基于深度学习的单目视觉深度估计研究综述

A Review of Monocular Depth Estimation Based on Deep Learning

随着智能化程度的不断提升,对深度估计的要求也越来越高,激光雷达和立体视觉被广泛应用,取得了不错的效果。但受限于传感单元重量、体积以及成本等因素,逐渐涌现出一种新的研究思路,即仅利用成本低廉的单目视觉实现对深度信息的精确测量。首先分析了现有深度信息提取方式的特点及缺陷,给出了单目深度估计的研究意义;其次对近年来基于深度学习进行单目深度估计的方法进行了分类及特点分析,包括监督学习、无监督学习、半监督学习、基于条件随机场(CRF)的方法、联合语义分割、引入其他信息辅助深度估计的方法;最后对此领域的未来发展趋势做出了简要分析。

With the continuous improvement of intelligence,the requirement of depth estimation is becoming higher and higher.Lidar and stereo vision are widely used,and good results have been achieved.However,limited by the weight,volume and cost of the sensor unit,a new research idea has emerged gradually,that is,precise measurement of depth information using only low-cost monocular vision.Firstly,the characteristics and shortcomings of the existing methods of extracting depth information are analyzed,and the research significance of monocular depth estimation is given.Secondly,the methods of monocular depth estimation based on deep learning in recent years are classified and analyzed,including supervised learning,unsupervised learning,semi-supervised learning,conditional random field(CRF)based method,joint semantics segmentation,and information-aided depth estimation method.Finally,the future development trend of this field is briefly analyzed.