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.