Unsupervised Learning of Monocular Depth Estimation:A Survey
As the key point of 3D reconstruction,automatic driving and visual SLAM,depth estimation has always been a hot re-search direction in the field of computer vision,among which,monocular depth estimation technology based on unsupervised learning has been widely concerned by academia and industry because of its advantages of convenient deployment,low computa-tional cost and so on.Firstly,this paper reviews the basic knowledge and research actuality of depth estimation and briefly intro-duces the advantages and disadvantages of depth estimation based on parametric learning,non-parametric learning,supervised learning,semi-supervised learning and unsupervised learning.Secondly,the research progress of monocular depth estimation based on unsupervised learning is summarized comprehensively.The monocular depth estimation based on unsupervised learning is sum-marized according to five categories:combination of interpretable mask,combination of visual odometer,combination of prior auxi-liary information,combination of generated adversarial network and real-time lightweight network,and the typical framework model is introduced and compared.Then,the application of monocular depth estimation based on unsupervised learning in medi-cine,autonomous driving,agriculture,military and other fields is introduced.Finally,the common data sets used for unsupervised depth estimation are briefly introduced,and the future research direction of monocular depth estimation based on unsupervised learning is proposed,while the prospects of various research directions in this rapidly growing field are also prospected.
万方数据
维普
