Unsupervised Multi-View Constrained Convolutional Network for Accurate Depth Estimation

Abstract

Accurate depth estimation from images is a fundamental problem in computer vision. In this paper, we propose an unsupervised learning based method to predict high-quality depth map from multiple images. A novel multi-view constrained DenseDepthNet is designed for this task. Our DenseDepthNet can effectively leverage both the low-level and high-level features of input images and generate appealing results, especially with sharp details. We employ the public datasets KITTI and Cityscapes for training in an end-to-end unsupervised fashion. A novel depth consistency loss based on multi-view geometry constraint is also applied to the corresponding points across pairwise images, which helps to improve the quality of predicted depth maps significantly. We conduct comprehensive evaluations on our DenseDepthNet and our depth consistency loss function. Experiments validate that our method outperforms the state-of-the-art unsupervised methods and produce comparable results with supervised methods.