Monocular depth estimation based on deep learning: An overview

作     者：ZHAO ChaoQiang SUN QiYu ZHANG ChongZhen TANG Yang QIAN Feng ZHAO ChaoQiang;SUN QiYu;ZHANG ChongZhen;TANG Yang;QIAN Feng

作者机构：Key Laboratory of Advanced Control and Optimization for Chemical ProcessMinistry of EducationEast China University of Science and TechnologyShanghai 200237China 

出 版 物：《Science China(Technological Sciences)》 (中国科学（技术科学英文版）)

年 卷 期：2020年第63卷第9期

页      面：1612-1627页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Key Research and Development Program of China (Grant No. 2018YFC0809302) the National Natural Science Foundation of China (Grant Nos. 61988101,61751305 and 61673176) the Fundamental Research Funds for the Central Universities (Grant No.JKH012016011) the Programme of Introducing Talents of Discipline to Universities (the “111” Project)(Grant No. B17017) 

主　　题：autonomous systems monocular depth estimation deep learning unsupervised learning 

摘      要：Depth information is important for autonomous systems to perceive environments and estimate their own state. Traditional depth estimation methods, like structure from motion and stereo vision matching, are built on feature correspondences of multiple viewpoints. Meanwhile, the predicted depth maps are sparse. Inferring depth information from a single image(monocular depth estimation) is an ill-posed problem. With the rapid development of deep neural networks, monocular depth estimation based on deep learning has been widely studied recently and achieved promising performance in accuracy. Meanwhile, dense depth maps are estimated from single images by deep neural networks in an end-to-end manner. In order to improve the accuracy of depth estimation, different kinds of network frameworks, loss functions and training strategies are proposed subsequently. Therefore, we survey the current monocular depth estimation methods based on deep learning in this review. Initially, we conclude several widely used datasets and evaluation indicators in deep learning-based depth estimation. Furthermore, we review some representative existing methods according to different training manners: supervised, unsupervised and semi-supervised. Finally, we discuss the challenges and provide some ideas for future researches in monocular depth estimation.