Unsupervised learning of depth estimation based on attention model and global pose optimization

Abstract

Depth estimation is a fundamental task for 3D scene perception. Unsupervised learning is a prevalent method for depth estimation due to its generalization ability, and it requires no extra ground truth of depth for training. The typical pipeline for unsupervised solution uses the photometric error between target view and reconstructed view from adjacent frames as supervisory signal, in which the depth and pose are both learned and used for reconstruction. In this paper, we proposed a novel framework for unsupervised learning of depth, which consists of a Details Preserved Depth Network with attention model (DPDN), and Global Pose Calculation (GPC) modules. The attention model is adopted in the depth network to preserve the details of the depth map, which enables the network to maintain the shape of objects and enhance edges of the depth map. Moreover, instead of using the learning-based pose-network with two frames, a global pose estimation is optimized using all previous frames by mathematically minimizing the reprojection error. Experimental results show that our method outperforms existing methods in terms of accuracy and visual quality.