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Tombari, F.[Federico],
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PRL(125), 2019, pp. 333-340.
Elsevier DOI
1909
Layered depth image, RGB-D inpainting,
Generative adversarial networks, Occlusion
BibRef
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Elsevier DOI
1911
Monocular depth estimation, Adversarial training, GAN
BibRef
Chen, Y.[Yu],
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Chen, H.[Hao],
Wei, X.S.[Xiu-Shen],
Liu, L.Q.[Ling-Qiao],
Yang, J.[Jian],
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],
Chochlakis, G.[Georgios],
Grammatikopoulos, L.[Lazaros],
Charou, E.[Eleni],
Generating Elevation Surface from a Single RGB Remotely Sensed Image
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RS(12), No. 12, 2020, pp. xx-yy.
DOI Link
2006
BibRef
Pilzer, A.[Andrea],
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Progressive Fusion for Unsupervised Binocular Depth Estimation Using
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PAMI(42), No. 10, October 2020, pp. 2380-2395.
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.,
Chambon, S.,
Puig, D.,
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,
BibRef
Ji, R.R.[Rong-Rong],
Li, K.[Ke],
Wang, Y.[Yan],
Sun, X.S.[Xiao-Shuai],
Guo, F.[Feng],
Guo, X.W.[Xiao-Wei],
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
Gadelha, M.[Matheus],
Rai, A.[Aartika],
Maji, S.[Subhransu],
Wang, R.[Rui],
Inferring 3D Shapes from Image Collections Using Adversarial Networks,
IJCV(128), No. 10-11, November 2020, pp. 2651-2664.
Springer DOI
2009
BibRef
Henderson, P.[Paul],
Ferrari, V.[Vittorio],
Learning Single-Image 3D Reconstruction by Generative Modelling of
Shape, Pose and Shading,
IJCV(128), No. 4, April 2020, pp. 835-854.
Springer DOI
2004
BibRef
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
BibRef
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
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
Nguyen-Phuoc, T.,
Li, C.,
Theis, L.,
Richardt, C.,
Yang, Y.,
HoloGAN: Unsupervised Learning of 3D Representations From Natural
Images,
ICCV19(7587-7596)
IEEE DOI
2004
BibRef
And:
NeruArch19(2037-2040)
IEEE DOI
2004
Shape, Training, Solid modeling, Generators, GAN
feature extraction, image sequences, object detection,
object tracking, pose estimation, rendering (computer graphics),
Generative adversarial networks
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
Depth Ordering, Single View 3D Reconstruction .