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Received：05 July 2021，
Revised：2021-10-12，
Accepted：19 October 2021，
Published：16 February 2022
移动端阅览
场景的深度估计问题是计算机视觉领域中的经典问题之一，也是3维重建和图像合成等应用中的一个重要环节。基于深度学习的单目深度估计技术高速发展，各种网络结构相继提出。本文对基于深度学习的单目深度估计技术最新进展进行了综述，回顾了基于监督学习和基于无监督学习方法的发展历程。重点关注单目深度估计的优化思路及其在深度学习网络结构中的表现，将监督学习方法分为多尺度特征融合的方法、结合条件随机场（conditional random field，CRF）的方法、基于序数关系的方法、结合多元图像信息的方法和其他方法等5类；将无监督学习方法分为基于立体视觉的方法、基于运动恢复结构（structure from motion，SfM）的方法、结合对抗性网络的方法、基于序数关系的方法和结合不确定性的方法等5类。此外，还介绍了单目深度估计任务中常用的数据集和评价指标，并对目前基于深度学习的单目深度估计技术在精确度、泛化性、应用场景和无监督网络中不确定性研究等方面的现状和面临的挑战进行了讨论，为相关领域的研究人员提供一个比较全面的参考。
Scene depth estimation is one of the key issues in the field of computer vision and an important aspect in the applications such as 3D reconstruction and image synthesis. Monocular depth estimation techniques based on deep learning have developed fast recently. Differentiated network structures have been proposed gradually. The current development of monocular depth estimation techniques based on deep learning and a categorical review of supervised and unsupervised learning-based methods have been illustrated in terms of the characteristics of the network structures. The supervised learning methods have been segmented as following: 1) Multi-scale feature fusion strategies: Different scales images contain different kinds of information via fusing multi-scale features extracted from the images. The demonstrated results of depth estimation can be effectively improved. 2)Conditional random fields (CRFs): CRFs
as one of probabilistic graphical models
have good performance in the field of semantic segmentation. Since depth information has similar data distribution attributes as semantic information
the use of consistent CRFs can be effective for predicting continuous depth values. CRFs can be operated as the loss function in the final part of the network as well as a feature fusion module in the medium layer of the network due to its effectiveness for fuse features. 3)Ordinal relations: One category is the relative depth estimation method which uses ordinal relation straight forward to estimate the relative position of two pixels in the image. The other category defines the depth estimation as an ordinal regression issue
which needs to discretize the continuous depth values into discrete depth labels and perform multi-class classification for the global depth. 4) Multiple image information: It is beneficial to combining various image information in depth estimation to improve the accuracy of depth estimation results whereas the image information of different dimensions (time
space
semantics
etc.) can be implicitly related to the depth of the image scene. Four types of information are often adopted: semantic information
neighborhood information
temporal information and object boundary information. 5)Miscellaneous strategies: Some other supervised learning methods still cannot be easily classified into the above-mentioned methods. 6) Various optimization strategies: Acquiring efficiency optimization
using synthetic data obtained via image style transfer for domain adaptation
and the hardware-oriented optimization for underwater scene depth estimation. The unsupervised learning methods of scene depth estimation are classified as below: 1) Stereo vision: Stereo vision aims to deduce the depth information of each pixel in the image from two or more images. Conventional binocular stereo vision algorithm is based on the stereo disparity
and can reconstruct the three-dimensional geometric information of surrounding scenery from the images captured by two camera sensors in terms of the principle of trigonometry. Researchers transform the depth estimation into an image reconstruction
and unsupervised depth estimation method is realized based on binocular (or multi-ocular) images and predicted disparity maps. 2) Structure from motion (SfM): SfM is a technique that automatically recovers camera parameters and the 3D structure of a scene from multiple images or video sequences. The unsupervised method based on SfM has its similarity to the unsupervised method based on stereo vision. It also transforms the depth estimation into the image reconstruction
but there are many differences in details. First
the SfM-based image reconstruction unsupervised processing method is generally using successive frames
that is
the image of the current frame is used to reconstruct the image of the previous or the next frame. Therefore
this kind of method uses image sequence generally-video as the training data. Second
the unsupervised method based on SfM needs to introduce a module for camera pose estimation in the training process. 3) Adversarial strategies: Generative adversarial networks (GANs) facilitate many imaging tasks with their powerful performance
where a discriminator can judge the results generated by the generator to force the generator to produce the same results as the labels. Adding discriminators to unsupervised learning networks can be effective in improving depth estimation results by optimizing image reconstruction results. 4) Ordinal relationship: Similar to the ordinal regression approach that utilizes ordinal relationships in the supervised learning methods
discrete disparity estimation is also desirable in unsupervised networks. In view of the fact that discrete depth values achieve more robust and sharper depth estimates than conventional regression predictions
discrete operations are equally effective in unsupervised networks. 5) Uncertainty: Since unsupervised learning does not use ground truth depth values
the depth results predicted is in doubt. From this viewpoint
it has been proposed to use the uncertainty of the prediction results of unsupervised methods as a benchmark for judging whether the prediction results are credible
and the results can be optimized in monocular depth estimation tasks. Meanwhile
this review refers to the NYU dataset
Karlsruhe Institute Technology and Toyota Technological Institute at Chicago (KITTI) dataset
Make3D dataset and Cityscapes dataset
which are mainly used in monocular deep estimation tasks
as well as six commonly-used evaluation metrics. Based on these datasets and evaluation metrics
a comparison among the reviewed methods is illustrated. Finally
the review discusses the current status of deep learning-based monocular depth estimation techniques in terms of accuracy
generalizability
application scenarios and uncertainty studies in unsupervised networks.
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