摘要
利用二维图像来进行场景的深度估计是计算机视觉领域的经典问题之一,也是实现三维重建、场景感知的重要环节。近年来基于深度学习的单目图像深度估计发展迅速,各种新算法层出不穷。介绍了深度学习在这一领域的应用历程与研究进展,采用监督与无监督两类方式分别系统地分析了有代表性的算法与框架,综述了深度学习在单目图像深度估计领域的研究进展与变化趋势,总结了当前研究的缺陷与不足,展望了未来研究的热点。
Obtaining depth estimation of a scene from a two-dimensional image is a classic computer vision problem that plays an important role in three-dimensional reconstruction and scene perception.Monocular image depth estimation based on deep learning has been developing rapidly in recent years with new methods being proposed rapidly.This study discusses the application history and research progress in deep learning-based monocular depth estimation and analyzes several representative deep learning algorithms and network architectures in detail for both supervised and unsupervised learning.Finally,the research progress and trend of the deep learning in the monocular depth estimation field are summarized.Existing problems and future research priorities are discussed as well.
作者
李阳
陈秀万
王媛
刘茂林
Li Yang;Chen Xiuwan;Wang Yuan;Liu Maolin(Institute of Remote Sensing and Geographic Information System,School of Earth and Space Sciences,Peking University,Beijing 100871,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2019年第19期1-17,共17页
Laser & Optoelectronics Progress
基金
国家重点研发项目(2017YFC1500900)
关键词
视觉光学
单目视觉
场景感知
深度学习
深度估计
三维重建
visual optics
monocular vision
scene perception
deep learning
depth estimation
three-dimensional reconstruction
