9.8.1 Single Image, Single View 3D Reconstruction, Learning

Recurrent Asynchronous Multimodal Networks + Events, Frames, Semantic labels, and Depth maps recorded in CARLA simulator,
2021
HTML Version. Code, Recurrent Networks. Code, Monocular Depth. Dataset, Monocular Depth.

Choi, S.H.[Sung-Hwan], Min, D.B.[Dong-Bo], Ham, B.[Bumsub], Kim, Y.J.[Young-Jung], Oh, C.J.[Chang-Jae], Sohn, K.H.[Kwang-Hoon],
Depth Analogy: Data-Driven Approach for Single Image Depth Estimation Using Gradient Samples,
IP(24), No. 12, December 2015, pp. 5953-5966.
IEEE DOI 1512
Poisson distribution BibRef

Jung, H., Kim, Y.J.[Young-Jung], Min, D.B.[Dong-Bo], Oh, C.J.[Chang-Jae], Sohn, K.H.[Kwang-Hoon],
Depth prediction from a single image with conditional adversarial networks,
ICIP17(1717-1721)
IEEE DOI 1803
Databases, Estimation, Generators, Periodic structures, Spatial resolution, Training, Depth from a single image, generative adversarial learning BibRef

Kim, Y.J.[Young-Jung], Min, D.B.[Dong-Bo], Ham, B.[Bumsub], Sohn, K.H.[Kwang-Hoon],
Fast Domain Decomposition for Global Image Smoothing,
IP(26), No. 8, August 2017, pp. 4079-4091.
IEEE DOI 1707
concave programming, decomposition, least squares approximations, minimisation, computational photography application, edge-preserving smoothing, BibRef

Kim, Y.J.[Young-Jung], Jung, H., Min, D.B.[Dong-Bo], Sohn, K.H.[Kwang-Hoon],
Deeply Aggregated Alternating Minimization for Image Restoration,
CVPR17(284-292)
IEEE DOI 1711
Algorithm design and analysis, Data models, Image reconstruction, Image restoration, Minimization, Neural networks, Optimization BibRef

Kim, S.[Sunok], Choi, S.H.[Sung-Hwan], Sohn, K.H.[Kwang-Hoon],
Learning depth from a single image using visual-depth words,
ICIP15(1895-1899)
IEEE DOI 1512
K-means clustering BibRef

Kim, Y.J.[Young-Jung], Choi, S.H.[Sung-Hwan], Sohn, K.H.[Kwang-Hoon],
Data-driven single image depth estimation using weighted median statistics,
ICIP14(3808-3812)
IEEE DOI 1502
Based on matches to similar images. Wrong section, one of several, learn patterns from other images, apply. BibRef

Herrera, J.L.[Jose L.], del-Bianco, C.R.[Carlos R.], García, N.[Narciso],
Automatic Depth Extraction from 2D Images Using a Cluster-Based Learning Framework,
IP(27), No. 7, July 2018, pp. 3288-3299.
IEEE DOI 1805
BibRef
Earlier:
Learning 3D structure from 2D images using LBP features,
ICIP14(2022-2025)
IEEE DOI 1502
feature extraction, image colour analysis, image convertors, image filtering, image representation, image segmentation, machine learning BibRef

Herrera, J.L.[Jose L.], Konrad, J.[Janusz], del-Bianco, C.R.[Carlos R.], Garcia, N.[Narciso],
Learning-based depth estimation from 2D images using GIST and saliency,
ICIP15(4753-4757)
IEEE DOI 1512
2D-to-3D Image Conversion; Depth maps; GIST Descriptor; Saliency. Color BibRef

Zhang, Z., Xu, C., Yang, J., Gao, J., Cui, Z.,
Progressive Hard-Mining Network for Monocular Depth Estimation,
IP(27), No. 8, August 2018, pp. 3691-3702.
IEEE DOI 1806
data mining, estimation theory, feature extraction, image colour analysis, image resolution, recursive learning BibRef

He, L., Wang, G., Hu, Z.,
Learning Depth From Single Images With Deep Neural Network Embedding Focal Length,
IP(27), No. 9, September 2018, pp. 4676-4689.
IEEE DOI 1807
Markov processes, image processing, learning (artificial intelligence), neural nets, single images BibRef

Ren, X.Y.[Xiao-Yuan], Jiang, L.B.[Li-Bing], Tang, X.A.[Xiao-An], Zhang, J.[Junda],
Single-Image 3D Pose Estimation for Texture-Less Object via Symmetric Prior,
IEICE(E101-D), No. 7, July 2018, pp. 1972-1975.
WWW Link. 1807
BibRef

Bostan, E., Kamilov, U.S., Waller, L.,
Learning-Based Image Reconstruction via Parallel Proximal Algorithm,
SPLetters(25), No. 7, July 2018, pp. 989-993.
IEEE DOI 1807
image reconstruction, iterative methods, learning (artificial intelligence), parallel algorithms, statistical modeling BibRef

Zhang, Z.Y.[Zhen-Yu], Xu, C.Y.[Chun-Yan], Yang, J.[Jian], Tai, Y.[Ying], Chen, L.[Liang],
Deep hierarchical guidance and regularization learning for end-to-end depth estimation,
PR(83), 2018, pp. 430-442.
Elsevier DOI 1808
Depth estimation, Multi-regularization, Deep neural network BibRef

Hou, B., Khanal, B., Alansary, A., McDonagh, S., Davidson, A., Rutherford, M., Hajnal, J.V., Rueckert, D., Glocker, B., Kainz, B.,
3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images,
MedImg(37), No. 8, August 2018, pp. 1737-1750.
IEEE DOI 1808
Image reconstruction, Manuals, Robustness, image registration BibRef

Santos, R.[Roi], Pardo, X.M.[Xose M.], Fdez-Vidal, X.R.[Xose R.],
Scene wireframes sketching for Unmanned Aerial Vehicles,
PR(86), 2019, pp. 354-367.
Elsevier DOI 1811
3D abstraction, Reconstruction, Line-based sketching, UAV BibRef

Mohaghegh, H., Karimi, N., Soroushmehr, S.M.R., Samavi, S., Najarian, K.,
Aggregation of Rich Depth-Aware Features in a Modified Stacked Generalization Model for Single Image Depth Estimation,
CirSysVideo(29), No. 3, March 2019, pp. 683-697.
IEEE DOI 1903
BibRef
Earlier:
Single image depth estimation using joint local-global features,
ICPR16(727-732)
IEEE DOI 1705
Estimation, Training, Semantics, Solid modeling, modified stacked generalization model. Monocular depth cues. Correlation, Databases, Data-driven approaches, Depth estimation, Joint local-global framework, KNN regression model. BibRef

Zeng, H.[Hui], Zhang, R.[Ran], Wang, X.Q.[Xiu-Qing], Fu, D.M.[Dong-Mei], Wei, Q.T.[Qing-Ting],
Dempster-Shafer evidence theory-based multi-feature learning and fusion method for non-rigid 3D model retrieval,
IET-CV(13), No. 3, April 2019, pp. 261-266.
DOI Link 1904
BibRef

Shalaby, A.[Abdou], Elmogy, M.[Mohammed], Elfetouh, A.A.[Ahmed Abo],
3D image reconstruction from different image formats using marching cubes technique,
IJCVR(9), No. 3, 2019, pp. 293-309.
DOI Link 1906
3D from any 2D image. BibRef

Li, J.[Jun], Yuce, C.[Can], Klein, R.[Reinhard], Yao, A.[Angela],
A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images,
CVIU(186), 2019, pp. 25-36.
Elsevier DOI 1908
BibRef
Earlier: A1, A3, A4, Only: ICCV17(3392-3400)
IEEE DOI 1802
Depth estimation, Depth gradient, Set loss, Indoor scenes, Man-made objects. image colour analysis, learning (artificial intelligence), NYU, NYU Depth, accurate depth map, deep learning methods, Training BibRef

Bartoli, A.[Adrien],
A Differential-Algebraic Projective Framework for the Deformable Single-View Geometry of the 1D Perspective Camera,
JMIV(61), No. 7, September 2019, pp. 1051-1068.
WWW Link. 1908
Single-View Geometry (SVG) for the world-to-image mapping. BibRef

Fan, B.[Bin], Kong, Q.Q.[Qing-Qun], Wang, X.C.[Xin-Chao], Wang, Z.H.[Zhi-Heng], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong], Fua, P.[Pascal],
A Performance Evaluation of Local Features for Image-Based 3D Reconstruction,
IP(28), No. 10, October 2019, pp. 4774-4789.
IEEE DOI 1909
What features for 3D descriptions. feature extraction, image capture, image classification, image matching, image reconstruction, image sequences, Internet, image matching BibRef

Zheng, Y.[Yan], Guo, B.L.[Bao-Long], Yan, Y.Y.[Yun-Yi], He, W.P.[Wang-Peng],
O2O Method for Fast 2D Shape Retrieval,
IP(28), No. 11, November 2019, pp. 5366-5378.
IEEE DOI 1909
Offline to offline. Shape, Databases, Transform coding, Histograms, Graphical models, Distribution functions, Measurement, Fast 2D shape retrieval, shape descriptor BibRef

Dai, R.Y.[Ren-Yue], Gao, Y.B.[Yong-Bin], Fang, Z.J.[Zhi-Jun], Jiang, X.Y.[Xiao-Yan], Wang, A.[Anjie], Zhang, J.[Juan], Zhong, C.S.[Ceng-Si],
Unsupervised learning of depth estimation based on attention model and global pose optimization,
SP:IC(78), 2019, pp. 284-292.
Elsevier DOI 1909
Depth estimation, Attention model, Global pose optimization BibRef

Wiles, O.[Olivia], Zisserman, A.[Andrew],
Learning to Predict 3D Surfaces of Sculptures from Single and Multiple Views,
IJCV(127), No. 11-12, December 2019, pp. 1780-1800.
Springer DOI 1911
BibRef
Earlier: Wiles, O.[Olivia], Zisserman, A.[Andrew],
3D Surface Reconstruction by Pointillism,
DeepLearn-G18(III:263-280).
Springer DOI 1905
BibRef

Xia, Y.[Yan], Wang, C.[Cheng], Xu, Y.S.[Yu-Sheng], Zang, Y.[Yu], Liu, W.[Weiquan], Li, J.[Jonathan], Stilla, U.[Uwe],
RealPoint3D: Generating 3D Point Clouds from a Single Image of Complex Scenarios,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Bao, W., Xu, B., Chen, Z.,
MonoFENet: Monocular 3D Object Detection With Feature Enhancement Networks,
IP(29), 2020, pp. 2753-2765.
IEEE DOI 2001
3D object detection, monocular images, feature enhancement, neural networks, autonomous driving BibRef

Moreau, A.[Ambroise], Mancas, M.[Matei], Dutoit, T.[Thierry],
Depth prediction from 2D images: A taxonomy and an evaluation study,
IVC(93), 2020, pp. 103825.
Elsevier DOI 2001
Depth prediction, Machine learning, Deep learning BibRef

Zhang, Y., Feng, Y., Liu, X., Zhai, D., Ji, X., Wang, H., Dai, Q.,
Color-Guided Depth Image Recovery With Adaptive Data Fidelity and Transferred Graph Laplacian Regularization,
CirSysVideo(30), No. 2, February 2020, pp. 320-333.
IEEE DOI 2002
Laplace equations, Color, Image color analysis, Task analysis, Adaptation models, Image resolution, Optimization, mixture probability maximization BibRef

Koch, T.[Tobias], Liebel, L.[Lukas], Körner, M.[Marco], Fraundorfer, F.[Friedrich],
Comparison of monocular depth estimation methods using geometrically relevant metrics on the IBims-1 dataset,
CVIU(191), 2020, pp. 102877.
Elsevier DOI 2002
BibRef

Chen, W.[Wei], Luo, X.[Xin], Liang, Z.F.[Zheng-Fa], Li, C.[Chen], Wu, M.F.[Ming-Fei], Gao, Y.M.[Yuan-Ming], Jia, X.G.[Xiao-Gang],
A Unified Framework for Depth Prediction from a Single Image and Binocular Stereo Matching,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Häne, C.[Christian], Tulsiani, S.[Sohubham], Malik, J.[Jitendra],
Hierarchical Surface Prediction,
PAMI(42), No. 6, June 2020, pp. 1348-1361.
IEEE DOI 2005
Geometry, Image color analysis, Shape, Octrees, Color, Surface reconstruction, Single view reconstruction, geometry prediction BibRef

Zhao, D.[Dong], Asano, Y.[Yuta], Gu, L.[Lin], Sato, I.[Imari], Zhou, H.[Huixin],
City-Scale Distance Sensing via Bispectral Light Extinction in Bad Weather,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Liu, J.[Jun], Li, Q.[Qing], Cao, R.[Rui], Tang, W.M.[Wen-Ming], Qiu, G.P.[Guo-Ping],
A contextual conditional random field network for monocular depth estimation,
IVC(98), 2020, pp. 103922.
Elsevier DOI 2006
Monocular depth estimation, Deep neural network, Skip connection, Conditional random field BibRef

Ye, X.C.[Xin-Chen], Zhang, M.L.[Ming-Liang], Yang, J.Y.[Jing-Yu], Fan, X.[Xin], Guo, F.F.[Fang-Fang],
A sparsity-promoting image decomposition model for depth recovery,
PR(107), 2020, pp. 107506.
Elsevier DOI 2008
Image decomposition, Depth recovery, Depth discontinuities, Depth cameras BibRef

Huang, S.F.[Shao-Fei], Liu, S.[Si], Hui, T.R.[Tian-Rui], Han, J.Z.[Ji-Zhong], Li, B.[Bo], Feng, J.S.[Jia-Shi], Yan, S.C.[Shui-Cheng],
ORDNet: Capturing Omni-Range Dependencies for Scene Parsing,
IP(29), 2020, pp. 8251-8263.
IEEE DOI 2008
Relative range. Semantics, Convolution, Task analysis, Feature extraction, Correlation, Cats, Visualization, Scene parsing, self-attention BibRef

Cao, Y.Z.[Yuan-Zhouhan], Zhao, T.Q.[Tian-Qi], Xian, K.[Ke], Shen, C.H.[Chun-Hua], Cao, Z.G.[Zhi-Guo], Xu, S.G.[Shu-Gong],
Monocular Depth Estimation With Augmented Ordinal Depth Relationships,
CirSysVideo(30), No. 8, August 2020, pp. 2674-2682.
IEEE DOI 2008
Estimation, Measurement, Videos, Training, Motion pictures, Task analysis, Labeling, Depth estimation, RGB-D dataset, deep network BibRef

Mathew, A.[Alwyn], Mathew, J.[Jimson],
Monocular depth estimation with SPN loss,
IVC(100), 2020, pp. 103934.
Elsevier DOI 2008
Depth estimation, Monocular depth estimation BibRef

Luo, C.X.[Chen-Xu], Yang, Z.H.[Zhen-Heng], Wang, P.[Peng], Wang, Y.[Yang], Xu, W.[Wei], Nevatia, R.[Ram], Yuille, A.L.[Alan L.],
Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding,
PAMI(42), No. 10, October 2020, pp. 2624-2641.
IEEE DOI 2009
Estimation, Optical imaging, Cameras, Videos, Geometry, Task analysis, Depth estimation, unsupervised learning BibRef

Yang, Z.H.[Zhen-Heng], Wang, P.[Peng], Wang, Y.[Yang], Xu, W.[Wei], Nevatia, R.[Ram],
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding,
ApolloScape18(V:691-709).
Springer DOI 1905
BibRef

Ye, X.C.[Xin-Chen], Chen, S.[Shude], Xu, R.[Rui],
DPNet: Detail-preserving network for high quality monocular depth estimation,
PR(109), 2021, pp. 107578.
Elsevier DOI 2009
Depth estimation, Detail-preserving, Spatial, Attention, Depth map BibRef

Rajeswar, S.[Sai], Mannan, F.[Fahim], Golemo, F.[Florian], Parent-Lévesque, J.[Jérôme], Vazquez, D.[David], Nowrouzezahrai, D.[Derek], Courville, A.[Aaron],
Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images Using a View-Based Representation,
IJCV(128), No. 10-11, November 2020, pp. 2478-2493.
Springer DOI 2009
BibRef

Xie, H.Z.[Hao-Zhe], Yao, H.X.[Hong-Xun], Zhang, S.P.[Sheng-Ping], Zhou, S.C.[Shang-Chen], Sun, W.X.[Wen-Xiu],
Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images,
IJCV(128), No. 12, December 2020, pp. 2919-2935.
Springer DOI 2010
RNN for shape recovery. BibRef

Su, Y.T.[Yu-Ting], Li, Y.Q.[Yu-Qian], Nie, W.Z.[Wei-Zhi], Song, D.[Dan], Liu, A.A.[An-An],
Joint Heterogeneous Feature Learning and Distribution Alignment for 2D Image-Based 3D Object Retrieval,
CirSysVideo(30), No. 10, October 2020, pp. 3765-3776.
IEEE DOI 2010
Feature extraction, Visualization, Task analysis, Solid modeling, distribution alignment BibRef

Turhan, C.G.[Ceren Guzel], Bilge, H.S.[Hasan Sakir],
Class-aware single image to 3D object translational autoencoder,
IET-IPR(14), No. 13, November 2020, pp. 3046-3053.
DOI Link 2012
BibRef

Liu, H.J.[Hua-Jun], Lei, D.[Dian], Zhu, Q.[Qing], Sui, H.G.[Hai-Gang], Zhang, H.R.[Huan-Ran], Wang, Z.[Ziyan],
Single-image depth estimation by refined segmentation and consistency reconstruction,
SP:IC(90), 2021, pp. 116048.
Elsevier DOI 2012
Depth estimation, Image segmentation, Consistency reconstruction, Single image BibRef

Chen, H.X., Li, K., Fu, Z., Liu, M., Chen, Z., Guo, Y.,
Distortion-Aware Monocular Depth Estimation for Omnidirectional Images,
SPLetters(28), 2021, pp. 334-338.
IEEE DOI 2102
Distortion, Convolution, Strips, Feature extraction, Estimation, Training, Kernel, Depth estimation, deformable convolution, omnidirectional images BibRef

Luo, N.[Nan], Huang, L.[Ling], Wang, Q.[Quan], Liu, G.[Gang],
An Improved Algorithm Robust to Illumination Variations for Reconstructing Point Cloud Models from Images,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Raihan, A.J.[A. Jarina], Abas, P.E.[Pg Emeroylariffion], de Silva, L.C.[Liyanage C.],
Depth estimation for underwater images from single view image,
IET-IPR(14), No. 16, 19 December 2020, pp. 4188-4197.
DOI Link 2103
BibRef

Jiang, H.Z.[Hong-Zhi], Li, Y.X.[Yu-Xi], Zhao, H.J.[Hui-Jie], Li, X.D.[Xu-Dong], Xu, Y.[Yang],
Parallel Single-Pixel Imaging: A General Method for Direct-Global Separation and 3D Shape Reconstruction Under Strong Global Illumination,
IJCV(129), No. 4, April 2021, pp. 1060-1086.
Springer DOI 2104
BibRef
And: Correction: IJCV(129), No. 5, May 2021, pp. 1787-1787.
Springer DOI 2105
Separation of direct and global illumination, to get 3D. BibRef

Xue, F.[Feng], Cao, J.F.[Jun-Feng], Zhou, Y.[Yu], Sheng, F.[Fei], Wang, Y.[Yankai], Ming, A.[Anlong],
Boundary-induced and scene-aggregated network for monocular depth prediction,
PR(115), 2021, pp. 107901.
Elsevier DOI 2104
Monocular depth prediction, Boundary-induced, Depth correlation BibRef

Xu, X.F.[Xian-Fa], Chen, Z.[Zhe], Yin, F.L.[Fu-Liang],
Monocular Depth Estimation With Multi-Scale Feature Fusion,
SPLetters(28), 2021, pp. 678-682.
IEEE DOI 2104
Convolution, Feature extraction, Estimation, Training, Fuses, Task analysis, Kernel, Attention, multi-scale feature fusion BibRef

Xu, X.F.[Xian-Fa], Chen, Z.[Zhe], Yin, F.L.[Fu-Liang],
Multi-Scale Spatial Attention-Guided Monocular Depth Estimation With Semantic Enhancement,
IP(30), 2021, pp. 8811-8822.
IEEE DOI 2111
Estimation, Semantics, Mutual information, Feature extraction, Correlation, Cameras, Visualization, Depth estimation, semantic enhancement BibRef

Ye, X.C.[Xin-Chen], Fan, X.[Xin], Zhang, M.L.[Ming-Liang], Xu, R.[Rui], Zhong, W.[Wei],
Unsupervised Monocular Depth Estimation via Recursive Stereo Distillation,
IP(30), 2021, pp. 4492-4504.
IEEE DOI 2105
Estimation, Training, Testing, Knowledge engineering, Recursive estimation, Network architecture, Image reconstruction, recursive BibRef

Song, X.B.[Xi-Bin], Li, W.[Wei], Zhou, D.F.[Ding-Fu], Dai, Y.C.[Yu-Chao], Fang, J.[Jin], Li, H.D.[Hong-Dong], Zhang, L.J.[Liang-Jun],
MLDA-Net: Multi-Level Dual Attention-Based Network for Self-Supervised Monocular Depth Estimation,
IP(30), 2021, pp. 4691-4705.
IEEE DOI 2105
BibRef

Xu, W.P.[Wan-Peng], Zou, L.[Ling], Wu, L.D.[Ling-Da], Fu, Z.P.[Zhi-Peng],
Self-Supervised Monocular Depth Learning in Low-Texture Areas,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Madhuanand, L.[Logambal], Nex, F.[Francesco], Yang, M.Y.[Michael Ying],
Self-supervised monocular depth estimation from oblique UAV videos,
PandRS(176), 2021, pp. 1-14.
Elsevier DOI 2106
Depth estimation, Monocular, UAV video, Self-supervised learning, Scene Understanding BibRef

Su, W.[Wen], Zhang, H.F.[Hai-Feng], Zhou, Q.[Quan], Yang, W.Z.[Wen-Zhen], Wang, Z.F.[Zeng-Fu],
Monocular Depth Estimation Using Information Exchange Network,
ITS(22), No. 6, June 2021, pp. 3491-3503.
IEEE DOI 2106
Estimation, Semantics, Information exchange, Image segmentation, Convolution, Feature extraction, semantic information BibRef

Bian, J.W.[Jia-Wang], Zhan, H.Y.[Huang-Ying], Wang, N.Y.[Nai-Yan], Li, Z.C.[Zhi-Chao], Zhang, L.[Le], Shen, C.H.[Chun-Hua], Cheng, M.M.[Ming-Ming], Reid, I.D.[Ian D.],
Unsupervised Scale-Consistent Depth Learning from Video,
IJCV(129), No. 9, September 2021, pp. 2548-2564.
Springer DOI 2108
Learn from video input. BibRef

Ali, U.[Usman], Bayramli, B.[Bayram], Alsarhan, T.[Tamam], Lu, H.T.[Hong-Tao],
A lightweight network for monocular depth estimation with decoupled body and edge supervision,
IVC(113), 2021, pp. 104261.
Elsevier DOI 2108
Monocular depth estimation, Deep learning, Lightweight network BibRef

Li, S.[Shuai], Shi, J.Y.[Jia-Ying], Song, W.F.[Wen-Feng], Hao, A.[Aimin], Qin, H.[Hong],
Hierarchical Object Relationship Constrained Monocular Depth Estimation.,
PR(120), 2021, pp. 108116.
Elsevier DOI 2109
Monocular Depth Estimation, Semantic Constraints, Hierarchical Object Relationship, Global and Local Context BibRef

Tao, Y.[Yu], Muller, J.P.[Jan-Peter], Xiong, S.[Siting], Conway, S.J.[Susan J.],
MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Song, M.S.[Min-Soo], Lim, S.[Seokjae], Kim, W.J.[Won-Jun],
Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals,
CirSysVideo(31), No. 11, November 2021, pp. 4381-4393.
IEEE DOI 2112
Estimation, Laplace equations, Decoding, Feature extraction, Convolution, Color, Image reconstruction, weight standardization BibRef

Hendra, A.[Andi], Kanazawa, Y.S.[Yasu-Shi],
Smaller Residual Network for Single Image Depth Estimation,
IEICE(E104-D), No. 11, November 2021, pp. 1992-2001.
WWW Link. 2112
BibRef

Yang, J.L.[Jing-Lun], Li, Y.[Youhua], Yang, L.[Lu],
Shape transformer nets: Generating viewpoint-invariant 3D shapes from a single image,
JVCIR(81), 2021, pp. 103345.
Elsevier DOI 2112
3D shape generation, Invariant viewpoint, Disentanglement, B-spline surfaces BibRef

Chang, Y.[Yakun], Jung, C.[Cheolkon], Sun, J.[Jun],
Joint Reflection Removal and Depth Estimation from a Single Image,
Cyber(51), No. 12, December 2021, pp. 5836-5849.
IEEE DOI 2112
Estimation, Glass, Cameras, Image edge detection, Feature extraction, Collaboration, Deep learning, transmission recovery BibRef

Takeyama, K.[Kojiro],
3D Reconstruction of Edge Line by ICP-based Matching with Geometric Constraints,
DICTA20(1-8)
IEEE DOI 2201
Iterative closest point algorithm, Image edge detection, Estimation, Cameras, Robustness, Strain, SFM, spatial map, edge line, iterative closest point algorithm BibRef

Lo, C.C.[Chen-Chou], Vandewalle, P.[Patrick],
Depth Estimation from Monocular Images and Sparse Radar Using Deep Ordinal Regression Network,
ICIP21(3343-3347)
IEEE DOI 2201
Deep learning, Rain, Radar measurements, Image processing, Estimation, Radar, monocular depth estimation, radar, nuScenes BibRef

Li, K.[Keyao], Li, G.[Ge], Li, T.[Thomas],
Rethinking Training Objective for Self-Supervised Monocular Depth Estimation: Semantic Cues To Rescue,
ICIP21(3308-3312)
IEEE DOI 2201
Training, Integrated optics, Solid modeling, Semantics, Estimation, Optical variables control, self-supervised learning, semantic cues BibRef

Jiang, C.[Chenweinan], Liu, H.[Haichun], Li, L.[Lanzhen], Pan, C.C.[Chang-Chun],
Attention-Based Self-Supervised Learning Monocular Depth Estimation With Edge Refinement,
ICIP21(3218-3222)
IEEE DOI 2201
Image edge detection, Neural networks, Brightness, Estimation, Feature extraction, Videos, self-supervised learning, monocular, edge refinement BibRef

Li, R.[Runfa], Nguyen, T.[Truong],
SM3D: Simultaneous Monocular Mapping and 3D Detection,
ICIP21(3652-3656)
IEEE DOI 2201
Training, Location awareness, Deep learning, Image processing, Pose estimation, SM3D, Monocular Mapping, Monocular 3D detection, Depth Estimation BibRef

Victor, A.C.[Ankita Christine], Sreevalsan-Nair, J.[Jaya],
Building 3D Virtual Worlds from Monocular Images of Urban Road Traffic Scenes,
ISVC21(II:461-474).
Springer DOI 2112
BibRef

Roddick, T.[Thomas], Biggs, B.[Benjamin], Reino, D.O.[Daniel Olmeda], Cipolla, R.[Roberto],
On the Road to Large-Scale 3D Monocular Scene Reconstruction using Deep Implicit Functions,
AVVision21(2875-2884)
IEEE DOI 2112
Training, Image resolution, Shape, Roads, Pipelines, Hazards BibRef

Heylen, J.[Jonas], de Wolf, M.[Mark], Dawagne, B.[Bruno], Proesmans, M.[Marc], Van Gool, L.J.[Luc J.], Abbeloos, W.[Wim], Abdelkawy, H.[Hazem], Reino, D.O.[Daniel Olmeda],
MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation,
3DODI21(923-934)
IEEE DOI 2112
Image segmentation, Pose estimation, Training data, Object detection, Benchmark testing BibRef

Souza, M.R.[Marcos R.], Conceição, J.S.[Jhonatas S.], Flores-Campana, J.L.[Jose L.], Decker, L.G.L.[Luis G. L.], Luvizon, D.C.[Diogo C.], Carvalho, G.S.P.[Gustavo Sutter P.], Maia, H.A.[Helena A.], Pedrini, H.[Helio],
Pyramidal Layered Scene Inference with Image Outpainting for Monocular View Synthesis,
CAIP21(I:37-46).
Springer DOI 2112
BibRef

Hornauer, J.[Julia], Nalpantidis, L.[Lazaros], Belagiannis, V.[Vasileios],
Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained Hardware,
ERCVAD21(954-962)
IEEE DOI 2112
Training, Deep learning, Visualization, Adaptation models, Neural networks, Estimation, Computer architecture BibRef

Laradji, I.[Issam], Rodríguez, P.[Pau], Vazquez, D.[David], Nowrouzezahrai, D.[Derek],
SSR: Semi-supervised Soft Rasterizer for single-view 2D to 3D Reconstruction,
Diff3D21(1427-1436)
IEEE DOI 2112
Training, Codes, Estimation, Entropy BibRef

Ramamonjisoa, M.[Michaël], Firman, M.[Michael], Watson, J.[Jamie], Lepetit, V.[Vincent], Turmukhambetov, D.[Daniyar],
Single Image Depth Prediction with Wavelet Decomposition,
CVPR21(11084-11093)
IEEE DOI 2111
Analytical models, Neural networks, Estimation, Computer architecture, Wavelet analysis, Decoding, Pattern recognition BibRef

Li, M.Y.[Man-Yi], Zhang, H.[Hao],
D2IM-Net: Learning Detail Disentangled Implicit Fields from Single Images,
CVPR21(10241-10250)
IEEE DOI 2111
Surface reconstruction, Laplace equations, Shape, Decoding, Pattern recognition BibRef

Liu, F.[Feng], Tran, L.[Luan], Liu, X.M.[Xiao-Ming],
Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction,
CVPR21(7419-7429)
IEEE DOI 2111
Training, Solid modeling, Image segmentation, Shape, Fitting BibRef

Bechtold, J.[Jan], Tatarchenko, M.[Maxim], Fischer, V.[Volker], Brox, T.[Thomas],
Fostering Generalization in Single-view 3D Reconstruction by Learning a Hierarchy of Local and Global Shape Priors,
CVPR21(15875-15884)
IEEE DOI 2111
Training, Shape, Training data, Computer architecture, Network architecture BibRef

Yang, S.[Shuo], Xu, M.[Min], Xie, H.[Haozhe], Perry, S.[Stuart], Xia, J.[Jiahao],
Single-View 3D Object Reconstruction from Shape Priors in Memory,
CVPR21(3151-3160)
IEEE DOI 2111
Training, Visualization, Shape, Transforms, Writing, Feature extraction BibRef

Kluger, F.[Florian], Ackermann, H.[Hanno], Brachmann, E.[Eric], Yang, M.Y.[Michael Ying], Rosenhahn, B.[Bodo],
Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images,
CVPR21(13065-13074)
IEEE DOI 2111
Training, Measurement, Shape, Annotations, Fitting, Neural networks BibRef

Reading, C.[Cody], Harakeh, A.[Ali], Chae, J.[Julia], Waslander, S.L.[Steven L.],
Categorical Depth Distribution Network for Monocular 3D Object Detection,
CVPR21(8551-8560)
IEEE DOI 2111
Codes, Estimation, Object detection, Distribution networks, Detectors BibRef

Luo, S.[Shujie], Dai, H.[Hang], Shao, L.[Ling], Ding, Y.[Yong],
M3DSSD: Monocular 3D Single Stage Object Detector,
CVPR21(6141-6150)
IEEE DOI 2111
Solid modeling, Shape, Computational modeling, Object detection, Detectors BibRef

Lienen, J.[Julian], Hüllermeier, E.[Eyke], Ewerth, R.[Ralph], Nommensen, N.[Nils],
Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model,
CVPR21(14590-14599)
IEEE DOI 2111
Training, Neural networks, Estimation, Training data, Predictive models, Data models, Probability distribution BibRef

Pintore, G.[Giovanni], Agus, M.[Marco], Almansa, E.[Eva], Schneider, J.[Jens], Gobbetti, E.[Enrico],
SliceNet: deep dense depth estimation from a single indoor panorama using a slice-based representation,
CVPR21(11531-11540)
IEEE DOI 2111
Deep learning, Surface reconstruction, Estimation, Feature extraction, Indoor environment BibRef

Zhou, Y.[Yunsong], He, Y.[Yuan], Zhu, H.Z.[Hong-Zi], Wang, C.[Cheng], Li, H.Y.[Hong-Yang], Jiang, Q.[Qinhong],
Monocular 3D Object Detection: An Extrinsic Parameter Free Approach,
CVPR21(7552-7562)
IEEE DOI 2111
Perturbation methods, Roads, Computational modeling, Object detection, Detectors, Predictive models BibRef

Zhang, Y.P.[Yun-Peng], Lu, J.W.[Ji-Wen], Zhou, J.[Jie],
Objects are Different: Flexible Monocular 3D Object Detection,
CVPR21(3288-3297)
IEEE DOI 2111
Location awareness, Heating systems, Image edge detection, Estimation, Object detection, Benchmark testing BibRef

Yang, Z.P.[Zhen-Pei], Li, L.E.[Li Erran], Huang, Q.X.[Qi-Xing],
StruMonoNet: Structure-Aware Monocular 3D Prediction,
CVPR21(7409-7418)
IEEE DOI 2111
Visualization, Boosting, Pattern recognition BibRef

Ma, X.Z.[Xin-Zhu], Zhang, Y.[Yinmin], Xu, D.[Dan], Zhou, D.Z.[Dong-Zhan], Yi, S.[Shuai], Li, H.[Haojie], Ouyang, W.L.[Wan-Li],
Delving into Localization Errors for Monocular 3D Object Detection,
CVPR21(4719-4728)
IEEE DOI 2111
Location awareness, Training, Estimation, Object detection, Detectors BibRef

Miangoleh, S.M.H.[S. Mahdi H.], Dille, S.[Sebastian], Mai, L.[Long], Paris, S.[Sylvain], Aksoy, Y.[Yagiz],
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging,
CVPR21(9680-9689)
IEEE DOI 2111
Location awareness, Image segmentation, Image resolution, Merging, Neural networks, Estimation BibRef

Kumar, A.[Abhinav], Brazil, G.[Garrick], Liu, X.M.[Xiao-Ming],
GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection,
CVPR21(8969-8979)
IEEE DOI 2111
Training, Closed-form solutions, Pipelines, Object detection, Detectors, Benchmark testing BibRef

Watson, J.[Jamie], Aodha, O.M.[Oisin Mac], Prisacariu, V.[Victor], Brostow, G.[Gabriel], Firman, M.[Michael],
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth,
CVPR21(1164-1174)
IEEE DOI 2111
Training, Costs, Adaptive systems, Computational modeling, Estimation, Cameras BibRef

Weng, Z.Z.[Zhen-Zhen], Yeung, S.[Serena],
Holistic 3D Human and Scene Mesh Estimation from Single View Images,
CVPR21(334-343)
IEEE DOI 2111
Measurement, Solid modeling, Computational modeling, Pose estimation BibRef

Yin, W.[Wei], Zhang, J.M.[Jian-Ming], Wang, O.[Oliver], Niklaus, S.[Simon], Mai, L.[Long], Chen, S.[Simon], Shen, C.H.[Chun-Hua],
Learning to Recover 3D Scene Shape from a Single Image,
CVPR21(204-213)
IEEE DOI 2111
Training, Geometry, Shape, Estimation, Reconstruction algorithms, Predictive models BibRef

Hu, T.[Tao], Wang, L.[Liwei], Xu, X.G.[Xiao-Gang], Liu, S.[Shu], Jia, J.Y.[Jia-Ya],
Self-Supervised 3D Mesh Reconstruction from Single Images,
CVPR21(5998-6007)
IEEE DOI 2111
Solid modeling, Annotations, Shape, Motorcycles, Reconstruction algorithms, Pattern recognition BibRef

Tang, Y.L.[Yun-Lei], Dorn, S.[Sebastian], Savani, C.[Chiragkumar],
Center3D: Center-Based Monocular 3D Object Detection with Joint Depth Understanding,
GCPR20(289-302).
Springer DOI 2110
BibRef

Buquet, J.[Julie], Zhang, J.S.[Jin-Song], Roulet, P.[Patrice], Thibault, S.[Simon], Lalonde, J.F.[Jean-François],
Evaluating the Impact of Wide-Angle Lens Distortion on Learning-based Depth Estimation,
OmniCV21(3688-3696)
IEEE DOI 2109
Training, Nonlinear distortion, Neural networks, Estimation, Tools, Cameras BibRef

Wang, Y.R.[Yi-Ran], Li, X.Y.[Xing-Yi], Shi, M.[Min], Xian, K.[Ke], Cao, Z.G.[Zhi-Guo],
Knowledge Distillation for Fast and Accurate Monocular Depth Estimation on Mobile Devices,
MAI21(2457-2465)
IEEE DOI 2109
Knowledge engineering, Performance evaluation, Training, Visualization, Neural networks, Estimation BibRef

Yücel, M.K.[Mehmet Kerim], Dimaridou, V.[Valia], Drosou, A.[Anastasios], Saà-Garriga, A.[Albert],
Real-time Monocular Depth Estimation with Sparse Supervision on Mobile,
MAI21(2428-2437)
IEEE DOI 2109
Runtime, Computational modeling, Graphics processing units, Estimation, Predictive models, Mobile handsets, Real-time systems BibRef

Ignatov, A.[Andrey], Malivenko, G.[Grigory], Plowman, D.[David], Shukla, S.[Samarth], Timofte, R.[Radu], Zhang, Z.[Ziyu], Wang, Y.[Yicheng], Huang, Z.L.[Zi-Long], Luo, G.Z.[Guo-Zhong], Yu, G.[Gang], Fu, B.[Bin], Wang, Y.R.[Yi-Ran], Li, X.Y.[Xing-Yi], Shi, M.[Min], Xian, K.[Ke], Cao, Z.G.[Zhi-Guo], Du, J.H.[Jin-Hua], Wu, P.L.[Pei-Lin], Ge, C.[Chao], Yao, J.Y.[Jiao-Yang], Tu, F.[Fangwen], Li, B.[Bo], Yoo, J.E.[Jung Eun], Seo, K.[Kwanggyoon], Xu, J.[Jialei], Li, Z.Y.[Zhen-Yu], Liu, X.[Xianming], Jiang, J.J.[Jun-Jun], Chen, W.C.[Wei-Chi], Joya, S.[Shayan], Fan, H.[Huanhuan], Kang, Z.[Zhaobing], Li, A.[Ang], Feng, T.[Tianpeng], Liu, Y.[Yang], Sheng, C.[Chuannan], Yin, J.[Jian], Benavides, F.T.[Fausto T.],
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report,
MAI21(2545-2557)
IEEE DOI 2109
Meters, Image resolution, Estimation, Real-time systems, Pattern recognition BibRef

Leroy, R., Trouvé-Peloux, P., Champagnat, F., Le Saux, B., Carvalho, M.,
Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal Transport,
MVA21(1-5)
DOI Link 2109
Training, Measurement, Neural networks, Estimation, Prediction methods BibRef

Niu, Z.F.[Zhao-Feng], Fujimoto, Y.[Yuichiro], Kanbara, M.[Masayuki], Kato, H.[Hirokazu],
HMA-Depth: A New Monocular Depth Estimation Model Using Hierarchical Multi-Scale Attention,
MVA21(1-5)
DOI Link 2109
Semantics, Estimation, Object segmentation, Network architecture, Task analysis BibRef

Chen, Z.W.[Zi-Wen], Guo, Z.X.[Zi-Xuan], Weinman, J.[Jerod],
Improved Point Transformation Methods For Self-Supervised Depth Prediction,
CRV21(111-118)
IEEE DOI 2108
Learn using stereo pairs. Training, Machine learning algorithms, Estimation, Machine learning, Predictive models, Network architecture, neural networks BibRef

Saeedan, F.[Faraz], Roth, S.[Stefan],
Boosting Monocular Depth with Panoptic Segmentation Maps,
WACV21(3852-3861)
IEEE DOI 2106
Training, Image segmentation, Smoothing methods, Semantics, Estimation BibRef

Kuznietsov, Y.[Yevhen], Proesmans, M.[Marc], Van Gool, L.J.[Luc J.],
CoMoDA: Continuous Monocular Depth Adaptation Using Past Experiences,
WACV21(2906-2916)
IEEE DOI 2106
Training, Adaptation models, Optical buffering, Pipelines, Estimation, Optimized production technology, Streaming media BibRef

Lu, Y.[Yawen], Lu, G.[Guoyu],
An Alternative of LiDAR in Nighttime: Unsupervised Depth Estimation Based on Single Thermal Image,
WACV21(3832-3842)
IEEE DOI 2106
Image sensors, Laser radar, Image resolution, Image color analysis, Lasers, Estimation, Lighting BibRef

Domnik, M.[Matthias], Proenca, P.[Pedro], Delaune, J.[Jeff], Thiem, J.[Jörg], Brockers, R.[Roland],
Dense 3D-Reconstruction from Monocular Image Sequences for Computationally Constrained UAS*,
WACV21(1819-1827)
IEEE DOI 2106
Structure from motion, Computational modeling, Cameras, Real-time systems, Hardware, Computational efficiency BibRef

Bae, K.[Kyuho], Ivan, A.[Andre], Nagahara, H.[Hajime], Park, I.K.[In Kyu],
5D Light Field Synthesis from a Monocular Video,
ICPR21(7157-7164)
IEEE DOI 2105
Learning systems, Deep learning, Feature extraction, Cameras, Light fields, Image reconstruction BibRef

Phan, M.H.[Minh Hieu], Phung, S.L.[Son Lam], Bouzerdoum, A.[Abdesselam],
Ordinal Depth Classification Using Region-based Self-attention,
ICPR21(3620-3627)
IEEE DOI 2105
Estimation, Channel estimation, Feature extraction, Classification algorithms, Reliability, Task analysis BibRef

Kim, D.[Doyeon], Joo, D.G.[Dong-Gyu], Kim, J.[Junmo],
Delivering Meaningful Representation for Monocular Depth Estimation,
ICPR21(7790-7795)
IEEE DOI 2105
Image analysis, Fuses, Estimation, Network architecture, Performance gain, Feature extraction BibRef

Li, Z.[Zhi], Zhu, X.Y.[Xiao-Yang], Yu, H.T.[Hai-Tao], Zhang, Q.[Qi], Jiang, Y.[Yongshi],
Edge-Aware Monocular Dense Depth Estimation with Morphology,
ICPR21(2935-2942)
IEEE DOI 2105
Simultaneous localization and mapping, Image edge detection BibRef

Zhou, D.F.[Ding-Fu], Song, X.[Xibin], Dai, Y.C.[Yu-Chao], Yin, J.[Junbo], Lu, F.X.[Fei-Xiang], Liao, M.[Miao], Fang, J.[Jin], Zhang, L.J.[Liang-Jun],
IAFA: Instance-Aware Feature Aggregation for 3d Object Detection from a Single Image,
ACCV20(I:417-435).
Springer DOI 2103
BibRef

Irie, G.[Go], Ikami, D.[Daiki], Kawanishi, T.[Takahito], Kashino, K.[Kunio],
Cascaded Transposed Long-range Convolutions for Monocular Depth Estimation,
ACCV20(III:437-453).
Springer DOI 2103
BibRef

Cheng, C.C.[Chia-Chun], Lai, S.H.[Shang-Hong],
3d Object Detection from Consecutive Monocular Images,
ACCV20(I:453-468).
Springer DOI 2103
BibRef

Wang, L.[Li], Du, L.[Liang], Ye, X.Q.[Xiao-Qing], Fu, Y.W.[Yan-Wei], Guo, G.D.[Guo-Dong], Xue, X.Y.[Xiang-Yang], Feng, J.F.[Jian-Feng], Zhang, L.[Li],
Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection,
CVPR21(454-463)
IEEE DOI 2111
Solid modeling, Image resolution, Image coding, Object detection, Feature extraction, Information filters BibRef

Ouyang, E.[Erli], Zhang, L.[Li], Chen, M.[Mohan], Arnab, A.[Anurag], Fu, Y.W.[Yan-Wei],
Dynamic Depth Fusion and Transformation for Monocular 3d Object Detection,
ACCV20(I:349-364).
Springer DOI 2103
BibRef

Sun, S.C.[Shi-Chen], Zhu, Z.B.[Zheng-Bang], Dai, X.W.[Xiao-Wei], Zhao, Q.J.[Qi-Jun], Li, J.[Jing],
Weakly-supervised Reconstruction of 3d Objects with Large Shape Variation from Single In-the-wild Images,
ACCV20(I:3-19).
Springer DOI 2103
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Peng, K.S.[Kuo-Shiuan], Ditzler, G.[Gregory], Rozenblit, J.[Jerzy],
A Light-weight Monocular Depth Estimation with Edge-guided Occlusion Fading Reduction,
ISVC20(II:69-81).
Springer DOI 2103
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Hidalgo-Carrió, J., Gehrig, D., Scaramuzza, D.,
Learning Monocular Dense Depth from Events,
3DV20(534-542)
IEEE DOI 2102
Cameras, Estimation, Sensors, Standards, Measurement, Robot vision systems, Training, deep learning, depth BibRef

Wang, Y., Luo, L., Shen, X., Mei, X.,
DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues,
3DV20(514-523)
IEEE DOI 2102
Videos, Estimation, Image edge detection, Training, Optical imaging, Reliability BibRef

Du, D., Zhang, Z., Han, X., Cui, S., Liu, L.,
VIPNet: A Fast and Accurate Single-View Volumetric Reconstruction by Learning Sparse Implicit Point Guidance,
3DV20(553-562)
IEEE DOI 2102
Shape, Image reconstruction, Topology, Network topology, Decoding, hybrid shape learning BibRef

Li, H., Ye, W., Zhang, G., Zhang, S., Bao, H.,
Saliency Guided Subdivision for Single-View Mesh Reconstruction,
3DV20(1098-1107)
IEEE DOI 2102
Strain, Faces, Shape, Surface reconstruction, Memory management, Feature extraction BibRef

Chen, Z., Wu, B., Liu, W.C.,
Deep Learning for 3d Reconstruction of the Martian Surface Using Monocular Images: A First Glance,
ISPRS20(B3:1111-1116).
DOI Link 2012
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Han, Z.Z.[Zhi-Zhong], Qiao, G.H.[Guan-Hui], Liu, Y.S.[Yu-Shen], Zwicker, M.[Matthias],
SeqXY2SeqZ: Structure Learning for 3d Shapes by Sequentially Predicting 1D Occupancy Segments from 2d Coordinates,
ECCV20(XXIV:607-625).
Springer DOI 2012
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Badger, M.[Marc], Wang, Y.[Yufu], Modh, A.[Adarsh], Perkes, A.[Ammon], Kolotouros, N.[Nikos], Pfrommer, B.G.[Bernd G.], Schmidt, M.F.[Marc F.], Daniilidis, K.[Kostas],
3d Bird Reconstruction: A Dataset, Model, and Shape Recovery from a Single View,
ECCV20(XVIII:1-17).
Springer DOI 2012
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Wang, J.R.[Jian-Ren], Fang, Z.Y.[Zhao-Yuan],
GSIR: Generalizable 3d Shape Interpretation and Reconstruction,
ECCV20(XIII:498-514).
Springer DOI 2011
Jointly learn 3D shape interpretation and reconstruction. BibRef

Tiwari, L.[Lokender], Ji, P.[Pan], Tran, Q.H.[Quoc-Huy], Zhuang, B.B.[Bing-Bing], Anand, S.[Saket], Chandraker, M.[Manmohan],
Pseudo RGB-D for Self-improving Monocular SLAM and Depth Prediction,
ECCV20(XI:437-455).
Springer DOI 2011
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Aleotti, F.[Filippo], Tosi, F.[Fabio], Zhang, L.[Li], Poggi, M.[Matteo], Mattoccia, S.[Stefano],
Reversing the Cycle: Self-supervised Deep Stereo Through Enhanced Monocular Distillation,
ECCV20(XI:614-632).
Springer DOI 2011
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Michalkiewicz, M.[Mateusz], Parisot, S.[Sarah], Tsogkas, S.[Stavros], Baktashmotlagh, M.[Mahsa], Eriksson, A.[Anders], Belilovsky, E.[Eugene],
Few-shot Single-view 3-d Object Reconstruction with Compositional Priors,
ECCV20(XXV:614-630).
Springer DOI 2011
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Simonelli, A.[Andrea], Buló, S.R.[Samuel Rota], Porzi, L.[Lorenzo], Ricci, E.[Elisa], Kontschieder, P.[Peter],
Towards Generalization Across Depth for Monocular 3d Object Detection,
ECCV20(XXII:767-782).
Springer DOI 2011
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Goel, S.[Shubham], Kanazawa, A.[Angjoo], Malik, J.[Jitendra],
Shape and Viewpoint Without Keypoints,
ECCV20(XV:88-104).
Springer DOI 2011
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Liu, L.J.[Li-Jie], Wu, C.[Chufan], Lu, J.W.[Ji-Wen], Xie, L.X.[Ling-Xi], Zhou, J.[Jie], Tian, Q.[Qi],
Reinforced Axial Refinement Network for Monocular 3d Object Detection,
ECCV20(XVII:540-556).
Springer DOI 2011
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Denninger, M.[Maximilian], Triebel, R.[Rudolph],
3d Scene Reconstruction from a Single Viewport,
ECCV20(XXII:51-67).
Springer DOI 2011
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Swami, K., Bondada, P.V., Bajpai, P.K.,
ACED: Accurate And Edge-Consistent Monocular Depth Estimation,
ICIP20(1376-1380)
IEEE DOI 2011
Estimation, Training, Computational modeling, Convolution, Task analysis, Cameras, Machine learning, deep learning BibRef

Wang, Z., Isler, V., Lee, D.D.,
Surface Hof: Surface Reconstruction From A Single Image Using Higher Order Function Networks,
ICIP20(2666-2670)
IEEE DOI 2011
Surface reconstruction, Image reconstruction, Surface treatment, Decoding, Task analysis, Computer Graphics BibRef

Chen, T.[Tian], An, S.J.[Shi-Jie], Zhang, Y.[Yuan], Ma, C.Y.[Chong-Yang], Wang, H.Y.[Hua-Yan], Guo, X.Y.[Xiao-Yan], Zheng, W.[Wen],
Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets,
ECCV20(XIV:90-108).
Springer DOI 2011
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Huynh, L.[Lam], Nguyen-Ha, P.[Phong], Matas, J.[Jiri], Rahtu, E.[Esa], Heikkilä, J.[Janne],
Guiding Monocular Depth Estimation Using Depth-attention Volume,
ECCV20(XXVI:581-597).
Springer DOI 2011
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Li, X.T.[Xue-Ting], Liu, S.F.[Si-Fei], Kim, K.H.[Ki-Hwan], de Mello, S.[Shalini], Jampani, V.[Varun], Yang, M.H.[Ming-Hsuan], Kautz, J.[Jan],
Self-supervised Single-view 3d Reconstruction via Semantic Consistency,
ECCV20(XIV:677-693).
Springer DOI 2011
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Klingner, M.[Marvin], Termöhlen, J.A.[Jan-Aike], Mikolajczyk, J.[Jonas], Fingscheidt, T.[Tim],
Self-supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance,
ECCV20(XX:582-600).
Springer DOI 2011
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Kniaz, V.V.[Vladimir V.], Knyaz, V.A.[Vladimir A.], Remondino, F.[Fabio], Bordodymov, A.[Artem], Moshkantsev, P.[Petr],
Image-to-voxel Model Translation for 3d Scene Reconstruction and Segmentation,
ECCV20(VII:105-124).
Springer DOI 2011
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Li, Y.C.[Yi-Chen], Mo, K.C.[Kai-Chun], Shao, L.[Lin], Sung, M.[Minhyuk], Guibas, L.J.[Leonidas J.],
Learning 3d Part Assembly from a Single Image,
ECCV20(VI:664-682).
Springer DOI 2011
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Ye, X.Q.[Xiao-Qing], Du, L.[Liang], Shi, Y.F.[Yi-Feng], Li, Y.Y.[Ying-Ying], Tan, X.[Xiao], Feng, J.F.[Jian-Feng], Ding, E.[Errui], Wen, S.L.[Shi-Lei],
Monocular 3d Object Detection via Feature Domain Adaptation,
ECCV20(IX:17-34).
Springer DOI 2011
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Xu, Y.F.[Yi-Fan], Fan, T.Q.[Tian-Qi], Yuan, Y.[Yi], Singh, G.[Gurprit],
Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3d Reconstruction with Symmetry,
ECCV20(I:248-263).
Springer DOI 2011
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Wang, L.J.[Li-Jun], Zhang, J.M.[Jian-Ming], Wang, Y.[Yifan], Lu, H.C.[Hu-Chuan], Ruan, X.[Xiang],
Cliffnet for Monocular Depth Estimation with Hierarchical Embedding Loss,
ECCV20(V:316-331).
Springer DOI 2011
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Hampali, S.[Shreyas], Stekovic, S.[Sinisa], Sarkar, S.D.[Sayan Deb], Kumar, C.S.[Chetan S.], Fraundorfer, F.[Friedrich], Lepetit, V.[Vincent],
Monte Carlo Scene Search for 3D Scene Understanding,
CVPR21(13799-13808)
IEEE DOI 2111
Monte Carlo methods, Layout, Training data, Search problems, Rendering (computer graphics), Space exploration BibRef

Stekovic, S.[Sinisa], Hampali, S.[Shreyas], Rad, M.[Mahdi], Sarkar, S.D.[Sayan Deb], Fraundorfer, F.[Friedrich], Lepetit, V.[Vincent],
General 3d Room Layout from a Single View by Render-and-compare,
ECCV20(XVI: 187-203).
Springer DOI 2010
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Shi, X.P.[Xue-Peng], Chen, Z.X.[Zhi-Xiang], Kim, T.K.[Tae-Kyun],
Distance-normalized Unified Representation for Monocular 3d Object Detection,
ECCV20(XXIX: 91-107).
Springer DOI 2010
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Zeng, W.[Wei], Karaoglu, S.[Sezer], Gevers, T.[Theo],
Joint 3d Layout and Depth Prediction from a Single Indoor Panorama Image,
ECCV20(XVI: 666-682).
Springer DOI 2010
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Toft, C.[Carl], Turmukhambetov, D.[Daniyar], Sattler, T.[Torsten], Kahl, F.[Fredrik], Brostow, G.J.[Gabriel J.],
Single-image Depth Prediction Makes Feature Matching Easier,
ECCV20(XVI: 473-492).
Springer DOI 2010
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Wu, H.T.[Hong-Tao], Meng, Y.[Ying], Niu, B.Q.[Bing-Qing],
A Novel 3D Surface Reconstruction Method with Posterior Constraints of Edge Detection,
ICIVC20(55-58)
IEEE DOI 2009
Image reconstruction, Calibration, Feature extraction, Image edge detection, Digital cameras, Feature point matching BibRef

Peluso, V., Cipolletta, A., Calimera, A., Poggi, M., Tosi, F., Aleotti, F., Mattoccia, S.,
Enabling monocular depth perception at the very edge,
LPCV20(1581-1583)
IEEE DOI 2008
Estimation, Monitoring, Microcontrollers, Computer architecture, Pattern recognition BibRef

Ren, H., El-Khamy, M., Lee, J.,
Stereo Disparity Estimation via Joint Supervised, Unsupervised, and Weakly Supervised Learning,
ICIP20(2760-2764)
IEEE DOI 2011
Supervised learning, Estimation, Feature extraction, Unsupervised learning, Loss measurement, Error analysis, Training, weakly supervised learning BibRef

Ren, H., Raj, A., El-Khamy, M., Lee, J.,
SUW-Learn: Joint Supervised, Unsupervised, Weakly Supervised Deep Learning for Monocular Depth Estimation,
DeepVision20(3235-3243)
IEEE DOI 2008
Estimation, Supervised learning, Training, Unsupervised learning, Semantics BibRef

Liu, Z., Wu, Z., Tóth, R.,
SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation,
AutoDrive20(4289-4298)
IEEE DOI 2008
Object detection, Proposals, Cameras, Feature extraction, Laser radar BibRef

Ramamonjisoa, M., Du, Y., Lepetit, V.,
Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields,
CVPR20(14636-14645)
IEEE DOI 2008
Image reconstruction, Estimation, Machine learning, Training, Task analysis, Manuals BibRef

Spencer, J., Bowden, R., Hadfield, S.,
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning,
CVPR20(14390-14401)
IEEE DOI 2008
Estimation, Robustness, Task analysis, Training, Feature extraction, Decoding, Meteorology BibRef

Chen, Y., Tai, L., Sun, K., Li, M.,
MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships,
CVPR20(12090-12099)
IEEE DOI 2008
Object detection, Uncertainty, Cameras, Feature extraction, Autonomous vehicles BibRef

Ding, M.Y.[Ming-Yu], Huo, Y.Q.[Yu-Qi], Yi, H.W.[Hong-Wei], Wang, Z.[Zhe], Shi, J.P.[Jian-Ping], Lu, Z.W.[Zhi-Wu], Luo, P.[Ping],
Learning Depth-Guided Convolutions for Monocular 3D Object Detection,
CVPR20(11669-11678)
IEEE DOI 2008
BibRef
And: AutoDrive20(4306-4315)
IEEE DOI 2008
Kernel, Feature extraction, Object detection, Laser radar, Convolutional codes BibRef

Johnston, A., Carneiro, G.,
Self-Supervised Monocular Trained Depth Estimation Using Self-Attention and Discrete Disparity Volume,
CVPR20(4755-4764)
IEEE DOI 2008
Estimation, Uncertainty, Videos, Training, Computational modeling, Convolution, Cameras BibRef

Wang, F., Yeh, Y., Sun, M., Chiu, W., Tsai, Y.,
BiFuse: Monocular 360 Depth Estimation via Bi-Projection Fusion,
CVPR20(459-468)
IEEE DOI 2008
Face, Estimation, Cameras, Distortion, Convolution, Neural networks BibRef

Guizilini, V., Ambru?, R., Pillai, S., Raventos, A., Gaidon, A.,
3D Packing for Self-Supervised Monocular Depth Estimation,
CVPR20(2482-2491)
IEEE DOI 2008
Estimation, Training, Image resolution, Cameras, Laser radar, Task analysis BibRef

Henzler, P., Mitra, N.J., Ritschel, T.,
Learning a Neural 3D Texture Space From 2D Exemplars,
CVPR20(8353-8361)
IEEE DOI 2008
Stochastic processes, Interpolation, Decoding, Graphics, Training BibRef

Xia, Z., Sullivan, P., Chakrabarti, A.,
Generating and Exploiting Probabilistic Monocular Depth Estimates,
CVPR20(62-71)
IEEE DOI 2008
Task analysis, Color, Estimation, Probabilistic logic, Training, Probability distribution, Sensors BibRef

Watson, J.[Jamie], Firman, M.[Michael], Monszpart, A.[Aron], Brostow, G.J.[Gabriel J.],
Footprints and Free Space From a Single Color Image,
CVPR20(11-20)
IEEE DOI 2008
Geometry, Cameras, Image segmentation, Color, Task analysis, Robot vision systems BibRef

Nie, Y., Han, X., Guo, S., Zheng, Y., Chang, J., Zhang, J.J.,
Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes From a Single Image,
CVPR20(52-61)
IEEE DOI 2008
Image reconstruction, Layout, Shape, Cameras, Object detection, Topology BibRef

Yao, Y., Schertler, N., Rosales, E., Rhodin, H., Sigal, L., Sheffer, A.,
Front2Back: Single View 3D Shape Reconstruction via Front to Back Prediction,
CVPR20(528-537)
IEEE DOI 2008
Surface reconstruction, Image reconstruction, Shape, Geometry, Task analysis BibRef

Wang, L., Zhang, J., Wang, O., Lin, Z., Lu, H.,
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation,
CVPR20(538-547)
IEEE DOI 2008
Semantics, Estimation, Image segmentation, Decoding, Feature extraction, Predictive models, Task analysis BibRef

Smirnov, D., Fisher, M., Kim, V.G., Zhang, R., Solomon, J.,
Deep Parametric Shape Predictions Using Distance Fields,
CVPR20(558-567)
IEEE DOI 2008
Shape, Task analysis, Geometry, Loss measurement, Machine learning BibRef

Baradad, M., Torralba, A.,
Height and Uprightness Invariance for 3D Prediction From a Single View,
CVPR20(488-497)
IEEE DOI 2008
Cameras, Training, Semantics, Task analysis, Measurement, Solid modeling BibRef

Paschalidou, D., Van Gool, L.J., Geiger, A.,
Learning Unsupervised Hierarchical Part Decomposition of 3D Objects From a Single RGB Image,
CVPR20(1057-1067)
IEEE DOI 2008
Shape, Geometry, Image reconstruction, Neural networks, Solid modeling BibRef

Chen, W., Qian, S., Fan, D., Kojima, N., Hamilton, M., Deng, J.,
OASIS: A Large-Scale Dataset for Single Image 3D in the Wild,
CVPR20(676-685)
IEEE DOI 2008
Surface reconstruction, Image reconstruction, Task analysis, Shape, Geometry, Face BibRef

Xian, K., Zhang, J., Wang, O., Mai, L., Lin, Z., Cao, Z.,
Structure-Guided Ranking Loss for Single Image Depth Prediction,
CVPR20(608-617)
IEEE DOI 2008
Image edge detection, Training, Sensors, Task analysis, Videos, Measurement BibRef

Yang, H., Carlone, L.,
In Perfect Shape: Certifiably Optimal 3D Shape Reconstruction From 2D Landmarks,
CVPR20(618-627)
IEEE DOI 2008
Shape, Image reconstruction, Optimization, Solid modeling, Robustness BibRef

Li, Z., Shafiei, M., Ramamoorthi, R., Sunkavalli, K., Chandraker, M.,
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image,
CVPR20(2472-2481)
IEEE DOI 2008
Lighting, Rendering (computer graphics), Geometry, Training, Shape, Image reconstruction, Task analysis BibRef

Li, Y., Mao, J., Zhang, X., Freeman, W.T., Tenenbaum, J.B., Wu, J.,
Perspective Plane Program Induction From a Single Image,
CVPR20(4433-4442)
IEEE DOI 2008
Cameras, Graphics, Task analysis, Lattices, Inference algorithms BibRef

Chen, Q., Nguyen, V., Han, F., Kiveris, R., Tu, Z.,
Topology-Aware Single-Image 3D Shape Reconstruction,
L3DGM20(1089-1097)
IEEE DOI 2008
Shape, Image reconstruction, Decoding, Topology, Training BibRef

Pokale, A., Aggarwal, A., Jatavallabhula, K.M., Krishna, K.M.[K. Madhava],
Reconstruct, Rasterize and Backprop: Dense shape and pose estimation from a single image,
VisualSLAM20(179-186)
IEEE DOI 2008
Shape, Image reconstruction, Cameras, Task analysis, Simultaneous localization and mapping, Neural networks BibRef

Xu, S., Yang, J., Chen, D., Wen, F., Deng, Y., Jia, Y., Tong, X.,
Deep 3D Portrait From a Single Image,
CVPR20(7707-7717)
IEEE DOI 2008
Face, Geometry, Image reconstruction, Hair BibRef

Wiles, O., Gkioxari, G., Szeliski, R., Johnson, J.,
SynSin: End-to-End View Synthesis From a Single Image,
CVPR20(7465-7475)
IEEE DOI 2008
Semantics, Task analysis, Image resolution, Training, Solid modeling, Rendering (computer graphics) BibRef

Onizuka, H., Hayirci, Z., Thomas, D., Sugimoto, A., Uchiyama, H., Taniguchi, R.,
TetraTSDF: 3D Human Reconstruction From a Single Image With a Tetrahedral Outer Shell,
CVPR20(6010-6019)
IEEE DOI 2008
Shape, Solid modeling, Image reconstruction, Biological system modeling, Task analysis BibRef

Schurischuster, S.[Stefan], Loaiciga R, J.M.[Jorge Mario], Kurtic, A.[Andrija], Sablatnig, R.[Robert],
In-Time 3D Reconstruction and Instance Segmentation from Monocular Sensor Data,
CRV20(142-149)
IEEE DOI 2006
BibRef

Mani, K., Daga, S., Garg, S., Shankar, N.S., Murthy, J.K.[J. Krishna], Krishna, K.M.,
Mono Lay out: Amodal scene layout from a single image,
WACV20(1678-1686)
IEEE DOI 2006
Layout, Roads, Vehicle dynamics, Estimation, Decoding, Feature extraction, Task analysis BibRef

Nguyen, D.[Duc], Choi, S.[Seonghwa], Kim, W.[Woojae], Lee, S.H.[Sang-Hoon],
GraphX-Convolution for Point Cloud Deformation in 2D-to-3D Conversion,
ICCV19(8627-8636)
IEEE DOI 2004
Reconstruct point cloud from single image. feature extraction, graph theory, image reconstruction, solid modelling, feature blending, Computational modeling BibRef

Brazil, G., Liu, X.,
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection,
ICCV19(9286-9295)
IEEE DOI 2004
feature extraction, image colour analysis, object detection, parameter estimation, stereo image processing, Laser radar BibRef

Ye, Y.[Yufei], Tulsiani, S.[Shubham], Gupta, A.[Abhinav],
Shelf-Supervised Mesh Prediction in the Wild,
CVPR21(8839-8848)
IEEE DOI 2111
Visualization, Shape, Scalability, Lighting, Predictive models, Rendering (computer graphics) BibRef

Kulkarni, N.[Nilesh], Misra, I.[Ishan], Tulsiani, S.[Shubham], Gupta, A.[Abhinav],
3D-RelNet: Joint Object and Relational Network for 3D Prediction,
ICCV19(2212-2221)
IEEE DOI 2004
learning (artificial intelligence), pose estimation, solid modelling, relational network, independent predictions, BibRef

Zhao, Y., Kong, S., Shin, D., Fowlkes, C.C.,
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation,
CVPR20(3327-3337)
IEEE DOI 2008
Training, Task analysis, Training data, Predictive models, Adaptation models, Data models, Clutter BibRef

Cha, G., Lee, M., Oh, S.,
Unsupervised 3D Reconstruction Networks,
ICCV19(3848-3857)
IEEE DOI 2004
cameras, feature extraction, image motion analysis, image reconstruction, pose estimation, shape recognition, Structure from motion BibRef

Mo, K.C.[Kai-Chun], Wang, H.[He], Yan, X.C.[Xin-Chen], Guibas, L.J.[Leonidas J.],
Pt2pc: Learning to Generate 3d Point Cloud Shapes from Part Tree Conditions,
ECCV20(VI:683-701).
Springer DOI 2011
BibRef

Huang, J., Zhou, Y., Funkhouser, T., Guibas, L.J.,
FrameNet: Learning Local Canonical Frames of 3D Surfaces From a Single RGB Image,
ICCV19(8637-8646)
IEEE DOI 2004
augmented reality, computational geometry, computer graphics, feature extraction, geometry, image colour analysis, Robustness BibRef

Gadelha, M.[Matheus], RoyChowdhury, A.[Aruni], Sharma, G.[Gopal], Kalogerakis, E.[Evangelos], Cao, L.L.[Liang-Liang], Learned-Miller, E.[Erik], Wang, R.[Rui], Maji, S.[Subhransu],
Label-efficient Learning on Point Clouds Using Approximate Convex Decompositions,
ECCV20(X:473-491).
Springer DOI 2011
BibRef

Gadelha, M., Wang, R., Maji, S.,
Shape Reconstruction Using Differentiable Projections and Deep Priors,
ICCV19(22-30)
IEEE DOI 2004
gradient methods, image reconstruction, noisy projections, viewpoint uncertainities, shape given measurements, Bayes methods BibRef

Li, L.[Lin], Khan, S.[Salman], Barnes, N.[Nick],
Geometry to the Rescue: 3D Instance Reconstruction from a Cluttered Scene,
L3DGM20(1098-1104)
IEEE DOI 2008
BibRef
Earlier:
Silhouette-Assisted 3D Object Instance Reconstruction from a Cluttered Scene,
3D-Wild19(2080-2088)
IEEE DOI 2004
Surface reconstruction, Shape, Estimation, Image reconstruction, Training. image colour analysis, object detection, shape recognition, perspective projection BibRef

Yan, D., Morimitsu, H., Gao, S., Ji, X.,
Monocular Piecewise Depth Estimation in Dynamic Scenes by Exploiting Superpixel Relations,
ICCV19(4362-4371)
IEEE DOI 2004
image matching, image motion analysis, image reconstruction, image segmentation, image sequences, object detection, BibRef

Chang, J., Wetzstein, G.,
Deep Optics for Monocular Depth Estimation and 3D Object Detection,
ICCV19(10192-10201)
IEEE DOI 2004
image capture, image coding, neural nets, object detection, optimisation, stereo image processing, deep optics, Object detection BibRef

Ramamonjisoa, M., Lepetit, V.,
SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation,
3D-Wild19(2109-2118)
IEEE DOI 2004
augmented reality, cameras, image colour analysis, image reconstruction, object recognition, Surface Normal Estimation BibRef

Ramon, E., Ruiz, G., Batard, T., Giró-i-Nieto, X.,
Hyperparameter-Free Losses for Model-Based Monocular Reconstruction,
GMDL19(4075-4084)
IEEE DOI 2004
cameras, computational complexity, computational geometry, image reconstruction, minimisation, pose estimation, deep learning BibRef

Simonelli, A., Bulò, S.R., Porzi, L., Lopez-Antequera, M., Kontschieder, P.,
Disentangling Monocular 3D Object Detection,
ICCV19(1991-1999)
IEEE DOI 2004
image colour analysis, interpolation, learning (artificial intelligence), object detection, Laser radar BibRef

Watson, J., Firman, M., Brostow, G., Turmukhambetov, D.,
Self-Supervised Monocular Depth Hints,
ICCV19(2162-2171)
IEEE DOI 2004
regression analysis, stereo image processing, supervised learning, monocular depth estimators, Laser radar BibRef

Issaranon, T., Zou, C., Forsyth, D.,
Counterfactual Depth from a Single RGB Image,
3D-Wild19(2129-2138)
IEEE DOI 2004
decoding, geometry, image coding, image colour analysis, image reconstruction, image representation, image resolution, Object Removal BibRef

van Dijk, T., de Croon, G.,
How Do Neural Networks See Depth in Single Images?,
ICCV19(2183-2191)
IEEE DOI 2004
cameras, image processing, neural nets, deep neural networks, depth estimation, camera pitch, vertical image position, Cameras, Biological neural networks BibRef

Wallace, B., Hariharan, B.,
Few-Shot Generalization for Single-Image 3D Reconstruction via Priors,
ICCV19(3817-3826)
IEEE DOI 2004
image classification, image reconstruction, stereo image processing, few-shot generalization, Generators BibRef

Godard, C., Aodha, O.M., Firman, M., Brostow, G.,
Digging Into Self-Supervised Monocular Depth Estimation,
ICCV19(3827-3837)
IEEE DOI 2004
distance measurement, image classification, image motion analysis, image reconstruction, image sampling, Image matching BibRef

Zhu, J., Fang, Y.,
Learning Object-Specific Distance From a Monocular Image,
ICCV19(3838-3847)
IEEE DOI 2004
learning (artificial intelligence), object detection, inverse perspective mapping algorithm, Training BibRef

Dhamo, H., Navab, N., Tombari, F.,
Object-Driven Multi-Layer Scene Decomposition From a Single Image,
ICCV19(5368-5377)
IEEE DOI 2004
image colour analysis, image reconstruction, image representation, image resolution, Image color analysis BibRef

Zhang, Y.[Yinda], Wadhwa, N.[Neal], Orts-Escolano, S.[Sergio], Häne, C.[Christian], Fanello, S.[Sean], Garg, R.[Rahul],
Du2net: Learning Depth Estimation from Dual-cameras and Dual-pixels,
ECCV20(I:582-598).
Springer DOI 2011
BibRef

Garg, R.[Rahul], Wadhwa, N.[Neal], Ansari, S., Barron, J.,
Learning Single Camera Depth Estimation Using Dual-Pixels,
ICCV19(7627-7636)
IEEE DOI 2004
cameras, image colour analysis, image matching, image sensors, stereo image processing, supervised learning, Training BibRef

Pinheiro, P.O., Rostamzadeh, N., Ahn, S.,
Domain-Adaptive Single-View 3D Reconstruction,
ICCV19(7637-7646)
IEEE DOI 2004
image reconstruction, image representation, learning (artificial intelligence), Decoding BibRef

Ho, C.H.[Chih-Hui], Morgado, P.[Pedro], Persekian, A.[Amir], Vasconcelos, N.M.[Nuno M.],
PIEs: Pose Invariant Embeddings,
CVPR19(12369-12378).
IEEE DOI 2002
BibRef

Meng, Y.[Yue], Lu, Y.X.[Yong-Xi], Raj, A.[Aman], Sunarjo, S.[Samuel], Guo, R.[Rui], Javidi, T.[Tara], Bansal, G.[Gaurav], Bharadia, D.[Dinesh],
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception,
CVPR19(9802-9812).
IEEE DOI 2002
BibRef

Manhardt, F.[Fabian], Kehl, W.[Wadim], Gaidon, A.[Adrien],
ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape,
CVPR19(2064-2073).
IEEE DOI 2002
BibRef

Weng, C.Y.[Chung-Yi], Curless, B.[Brian], Kemelmacher-Shlizerman, I.[Ira],
Photo Wake-Up: 3D Character Animation From a Single Photo,
CVPR19(5901-5910).
IEEE DOI 2002
BibRef

Liu, L.J.[Li-Jie], Lu, J.W.[Ji-Wen], Xu, C.J.[Chun-Jing], Tian, Q.[Qi], Zhou, J.[Jie],
Deep Fitting Degree Scoring Network for Monocular 3D Object Detection,
CVPR19(1057-1066).
IEEE DOI 2002
BibRef

Ku, J.[Jason], Pon, A.D.[Alex D.], Waslander, S.L.[Steven L.],
Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction,
CVPR19(11859-11868).
IEEE DOI 2002
BibRef

Riegler, G.[Gernot], Liao, Y.[Yiyi], Donne, S.[Simon], Koltun, V.[Vladlen], Geiger, A.[Andreas],
Connecting the Dots: Learning Representations for Active Monocular Depth Estimation,
CVPR19(7616-7625).
IEEE DOI 2002
BibRef

Yang, Y.[Yanchao], Wong, A.[Alex], Soatto, S.[Stefano],
Dense Depth Posterior (DDP) From Single Image and Sparse Range,
CVPR19(3348-3357).
IEEE DOI 2002
BibRef

Wong, A.[Alex], Soatto, S.[Stefano],
Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction,
CVPR19(5637-5646).
IEEE DOI 2002
BibRef

Lee, J.H.[Jae-Han], Kim, C.S.[Chang-Su],
Monocular Depth Estimation Using Relative Depth Maps,
CVPR19(9721-9730).
IEEE DOI 2002
BibRef

Pilzer, A.[Andrea], Lathuiliere, S.[Stephane], Sebe, N.[Nicu], Ricci, E.[Elisa],
Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation,
CVPR19(9760-9769).
IEEE DOI 2002
BibRef

Zhao, S.S.[Shan-Shan], Fu, H.[Huan], Gong, M.M.[Ming-Ming], Tao, D.C.[Da-Cheng],
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation,
CVPR19(9780-9790).
IEEE DOI 2002
BibRef

Tosi, F.[Fabio], Aleotti, F.[Filippo], Poggi, M.[Matteo], Mattoccia, S.[Stefano],
Learning Monocular Depth Estimation Infusing Traditional Stereo Knowledge,
CVPR19(9791-9801).
IEEE DOI 2002
BibRef

Zhi, S.[Shuaifeng], Bloesch, M.[Michael], Leutenegger, S.[Stefan], Davison, A.J.[Andrew J.],
SceneCode: Monocular Dense Semantic Reconstruction Using Learned Encoded Scene Representations,
CVPR19(11768-11777).
IEEE DOI 2002
BibRef

Chen, P.Y.[Po-Yi], Liu, A.H.[Alexander H.], Liu, Y.C.[Yen-Cheng], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation,
CVPR19(2619-2627).
IEEE DOI 2002
BibRef

Tatarchenko, M.[Maxim], Richter, S.R.[Stephan R.], Ranftl, R.[Rene], Li, Z.[Zhuwen], Koltun, V.[Vladlen], Brox, T.[Thomas],
What Do Single-View 3D Reconstruction Networks Learn?,
CVPR19(3400-3409).
IEEE DOI 2002
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Yu, Y.[Ye], Smith, W.A.P.[William A. P.],
InverseRenderNet: Learning Single Image Inverse Rendering,
CVPR19(3150-3159).
IEEE DOI 2002
BibRef

Kato, H.[Hiroharu], Harada, T.[Tatsuya],
Learning View Priors for Single-View 3D Reconstruction,
CVPR19(9770-9779).
IEEE DOI 2002
BibRef

Wei, Y.[Yi], Liu, S.H.[Shao-Hui], Zhao, W.[Wang], Lu, J.W.[Ji-Wen],
Conditional Single-View Shape Generation for Multi-View Stereo Reconstruction,
CVPR19(9643-9652).
IEEE DOI 2002
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Gur, S.[Shir], Wolf, L.[Lior],
Single Image Depth Estimation Trained via Depth From Defocus Cues,
CVPR19(7675-7684).
IEEE DOI 2002
BibRef

Jack, D.[Dominic], Maire, F.[Frederic], Shirazi, S.[Sareh], Eriksson, A.[Anders],
IGE-Net: Inverse Graphics Energy Networks for Human Pose Estimation and Single-View Reconstruction,
CVPR19(7068-7077).
IEEE DOI 2002
BibRef

Chen, W.F.[Wei-Feng], Qian, S.Y.[Sheng-Yi], Deng, J.[Jia],
Learning Single-Image Depth From Videos Using Quality Assessment Networks,
CVPR19(5597-5606).
IEEE DOI 2002
BibRef

Facil, J.M.[Jose M.], Ummenhofer, B.[Benjamin], Zhou, H.Z.[Hui-Zhong], Montesano, L.[Luis], Brox, T.[Thomas], Civera, J.[Javier],
CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth,
CVPR19(11818-11827).
IEEE DOI 2002
BibRef

Kaushik, V., Lall, B.,
UnDispNet: Unsupervised Learning for Multi-Stage Monocular Depth Prediction,
3DV19(633-642)
IEEE DOI 1911
Training, Estimation, Image resolution, Image reconstruction, Computer architecture, Cameras, Depth Prediction BibRef

Poggi, M., Aleotti, F., Tosi, F., Mattoccia, S.,
On the Uncertainty of Self-Supervised Monocular Depth Estimation,
CVPR20(3224-3234)
IEEE DOI 2008
Uncertainty, Estimation, Task analysis, Cameras, Predictive models, Training, Optical imaging BibRef

Andraghetti, L., Myriokefalitakis, P., Dovesi, P.L., Luque, B., Poggi, M., Pieropan, A., Mattoccia, S.,
Enhancing Self-Supervised Monocular Depth Estimation with Traditional Visual Odometry,
3DV19(424-433)
IEEE DOI 1911
Estimation, Training, Cameras, Visual odometry, Pipelines, Feature extraction, self supervised BibRef

Ramirez, P.Z.[Pierluigi Zama], Poggi, M.[Matteo], Tosi, F.[Fabio], Mattoccia, S.[Stefano], di Stefano, L.[Luigi],
Geometry Meets Semantics for Semi-supervised Monocular Depth Estimation,
ACCV18(III:298-313).
Springer DOI 1906
BibRef

Grabner, A., Roth, P.M., Lepetit, V.,
Location Field Descriptors: Single Image 3D Model Retrieval in the Wild,
3DV19(583-593)
IEEE DOI 1806
Solid modeling, Computational modeling, Shape, Task analysis, Feature extraction, 3D Pose BibRef

Elkerdawy, S., Zhang, H., Ray, N.,
Lightweight Monocular Depth Estimation Model by Joint End-to-End Filter Pruning,
ICIP19(4290-4294)
IEEE DOI 1910
Monocular depth estimation, Filter pruning, Model compression BibRef

Atapour-Abarghouei, A.[Amir], Breckon, T.P.[Toby P.],
Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach,
CVPR19(3368-3379).
IEEE DOI 2002
BibRef

Lu, G.Y.[Guo-Yu], Han, Y.H.[Ya-Hong],
3D Shape Retrieval through Multilayer RBF Neural Network,
ICIP19(2394-2398)
IEEE DOI 1910
3D Object Retrieval, RBF Neural Network, Multilayer Perceptron BibRef

Hsieh, Y., Lin, W., Li, D., Chuang, J.,
Deep Learning-Based Obstacle Detection and Depth Estimation,
ICIP19(1635-1639)
IEEE DOI 1910
Deep learning, YOLOv3, object detection, depth prediction, KITTI dataset BibRef

Irie, G., Kawanishi, T., Kashino, K.,
Robust Learning for Deep Monocular Depth Estimation,
ICIP19(964-968)
IEEE DOI 1910
Monocular depth estimation, robust loss function, supervised learning BibRef

Zou, H., Xian, K., Yang, J., Cao, Z.,
Mean-Variance Loss for Monocular Depth Estimation,
ICIP19(1760-1764)
IEEE DOI 1910
mean-variance loss, monocular depth estimation, classification BibRef

Xiong, X., Cao, Z., Zhang, C., Xian, K., Zou, H.,
Binoboost: Boosting Self-Supervised Monocular Depth Prediction with Binocular Guidance,
ICIP19(1770-1774)
IEEE DOI 1910
Self-supervised, Monocular depth prediction, Binocular guidance BibRef

Jiang, H., Huang, R.,
High Quality Monocular Depth Estimation Via A Multi-Scale Network And A Detail-Preserving Objective,
ICIP19(1920-1924)
IEEE DOI 1910
Monocular Depth Estimation, MultiScale Network, Detail-Preserving Loss BibRef

Choi, S., Nguyen, A., Kim, J., Ahn, S., Lee, S.,
Point Cloud Deformation for Single Image 3d Reconstruction,
ICIP19(2379-2383)
IEEE DOI 1910
3D reconstruction, point cloud processing, neural network, deep learning BibRef

Batavia, D.[Darshan], Hladuvka, J.[Jirí], Kropatsch, W.G.[Walter G.],
Partitioning 2D Images into Prototypes of Slope Region,
CAIP19(I:363-374).
Springer DOI 1909
BibRef

Li, R.[Ruibo], Xian, K.[Ke], Shen, C.H.[Chun-Hua], Cao, Z.G.[Zhi-Guo], Lu, H.[Hao], Hang, L.X.[Ling-Xiao],
Deep Attention-Based Classification Network for Robust Depth Prediction,
ACCV18(IV:663-678).
Springer DOI 1906
BibRef

Jack, D.[Dominic], Maire, F.[Frederic], Denman, S.[Simon], Eriksson, A.[Anders],
Sparse Convolutions on Continuous Domains for Point Cloud and Event Stream Networks,
ACCV20(I:400-416).
Springer DOI 2103
BibRef

Michalkiewicz, M., Pontes, J.K., Jack, D., Baktashmotlagh, M., Eriksson, A.,
Implicit Surface Representations As Layers in Neural Networks,
ICCV19(4742-4751)
IEEE DOI 2004
image reconstruction, image representation, neural net architecture, implicit surface representations, Geometry BibRef

Jack, D.[Dominic], Pontes, J.K.[Jhony K.], Sridharan, S.[Sridha], Fookes, C.[Clinton], Shirazi, S.[Sareh], Maire, F.[Frederic], Eriksson, A.[Anders],
Learning Free-Form Deformations for 3D Object Reconstruction,
ACCV18(II:317-333).
Springer DOI 1906
BibRef

Pontes, J.K.[Jhony K.], Kong, C.[Chen], Sridharan, S.[Sridha], Lucey, S.[Simon], Eriksson, A.[Anders], Fookes, C.[Clinton],
Image2Mesh: A Learning Framework for Single Image 3D Reconstruction,
ACCV18(I:365-381).
Springer DOI 1906
BibRef

Smith, R.[Rory], Burghardt, T.[Tilo],
DeepKey: Towards End-to-End Physical Key Replication from a Single Photograph,
GCPR18(487-502).
Springer DOI 1905
RGB image of a key, generate the 3D key. BibRef

Ito, S.[Seiya], Kaneko, N.[Naoshi], Shinohara, Y.[Yuma], Sumi, K.[Kazuhiko],
Deep Modular Network Architecture for Depth Estimation from Single Indoor Images,
3D-Wild18(I:324-336).
Springer DOI 1905
BibRef

Mandikal, P.[Priyanka], Navaneet, K.L., Babu, R.V.[R. Venkatesh],
3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction from a Single Image,
3DSemantics18(III:662-674).
Springer DOI 1905
BibRef

Ochs, M.[Matthias], Kretz, A.[Adrian], Mester, R.[Rudolf],
SDNet: Semantically Guided Depth Estimation Network,
GCPR19(288-302).
Springer DOI 1911
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Brickwedde, F.[Fabian], Abraham, S.[Steffen], Mester, R.[Rudolf],
Exploiting Single Image Depth Prediction for Mono-stixel Estimation,
CVRoads18(I:240-255).
Springer DOI 1905
BibRef

Stathopoulou, E.K., Remondino, F.,
Semantic Photogrammetry: Boosting Image-based 3D Reconstruction With Semantic Labeling,
3DARCH19(685-690).
DOI Link 1904
BibRef

Yusiong, J.P., Naval, P.,
AsiANet: Autoencoders in Autoencoder for Unsupervised Monocular Depth Estimation,
WACV19(443-451)
IEEE DOI 1904
image classification, image motion analysis, learning (artificial intelligence), neural nets, Network architecture BibRef

Kumar, A.C.S.[Arun C.S.], Bhandarkar, S.M.[Suchendra M.], Prasad, M.[Mukta],
Learning Hierarchical Models for Class-Specific Reconstruction from Natural Data,
AutoDrive18(1170-11708)
IEEE DOI 1812
Shape, Solid modeling, Image reconstruction, Deformable models, Strain BibRef

Xie, J.W.[Jian-Wen], Zheng, Z.L.[Zi-Long], Gao, R.Q.[Rui-Qi], Wang, W.G.[Wen-Guan], Zhu, S.C.[Song-Chun], Wu, Y.N.[Ying Nian],
Learning Descriptor Networks for 3D Shape Synthesis and Analysis,
CVPR18(8629-8638)
IEEE DOI 1812
Solid modeling, Shape, Data models, Training, Generators, Analytical models BibRef

Tulsiani, S.[Shubham], Efros, A.A.[Alexei A.], Malik, J.[Jitendra],
Multi-view Consistency as Supervisory Signal for Learning Shape and Pose Prediction,
CVPR18(2897-2905)
IEEE DOI 1812
Shape, Training, Geometry, Cameras, Loss measurement, Image reconstruction BibRef

Tulsiani, S.[Shubham], Gupta, S.[Saurabh], Fouhey, D.[David], Efros, A.A.[Alexei A.], Malik, J.[Jitendra],
Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene,
CVPR18(302-310)
IEEE DOI 1812
Shape, Layout, Proposals, Image resolution, Standards BibRef

Fu, H., Gong, M., Wang, C., Batmanghelich, K., Tao, D.,
Deep Ordinal Regression Network for Monocular Depth Estimation,
CVPR18(2002-2011)
IEEE DOI 1812
Estimation, Feature extraction, Training, Spatial resolution, Kernel BibRef

Srinivasan, P.P., Garg, R., Wadhwa, N., Ng, R., Barron, J.T.,
Aperture Supervision for Monocular Depth Estimation,
CVPR18(6393-6401)
IEEE DOI 1812
Apertures, Cameras, Rendering (computer graphics), Estimation, Geometry, Prediction algorithms, Three-dimensional displays BibRef

Kumar, A.C., Bhandarkar, S.M., Prasad, M.,
DepthNet: A Recurrent Neural Network Architecture for Monocular Depth Prediction,
DeepSLAM18(396-3968)
IEEE DOI 1812
Simultaneous localization and mapping, Image reconstruction, Recurrent neural networks, Video sequences BibRef

Ron, D., Duan, K., Ma, C., Xu, N., Wang, S., Hanumante, S., Sagar, D.,
Monocular Depth Estimation via Deep Structured Models with Ordinal Constraints,
3DV18(570-577)
IEEE DOI 1812
feedforward neural nets, image resolution, inference mechanisms, user interfaces, deep structured model, ordinal constraints BibRef

Xian, K., Shen, C., Cao, Z., Lu, H., Xiao, Y., Li, R., Luo, Z.,
Monocular Relative Depth Perception with Web Stereo Data Supervision,
CVPR18(311-320)
IEEE DOI 1812
Training, Measurement, Task analysis, Semantics, Estimation, Image segmentation, Network architecture BibRef

Niu, C., Li, J., Xu, K.,
Im2Struct: Recovering 3D Shape Structure from a Single RGB Image,
CVPR18(4521-4529)
IEEE DOI 1812
Shape, Solid modeling, Periodic structures, Image reconstruction BibRef

Zou, C., Colburn, A., Shan, Q., Hoiem, D.,
LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image,
CVPR18(2051-2059)
IEEE DOI 1812
Layout, Convolution, Training, Cameras, Estimation BibRef

Qian, Y.M.[Yi-Ming], Ramalingam, S.[Srikumar], Elder, J.H.[James H.],
LS3D: Single-View Gestalt 3D Surface Reconstruction from Manhattan Line Segments,
ACCV18(IV:399-416).
Springer DOI 1906
BibRef

Ranade, S.[Siddhant], Yu, X.[Xin], Kakkar, S.[Shantnu], Miraldo, P.[Pedro], Ramalingam, S.[Srikumar],
Mapping of Sparse 3d Data Using Alternating Projection,
ACCV20(I:295-313).
Springer DOI 2103
BibRef

Ranade, S.[Siddhant], Ramalingam, S.[Srikumar],
Novel Single View Constraints for Manhattan 3D Line Reconstruction,
3DV18(625-633)
IEEE DOI 1812
computational geometry, game theory, graph theory, image reconstruction, integer programming, linear programming, structure from motion BibRef

Lin, H.J., Huang, S., Lai, S., Chiang, C.,
Indoor Scene Layout Estimation from a Single Image,
ICPR18(842-847)
IEEE DOI 1812
Layout, Estimation, Semantics, Training, Image edge detection, Task analysis, Pipelines BibRef

Kim, S., Manduchi, R., Qin, S.,
Multi-planar Monocular Reconstruction of Manhattan Indoor Scenes,
3DV18(616-624)
IEEE DOI 1812
cameras, computational geometry, image matching, image motion analysis, image reconstruction, image sequences, Plane Reconstruction BibRef

Pan, J., Li, J., Han, X., Jia, K.,
Residual MeshNet: Learning to Deform Meshes for Single-View 3D Reconstruction,
3DV18(719-727)
IEEE DOI 1812
approximation theory, image reconstruction, learning (artificial intelligence), mesh generation, neural nets, Mesh BibRef

Hao, Z.X.[Zhi-Xiang], Li, Y.[Yu], You, S.D.[Shao-Di], Lu, F.[Feng],
Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks,
3DV18(304-313)
IEEE DOI 1812
convolution, feature extraction, feedforward neural nets, image classification, image resolution, attention mechanism BibRef

Poggi, M., Tosi, F., Mattoccia, S.,
Learning Monocular Depth Estimation with Unsupervised Trinocular Assumptions,
3DV18(324-333)
IEEE DOI 1812
image motion analysis, image reconstruction, image sensors, learning (artificial intelligence), stereo image processing, trinocular BibRef

Bednarik, J., Fua, P., Salzmann, M.,
Learning to Reconstruct Texture-Less Deformable Surfaces from a Single View,
3DV18(606-615)
IEEE DOI 1812
image reconstruction, image representation, learning (artificial intelligence), mesh generation, shape recovery BibRef

Xu, B.[Bin], Chen, Z.Z.[Zhen-Zhong],
Multi-Level Fusion Based 3D Object Detection from Monocular Images,
CVPR18(2345-2353)
IEEE DOI 1812
Object detection, Proposals, Detectors, Feature extraction, Estimation BibRef

Lee, J., Heo, M., Kim, K., Kim, C.,
Single-Image Depth Estimation Based on Fourier Domain Analysis,
CVPR18(330-339)
IEEE DOI 1812
Estimation, Feature extraction, Frequency-domain analysis, Reliability, Discrete Fourier transforms, Image reconstruction BibRef

Li, Z., Snavely, N.,
MegaDepth: Learning Single-View Depth Prediction from Internet Photos,
CVPR18(2041-2050)
IEEE DOI 1812
Semantics, Image reconstruction, Training data, Internet, Training, Image segmentation BibRef

Sun, X., Wu, J., Zhang, X., Zhang, Z., Zhang, C., Xue, T., Tenenbaum, J.B., Freeman, W.T.,
Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling,
CVPR18(2974-2983)
IEEE DOI 1812
Shape, Solid modeling, Benchmark testing, Pose estimation, Image reconstruction BibRef

Shin, D., Fowlkes, C.C., Hoiem, D.,
Pixels, Voxels, and Views: A Study of Shape Representations for Single View 3D Object Shape Prediction,
CVPR18(3061-3069)
IEEE DOI 1812
Shape, Solid modeling, Predictive models, Decoding, Training, Automobiles BibRef

Pumarola, A., Agudo, A., Porzi, L., Sanfeliu, A., Lepetit, V., Moreno-Noguer, F.,
Geometry-Aware Network for Non-rigid Shape Prediction from a Single View,
CVPR18(4681-4690)
IEEE DOI 1812
Shape, Surface reconstruction, Strain, Image reconstruction, Surface texture BibRef

Chen, Z.[Zhao], Badrinarayanan, V.[Vijay], Drozdov, G.[Gilad], Rabinovich, A.[Andrew],
Estimating Depth from RGB and Sparse Sensing,
ECCV18(II: 176-192).
Springer DOI 1810
BibRef

de La Garanderie, G.P.[Grégoire Payen], Abarghouei, A.A.[Amir Atapour], Breckon, T.P.[Toby P.],
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery,
ECCV18(XIII: 812-830).
Springer DOI 1810
BibRef

Clark, R.[Ronald], Bloesch, M.[Michael], Czarnowski, J.[Jan], Leutenegger, S.[Stefan], Davison, A.J.[Andrew J.],
Learning to Solve Nonlinear Least Squares for Monocular Stereo,
ECCV18(VIII: 291-306).
Springer DOI 1810
BibRef

Heo, M.[Minhyeok], Lee, J.[Jaehan], Kim, K.R.[Kyung-Rae], Kim, H.U.[Han-Ul], Kim, C.S.[Chang-Su],
Monocular Depth Estimation Using Whole Strip Masking and Reliability-Based Refinement,
ECCV18(II: 39-55).
Springer DOI 1810
BibRef

Zhong, Y.R.[Yi-Ran], Dai, Y.C.[Yu-Chao], Li, H.D.[Hong-Dong],
Stereo Computation for a Single Mixture Image,
ECCV18(IX: 441-456).
Springer DOI 1810
BibRef

Huang, S.Y.[Si-Yuan], Qi, S.Y.[Si-Yuan], Zhu, Y.X.[Yi-Xin], Xiao, Y.[Yinxue], Xu, Y.[Yuanlu], Zhu, S.C.[Song-Chun],
Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image,
ECCV18(VII: 194-211).
Springer DOI 1810
BibRef

Jiao, J.B.[Jian-Bo], Cao, Y.[Ying], Song, Y.B.[Yi-Bing], Lau, R.[Rynson],
Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss,
ECCV18(XV: 55-71).
Springer DOI 1810
BibRef

Guo, X.Y.[Xiao-Yang], Li, H.S.[Hong-Sheng], Yi, S.[Shuai], Ren, J.[Jimmy], Wang, X.G.[Xiao-Gang],
Learning Monocular Depth by Distilling Cross-Domain Stereo Networks,
ECCV18(XI: 506-523).
Springer DOI 1810
BibRef

Wang, N.Y.[Nan-Yang], Zhang, Y.[Yinda], Li, Z.W.[Zhu-Wen], Fu, Y.W.[Yan-Wei], Liu, W.[Wei], Jiang, Y.G.[Yu-Gang],
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images,
ECCV18(XI: 55-71).
Springer DOI 1810
BibRef

Gan, Y.K.[Yu-Kang], Xu, X.Y.[Xiang-Yu], Sun, W.X.[Wen-Xiu], Lin, L.[Liang],
Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement,
ECCV18(III: 232-247).
Springer DOI 1810
BibRef

Zheng, C.X.[Chuan-Xia], Cham, T.J.[Tat-Jen], Cai, J.F.[Jian-Fei],
T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks,
ECCV18(VII: 798-814).
Springer DOI 1810
BibRef

Yang, G.[Guandao], Cui, Y.[Yin], Belongie, S.[Serge], Hariharan, B.[Bharath],
Learning Single-View 3D Reconstruction with Limited Pose Supervision,
ECCV18(XV: 90-105).
Springer DOI 1810
BibRef

Wu, J.J.[Jia-Jun], Zhang, C.K.[Cheng-Kai], Zhang, X.M.[Xiu-Ming], Zhang, Z.T.[Zhou-Tong], Freeman, W.T.[William T.], Tenenbaum, J.B.[Joshua B.],
Learning Shape Priors for Single-View 3D Completion And Reconstruction,
ECCV18(XI: 673-691).
Springer DOI 1810
BibRef

Jayaraman, D.[Dinesh], Gao, R.[Ruohan], Grauman, K.[Kristen],
ShapeCodes: Self-supervised Feature Learning by Lifting Views to Viewgrids,
ECCV18(XVI: 126-144).
Springer DOI 1810
BibRef

Carvalho, M., Saux, B.L., Trouvé-Peloux, P., Almansa, A., Champagnat, F.,
On Regression Losses for Deep Depth Estimation,
ICIP18(2915-2919)
IEEE DOI 1809
Estimation, Training, Standards, Convolution, Machine learning, Network architecture, Depth estimation, loss function BibRef

Huang, J.[Jun], Bi, T.T.[Tian-Teng], Liu, Y.[Yue], Wang, Y.T.[Yong-Tian],
Stereo Generation from a Single Image Using Deep Residual Network,
ICIP18(3653-3657)
IEEE DOI 1809
Painting, Training, Interpolation, Measurement, Stereo image processing, Image edge detection, layered images BibRef

Tian, H., Li, F.,
Depth Prediction From a Single Image with 3D Consistency,
ICIP18(111-115)
IEEE DOI 1809
Image color analysis, Training, Distortion, Color, Computer architecture, Solid modeling, random projection BibRef

Kurenkov, A., Ji, J., Garg, A., Mehta, V., Gwak, J., Choy, C., Savarese, S.,
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image,
WACV18(858-866)
IEEE DOI 1806
CAD, augmented reality, image reconstruction, learning (artificial intelligence), object recognition, BibRef

Nimisha, T.M., Mathamkode, A.[Arun], Ambasamudram, R.[Rajagopalan],
Dictionary Replacement for Single Image Restoration of 3D Scenes,
BMVC16(xx-yy).
HTML Version. 1805
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Schubert, D.[David], Demmel, N.[Nikolaus], Usenko, V.[Vladyslav], Stückler, J.[Jörg], Cremers, D.[Daniel],
Direct Sparse Odometry with Rolling Shutter,
ECCV18(VIII: 699-714).
Springer DOI 1810
BibRef

Yang, N.[Nan], Wang, R.[Rui], Stückler, J.[Jörg], Cremers, D.[Daniel],
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry,
ECCV18(VIII: 835-852).
Springer DOI 1810
BibRef

Kuznietsov, Y., Stückler, J.[Jörg], Leibe, B.[Bastian],
Semi-Supervised Deep Learning for Monocular Depth Map Prediction,
CVPR17(2215-2223)
IEEE DOI 1711
Cameras, Laser noise, Machine learning, Measurement by laser beam, Sensors, Training BibRef

Yao, Q., Luo, G., Zhu, Y.,
Depth estimation for outdoor image using couple dictionary learning and region detection,
VCIP17(1-4)
IEEE DOI 1804
edge detection, image representation, image retrieval, learning (artificial intelligence), single image depth estimation BibRef

Guo, X., Nguyen, K., Denman, S., Fookes, C., Sridharan, S.,
Single image depth prediction using super-column super-pixel features,
ICIP17(2657-2661)
IEEE DOI 1803
Error analysis, Feature extraction, Image color analysis, Image segmentation, Interpolation, Task analysis, Training, depth, super pixel BibRef

Chen, Y., Wang, F., Wang, X.,
Recovering complex non-rigid 3D structures from monocular images by union of nonlinear subspaces,
ICIP17(2622-2626)
IEEE DOI 1803
Cameras, Kernel, Radio frequency, Shape, Trajectory, subspace clustering BibRef

Weerasekera, C.S.[Chamara Saroj], Garg, R.[Ravi], Latif, Y.[Yasir], Reid, I.D.[Ian D.],
Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction,
ACCV18(V:609-624).
Springer DOI 1906
BibRef

Romaszko, L., Williams, C.K.I., Moreno, P., Kohli, P.,
Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image,
DeepLearn-G17(940-948)
IEEE DOI 1802
Cameras, Detectors, Graphics, Lighting, Object detection, Probabilistic logic, Transforms BibRef

Li, X., Jie, Z., Wang, W., Liu, C., Yang, J., Shen, X., Lin, Z., Chen, Q., Yan, S., Feng, J.,
FoveaNet: Perspective-Aware Urban Scene Parsing,
ICCV17(784-792)
IEEE DOI 1802
geometry, image recognition, neural nets, object detection, object recognition, FoveaNet model, BibRef

Zhu, R.[Rui], Galoogahi, H.K.[Hamed Kiani], Wang, C.Y.[Chao-Yang], Lucey, S.[Simon],
Rethinking Reprojection: Closing the Loop for Pose-Aware Shape Reconstruction from a Single Image,
ICCV17(57-65)
IEEE DOI 1802
image classification, image reconstruction, learning (artificial intelligence), object detection, BibRef

Baig, M.H.[Mohammad Haris], Torresani, L.[Lorenzo],
Coupled depth learning,
WACV16(1-10)
IEEE DOI 1606
Computational modeling. Depth from single image via learning. BibRef

Akhmadeev, F.[Foat],
Surface Prediction for a Single Image of Urban Scenes,
AutoSystems14(369-382).
Springer DOI 1504
BibRef

Ikeoka, H.[Hiroshi], Murata, T.[Takafumi], Okuwaki, M.[Maiki], Hamamoto, T.[Takayuki],
Depth estimation for automotive with tilted optics imaging,
ICIP14(3852-3856)
IEEE DOI 1502
Automotive engineering BibRef

Li, H.S.[Hong-Song], Song, T.[Ting], Wu, Z.H.[Ze-Huan], Ma, J.D.[Jian-Dong], Ding, G.Y.[Gang-Yi],
Reconstruction of a Complex Mirror Surface from a Single Image,
ISVC14(I: 402-412).
Springer DOI 1501
Use the multiple reflections of same environment point. BibRef

Liu, M.M.[Miao-Miao], Salzmann, M.[Mathieu], He, X.M.[Xu-Ming],
Discrete-Continuous Depth Estimation from a Single Image,
CVPR14(716-723)
IEEE DOI 1409
BibRef

Cheng, H.M.[Hsin-Min], Tseng, C.Y.[Chen-Yu], Hsin, C.H.[Cheng-Ho], Wang, S.J.[Sheng-Jyh],
Single-image 3-D depth estimation for urban scenes,
ICIP13(2121-2125)
IEEE DOI 1402
3-D depth recovery;Depth estimation BibRef

Chapter on 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings continues in
Single View 3D Reconstruction, Convolutional Neural Networks, CNN .