9.8.1.3 Single View 3D Reconstruction, Generative Adversarial Networks, GAN

Dhamo, H.[Helisa], Tateno, K.[Keisuke], Laina, I.[Iro], Navab, N.[Nassir], Tombari, F.[Federico],
Peeking behind objects: Layered depth prediction from a single image,
PRL(125), 2019, pp. 333-340.
Elsevier DOI 1909
Layered depth image, RGB-D inpainting, Generative adversarial networks, Occlusion BibRef

Groenendijk, R.[Rick], Karaoglu, S.[Sezer], Gevers, T.[Theo], Mensink, T.[Thomas],
On the benefit of adversarial training for monocular depth estimation,
CVIU(190), 2020, pp. 102848.
Elsevier DOI 1911
Monocular depth estimation, Adversarial training, GAN BibRef

Chen, Y.[Yu], Shen, C.H.[Chun-Hua], Chen, H.[Hao], Wei, X.S.[Xiu-Shen], Liu, L.Q.[Ling-Qiao], Yang, J.[Jian],
Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization,
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 Using Deep Learning,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Pilzer, A.[Andrea], Lathuilière, S., Xu, D.[Dan], Puscas, M.M.[Mihai M.], Ricci, E.[Elisa], Sebe, N.[Nicu],
Progressive Fusion for Unsupervised Binocular Depth Estimation Using Cycled Networks,
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 Image,
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

Wu, G.H.[Guang-Hui], Liu, H.[Hao], Wang, L.G.[Long-Guang], Li, K.H.[Kun-Hong], Guo, Y.L.[Yu-Lan], Chen, Z.P.[Zeng-Ping],
Self-Supervised Multi-Frame Monocular Depth Estimation for Dynamic Scenes,
CirSysVideo(34), No. 6, June 2024, pp. 4989-5001.
IEEE DOI 2406
Costs, Estimation, Feature extraction, Bundle adjustment, Transformers, Dynamics, Depth estimation, self-supervised learning, dynamic scenes BibRef

Wu, G.H.[Guang-Hui], Chen, L.[Lili], Chen, Z.P.[Zeng-Ping],
Uni-DPM: Unifying Self-Supervised Monocular Depth, Pose, and Object Motion Estimation with a Shared Representation,
MultMed(27), 2025, pp. 1498-1511.
IEEE DOI 2503
Cameras, Optical flow, Image motion analysis, Estimation, Videos, Motion segmentation, Geometry, Depth measurement 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

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

Wang, Y.F.[Yu-Fan], Zhao, Q.F.[Qun-Fei], Gan, Y.Z.[Yang-Zhou], Xia, Z.Y.[Ze-Yang],
Joint-Confidence-Guided Multi-Task Learning for 3D Reconstruction and Understanding From Monocular Camera,
IP(32), 2023, pp. 1120-1133.
IEEE DOI 2302
Task analysis, Multitasking, Decoding, Uncertainty, Semantics, Optimization, Estimation, Monocular scene, multi-task learning, stochastic trust mechanism BibRef

Wu, J.P.[Ji-Peng], Ji, R.R.[Rong-Rong], Wang, Q.[Qiang], Zhang, S.C.[Sheng-Chuan], Sun, X.S.[Xiao-Shuai], Wang, Y.[Yan], Xu, M.L.[Ming-Liang], Huang, F.Y.[Fei-Yue],
Fast Monocular Depth Estimation via Side Prediction Aggregation with Continuous Spatial Refinement,
MultMed(25), 2023, pp. 1204-1216.
IEEE DOI 2305
Estimation, Predictive models, Task analysis, Spatial resolution, Real-time systems, Generators, Generative adversarial networks, spatial refinement constraint BibRef

Gu, J.[Jiatao], Gao, Q.Z.[Qing-Zhe], Zhai, S.[Shuangfei], Chen, B.Q.[Bao-Quan], Liu, L.J.[Ling-Jie], Susskind, J.[Josh],
Control3Diff: Learning Controllable 3D Diffusion Models from Single-view Images,
3DV24(685-696)
IEEE DOI 2408
Training, Solid modeling, Image synthesis, Diffusion processes, Benchmark testing, Standards, diffusion models, 3D GAN 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 Patches,
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, 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, 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, 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.M.[Nick M.],
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 .