]  Xiangyue Duan, Xinchen Ye*, Yang Li, Haojie Li, High Quality Depth Estimation from Monocular Images Based on Depth Prediction and Enhancement Sub-Networks. IEEE International Conference onMultimedia and Expo, ICME 2018, San Diego, USA. (CCF-B)