Monocular depth estimation based on unsupervised learning

Abstract

Due to the low cost and easy deployment, the depth estimation of monocular cameras has always attracted attention of researchers. As good performance based on deep learning technology in depth estimation, more and more training models has emerged for depth estimation. Most existing works have required very promising results that belongs to supervised learning methods, but corresponding ground truth depth data for training is inevitable that makes training complicated. To overcome this limitation, an unsupervised learning framework is used for monocular depth estimation from videos, which contains depth map and pose network. In this paper, better results can be achieved by optimizing training models and improving training loss. Besides, training and evaluation data is based on standard dataset KITTI (Karlsruhe Institute of Technology and Toyota Institute of Technology). In the end, the results are shown through comparing with different training models used in this paper.