2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2019
DOI: 10.1109/cvpr.2019.00273
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Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation

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Cited by 218 publications
(153 citation statements)
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“…Monocular depth estimation is a fundamental but challenging task in computer vision, the goal of which is to predict a dense depth map from a given image. The technical progress in this area can be applied to widespread applications, such as scene understanding [1], [2], action recognition [3], 3D reconstruction [4], robotics [5], [6], etc. However, it is still a very challenging topic since one image may correspond to several real scenes and there are no other available clues, e.g.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Monocular depth estimation is a fundamental but challenging task in computer vision, the goal of which is to predict a dense depth map from a given image. The technical progress in this area can be applied to widespread applications, such as scene understanding [1], [2], action recognition [3], 3D reconstruction [4], robotics [5], [6], etc. However, it is still a very challenging topic since one image may correspond to several real scenes and there are no other available clues, e.g.…”
Section: Introductionmentioning
confidence: 99%
“…represent the horizontal and vertical convolutional Sobel operator, which calculate the gradient information and are sensitive to the shift of edges in x and y directions 2. …”
mentioning
confidence: 99%
“…(11)). Similarly, Chen et al [74] also leveraged semantic segmentation to improve the monocular depth estimation. In ref.…”
Section: Semi-supervised Monocular Depth Estimationmentioning
confidence: 99%
“…Multi-task learning with the goal of learning multiple tasks simultaneously [ 40 ] is nowadays based on CNNs [ 13 , 15 , 16 , 17 , 18 , 19 , 20 , 41 , 42 , 43 , 44 , 44 ]. Eigen and Fergus proposed a network structure that can estimate the depth, semantic labels, and the surface orientation of a scene [ 13 ].…”
Section: Related Workmentioning
confidence: 99%