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Layered depth image, RGB-D inpainting,
Generative adversarial networks, Occlusion
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Elsevier DOI
1911
Monocular depth estimation, Adversarial training, GAN
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Chen, Y.[Yu],
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Wei, X.S.[Xiu-Shen],
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Adversarial Learning of Structure-Aware Fully Convolutional Networks
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PAMI(42), No. 7, July 2020, pp. 1654-1669.
IEEE DOI
2006
Pose estimation,
Heating systems, Task analysis,
deep convolutional networks
BibRef
Panagiotou, E.[Emmanouil],
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Generating Elevation Surface from a Single RGB Remotely Sensed Image
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RS(12), No. 12, 2020, pp. xx-yy.
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BibRef
Pilzer, A.[Andrea],
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IEEE DOI
2009
BibRef
Earlier: A1, A3, A4, A5, A6, Only:
Unsupervised Adversarial Depth Estimation Using Cycled Generative
Networks,
3DV18(587-595)
IEEE DOI
1812
Estimation, Training, Deep learning, Cameras, Solid modeling,
Predictive models, Network architecture, Stereo depth estimation,
cycle network.
cameras, estimation theory, stereo image processing,
unsupervised learning, supervised regression,
Adversarial Learning
BibRef
Puscas, M.M.[Mihai Marian],
Xu, D.[Dan],
Pilzer, A.[Andrea],
Sebe, N.[Niculae],
Structured Coupled Generative Adversarial Networks for Unsupervised
Monocular Depth Estimation,
3DV19(18-26)
IEEE DOI
1806
Estimation, Generative adversarial networks, Couplings, Generators,
Task analysis, Testing, Unsupervised,
unsupervised monocular depth estimation
BibRef
Abdulwahab, S.,
Rashwan, H.A.,
García, M.Á.,
Jabreel, M.,
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Adversarial Learning for Depth and Viewpoint Estimation From a Single
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CirSysVideo(30), No. 9, September 2020, pp. 2947-2958.
IEEE DOI
2009
Solid modeling,
Generative adversarial networks, Color, Pose estimation, Face,
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Sun, X.S.[Xiao-Shuai],
Guo, F.[Feng],
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Wu, Y.J.[Yong-Jian],
Huang, F.Y.[Fei-Yue],
Luo, J.B.[Jie-Bo],
Semi-Supervised Adversarial Monocular Depth Estimation,
PAMI(42), No. 10, October 2020, pp. 2410-2422.
IEEE DOI
2009
Estimation, Generators, Training, Image reconstruction, Sensors,
Adaptation models, Data models, Monocular depth estimation,
semi-supervise learning
BibRef
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Rai, A.[Aartika],
Maji, S.[Subhransu],
Wang, R.[Rui],
Inferring 3D Shapes from Image Collections Using Adversarial Networks,
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Springer DOI
2009
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Henderson, P.[Paul],
Ferrari, V.[Vittorio],
Learning Single-Image 3D Reconstruction by Generative Modelling of
Shape, Pose and Shading,
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Springer DOI
2004
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Han, X.F.[Xian-Feng],
Laga, H.[Hamid],
Bennamoun, M.[Mohammed],
Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in
the Deep Learning Era,
PAMI(43), No. 5, May 2021, pp. 1578-1604.
IEEE DOI
2104
Survey, 3D Reconstruction. Image reconstruction, Shape, Training,
Deep learning, 3D video
BibRef
Li, K.H.[Kun-Hong],
Fu, Z.H.[Zhi-Heng],
Wang, H.Y.[Han-Yun],
Chen, Z.H.[Zong-Hao],
Guo, Y.L.[Yu-Lan],
Adv-Depth: Self-Supervised Monocular Depth Estimation With an
Adversarial Loss,
SPLetters(28), 2021, pp. 638-642.
IEEE DOI
2104
Generative adversarial networks, Generators, Estimation,
Feature extraction, Training, Task analysis,
single-image depth prediction
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Yang, Y.[Ying],
Xie, Y.[Yong],
Chen, X.H.[Xun-Hao],
Sun, Y.B.[Yu-Bao],
Hyperspectral Snapshot Compressive Imaging with Non-Local
Spatial-Spectral Residual Network,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link
2105
How to reconstruct 3D hyperspectral data from the 2D snapshot
measurement in a fast and high-quality manner.
BibRef
Liu, C.X.[Cai-Xia],
Kong, D.[Dehui],
Wang, S.F.[Shao-Fan],
Li, J.H.[Jing-Hua],
Yin, B.C.[Bao-Cai],
DLGAN: Depth-Preserving Latent Generative Adversarial Network for 3D
Reconstruction,
MultMed(23), 2021, pp. 2843-2856.
IEEE DOI
2109
Image reconstruction, Shape, Generative adversarial networks,
monocular depth image
BibRef
La Grassa, R.[Riccardo],
Gallo, I.[Ignazio],
Re, C.[Cristina],
Cremonese, G.[Gabriele],
Landro, N.[Nicola],
Pernechele, C.[Claudio],
Simioni, E.[Emanuele],
Gatti, M.[Mattia],
An Adversarial Generative Network Designed for High-Resolution
Monocular Depth Estimation from 2D HiRISE Images of Mars,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Xu, Y.F.[Yu-Fan],
Wang, Y.[Yan],
Huang, R.[Rui],
Lei, Z.[Zeyu],
Yang, J.[Junyao],
Li, Z.J.[Zi-Jian],
Unsupervised Learning of Depth Estimation and Camera Pose With
Multi-Scale GANs,
ITS(23), No. 10, October 2022, pp. 17039-17047.
IEEE DOI
2210
Generative adversarial networks, Unsupervised learning,
Task analysis, Pose estimation, Convolution, Learning systems, MSGAN,
unsupervised learning
BibRef
Cheng, Z.Y.[Zhi-Yuan],
Liang, J.[James],
Choi, H.J.[Hong-Jun],
Tao, G.H.[Guan-Hong],
Cao, Z.W.[Zhi-Wen],
Liu, D.F.[Dong-Fang],
Zhang, X.Y.[Xiang-Yu],
Physical Attack on Monocular Depth Estimation with Optimal Adversarial
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ECCV22(XXXVIII:514-532).
Springer DOI
2211
BibRef
Granot, N.[Niv],
Feinstein, B.[Ben],
Shocher, A.[Assaf],
Bagon, S.[Shai],
Irani, M.[Michal],
Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image
Generative Models,
CVPR22(13450-13459)
IEEE DOI
2210
Training, Deep learning, Visualization, Technological innovation,
Runtime, Europe, Image and video synthesis and generation
BibRef
Verdié, Y.[Yannick],
Song, J.[Jifei],
Mas, B.[Barnabé],
Busam, B.[Benjamin],
Leonardis, A.[Aleš],
McDonagh, S.[Steven],
CroMo: Cross-Modal Learning for Monocular Depth Estimation,
CVPR22(3927-3937)
IEEE DOI
2210
Training, Geometry, Pipelines, Estimation, Robustness,
Pattern recognition, 3D from single images,
Physics-based vision and shape-from-X
BibRef
Tewari, A.[Ayush],
Mallikarjun, B.R.,
Pan, X.G.[Xin-Gang],
Fried, O.[Ohad],
Agrawala, M.[Maneesh],
Theobalt, C.[Christian],
Disentangled3D: Learning a 3D Generative Model with Disentangled
Geometry and Appearance from Monocular Images,
CVPR22(1506-1515)
IEEE DOI
2210
Geometry, Deformable models, Training, Solid modeling,
Computational modeling, Cameras, 3D from single images, Vision + graphics
BibRef
Bhat, S.F.[Shariq Farooq],
Alhashim, I.[Ibraheem],
Wonka, P.[Peter],
LocalBins: Improving Depth Estimation by Learning Local Distributions,
ECCV22(I:480-496).
Springer DOI
2211
BibRef
Akada, H.[Hiroyasu],
Bhat, S.F.[Shariq Farooq],
Alhashim, I.[Ibraheem],
Wonka, P.[Peter],
Self-Supervised Learning of Domain Invariant Features for Depth
Estimation,
WACV22(997-1007)
IEEE DOI
2202
Training, Representation learning, Measurement, Image segmentation,
Codes, Semantics, Transfer, Few-shot, GANs
BibRef
Lu, Y.W.[Ya-Wen],
Wang, Y.X.[Yu-Xing],
Parikh, D.[Devarth],
Xin, Y.[Yuan],
Lu, G.Y.[Guo-Yu],
Extending Single Beam Lidar To Full Resolution By Fusing with Single
Image Depth Estimation,
ICPR21(6343-6350)
IEEE DOI
2105
Laser radar, Image resolution, Uncertainty,
Pipelines, Estimation,
Calibration
BibRef
Xing, X.X.[Xiao-Xia],
Cai, Y.H.[Ying-Hao],
Wang, Y.Q.[Yan-Qing],
Lu, T.[Tao],
Yang, Y.P.[Yi-Ping],
Wen, D.[Dayong],
Dynamic Guided Network for Monocular Depth Estimation,
ICPR21(5459-5465)
IEEE DOI
2105
Computational modeling, Neural networks, Estimation, Decoding,
Pattern recognition, Kernel
BibRef
Liu, J.[Jing],
Zhang, X.N.[Xiao-Na],
Li, Z.X.[Zhao-Xin],
Mao, T.L.[Tian-Lu],
Multi-Scale Residual Pyramid Attention Network for Monocular Depth
Estimation,
ICPR21(5137-5144)
IEEE DOI
2105
Geometry, Image segmentation, Correlation, Semantics, Estimation, context
BibRef
Chiu, M.J.[Mian-Jhong],
Chiu, W.C.[Wei-Chen],
Chen, H.T.[Hua-Tsung],
Chuang, J.H.[Jen-Hui],
Real-time Monocular Depth Estimation with Extremely Light-Weight
Neural Network,
ICPR21(7050-7057)
IEEE DOI
2105
Performance evaluation, Computational modeling, Semantics,
Estimation, Graphics processing units, Computer architecture,
Supervised learning
BibRef
Frisky, A.Z.K.[Aufaclav Zatu Kusuma],
Putranto, A.[Andi],
Zambanini, S.[Sebastian],
Sablatnig, R.[Robert],
Mccnet: Multi-color Cascade Network with Weight Transfer for Single
Image Depth Prediction on Outdoor Relief Images,
PATRECH20(263-278).
Springer DOI
2103
BibRef
Hambarde, P.,
Dudhane, A.,
Patil, P.W.,
Murala, S.,
Dhall, A.,
Depth Estimation From Single Image And Semantic Prior,
ICIP20(1441-1445)
IEEE DOI
2011
Estimation, Semantics, Generators, Robot sensing systems,
Laser radar, Generative adversarial networks, Training,
Fine-level depth-map.
BibRef
Vankadari, M.[Madhu],
Garg, S.[Sourav],
Majumder, A.[Anima],
Kumar, S.[Swagat],
Behera, A.[Ardhendu],
Unsupervised Monocular Depth Estimation for Night-time Images Using
Adversarial Domain Feature Adaptation,
ECCV20(XXVIII:443-459).
Springer DOI
2011
BibRef
Khan, S.H.[Salman H.],
Guo, Y.L.[Yu-Lan],
Hayat, M.[Munawar],
Barnes, N.[Nick],
Unsupervised Primitive Discovery for Improved 3D Generative Modeling,
CVPR19(9731-9740).
IEEE DOI
2002
BibRef
Chen, Z.Q.[Zhi-Qin],
Zhang, H.[Hao],
Learning Implicit Fields for Generative Shape Modeling,
CVPR19(5932-5941).
IEEE DOI
2002
BibRef
Tang, J.P.[Jia-Peng],
Han, X.G.[Xiao-Guang],
Pan, J.[Junyi],
Jia, K.[Kui],
Tong, X.[Xin],
A Skeleton-Bridged Deep Learning Approach for Generating Meshes of
Complex Topologies From Single RGB Images,
CVPR19(4536-4545).
IEEE DOI
2002
BibRef
Xiang, C.[Chong],
Qi, C.R.[Charles R.],
Li, B.[Bo],
Generating 3D Adversarial Point Clouds,
CVPR19(9128-9136).
IEEE DOI
2002
BibRef
Hambarde, P.,
Dudhane, A.,
Murala, S.,
Single Image Depth Estimation Using Deep Adversarial Training,
ICIP19(989-993)
IEEE DOI
1910
Scene depth, deep learning, adversarial training
See also CDNet: Single Image De-Hazing Using Unpaired Adversarial Training.
BibRef
Aleotti, F.[Filippo],
Tosi, F.[Fabio],
Poggi, M.[Matteo],
Mattoccia, S.[Stefano],
Generative Adversarial Networks for Unsupervised Monocular Depth
Prediction,
3D-Wild18(I:337-354).
Springer DOI
1905
BibRef
Kniaz, V.V.,
Remondino, F.,
Knyaz, V.A.,
Generative Adversarial Networks for Single Photo 3D Reconstruction,
3DARCH19(403-408).
DOI Link
1904
BibRef
Kumar, A.C.,
Bhandarkar, S.M.,
Prasad, M.,
Monocular Depth Prediction Using Generative Adversarial Networks,
DeepSLAM18(413-4138)
IEEE DOI
1812
Image reconstruction, Generators, Training,
Generative adversarial networks, Estimation, Cameras
BibRef
Mehta, I.,
Sakurikar, P.,
Narayanan, P.J.,
Structured Adversarial Training for Unsupervised Monocular Depth
Estimation,
3DV18(314-323)
IEEE DOI
1812
image reconstruction, stereo image processing,
unsupervised learning, StrAT, adversarial framework,
3D
BibRef
Jiang, L.[Li],
Shi, S.S.[Shao-Shuai],
Qi, X.J.[Xiao-Juan],
Jia, J.Y.[Jia-Ya],
GAL: Geometric Adversarial Loss for Single-View 3D-Object
Reconstruction,
ECCV18(VIII: 820-834).
Springer DOI
1810
BibRef
Chapter on 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings continues in
Monocular 3D Object Detection .