9.8.1 Single Image, Single View 3D Reconstruction, Learning

Chapter Contents (Back)
Single View. Monocular Depth. A lot of related work is in IBR. See also Virtual View Generation, View Synthesis, Image Based Rendering, IBR, Morphing. See also Depth Ordering, Single View 3D Reconstruction.

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
computer vision, 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

Cao, Y., Wu, Z., Shen, C.,
Estimating Depth From Monocular Images as Classification Using Deep Fully Convolutional Residual Networks,
CirSysVideo(28), No. 11, November 2018, pp. 3174-3182.
IEEE DOI 1811
Estimation, Training, Semantics, Network architecture, Predictive models, Neural networks, Probability distribution, depth estimation 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

Yan, H., Yu, X., Zhang, Y., Zhang, S., Zhao, X., Zhang, L.,
Single Image Depth Estimation With Normal Guided Scale Invariant Deep Convolutional Fields,
CirSysVideo(29), No. 1, January 2019, pp. 80-92.
IEEE DOI 1901
Estimation, Semantics, Memory management, Feature extraction, multitask CNN 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

Amirkolaee, H.A.[Hamed Amini], Arefi, H.[Hossein],
Height estimation from single aerial images using a deep convolutional encoder-decoder network,
PandRS(149), 2019, pp. 50-66.
Elsevier DOI 1903
Convolutional neural network, Height image, Digital aerial image, Encoder, Decoder BibRef

Han, X., Yu, J., Luo, J., Sun, W.,
Reconstruction From Multispectral to Hyperspectral Image Using Spectral Library-Based Dictionary Learning,
GeoRS(57), No. 3, March 2019, pp. 1325-1335.
IEEE DOI 1903
geophysical image processing, hyperspectral imaging, image classification, image fusion, image matching, spectral library 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

Liu, J.[Jiwei], Zhang, Y.Z.[Yun-Zhou], Cui, J.[Jiahua], Feng, Y.H.[Yong-Hui], Pang, L.[Linzhuo],
Fully convolutional multi-scale dense networks for monocular depth estimation,
IET-CV(13), No. 5, August 2019, pp. 515-522.
DOI Link 1908
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

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

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

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

Yang, X., Gao, Y., Luo, H., Liao, C., Cheng, K.,
Bayesian DeNet: Monocular Depth Prediction and Frame-Wise Fusion With Synchronized Uncertainty,
MultMed(21), No. 11, November 2019, pp. 2701-2713.
IEEE DOI 1911
Uncertainty, Cameras, Bayes methods, Simultaneous localization and mapping, Training, Video sequences, convolutional neural network BibRef

Padhy, R.P.[Ram Prasad], Chang, X.J.[Xiao-Jun], Choudhury, S.K.[Suman Kumar], Sa, P.K.[Pankaj Kumar], Bakshi, S.[Sambit],
Multi-stage cascaded deconvolution for depth map and surface normal prediction from single image,
PRL(127), 2019, pp. 165-173.
Elsevier DOI 1911
Scene understanding, Depth map, Surface normal, CNN, Multi-stage, Deconvolution 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

Chen, S.[Songnan], Tang, M.[Mengxia], Kan, J.[Jiangming],
Encoder-decoder with densely convolutional networks for monocular depth estimation,
JOSA-A(36), No. 10, October 2019, pp. 1709-1718.
DOI Link 1912
Feature extraction, Image registration, Image resolution, Motion estimation, Neural networks, Stochastic gradient descent 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, Computer vision 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.[Zhengfa], Li, C.[Chen], Wu, M.[Mingfei], Gao, Y.[Yuanming], 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

Sun, Y.H.[Yun-Han], Shi, J.L.[Jin-Long], Bai, S.[Suqin], Qian, Q.A.[Qi-Ang], Sun, Z.X.[Zheng-Xing],
Single View Depth Estimation via Dense Convolution Network with Self-supervision,
MMMod20(II:241-253).
Springer DOI 2003
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

Song, W., Li, S., Liu, J., Hao, A., Zhao, Q., Qin, H.,
Contextualized CNN for Scene-Aware Depth Estimation From Single RGB Image,
MultMed(22), No. 5, May 2020, pp. 1220-1233.
IEEE DOI 2005
Estimation, Semantics, Training, Task analysis, Feature extraction, Decoding, Convolution, Depth Estimation, CNN, Single RGB Image, Scene-Aware Algorithm 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

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

Zhang, Y.Y.[Yu-Yang], Xu, S.B.[Shi-Biao], Wu, B.Y.[Bao-Yuan], Shi, J.[Jian], Meng, W.L.[Wei-Liang], Zhang, X.P.[Xiao-Peng],
Unsupervised Multi-View Constrained Convolutional Network for Accurate Depth Estimation,
IP(29), 2020, pp. 7019-7031.
IEEE DOI 2007
Estimation, Training, Feature extraction, Geometry, Computer vision, Cameras, Unsupervised learning, Unsupervised learning, depth consistency BibRef

Liu, J.[Jun], Li, Q.[Qing], Cao, R.[Rui], Tang, W.M.[Wen-Ming], Qiu, G.P.[Guo-Ping],
MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation,
PandRS(166), 2020, pp. 255-267.
Elsevier DOI 2007
Monocular depth estimation, Convolutional neural network, Unsupervised learning, Lightweight, Real-time 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

Pilzer, A.[Andrea], Lathuiličre, 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

Cheng, X.J.[Xin-Jing], Wang, P.[Peng], Yang, R.G.[Rui-Gang],
Learning Depth with Convolutional Spatial Propagation Network,
PAMI(42), No. 10, October 2020, pp. 2361-2379.
IEEE DOI 2009
BibRef
Earlier:
Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network,
ECCV18(XVI: 108-125).
Springer DOI 1810
Estimation, Task analysis, Cameras, Laser radar, Convolutional codes, Benchmark testing, spatial pyramid pooling 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


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, Computer vision, Conferences, Microcontrollers, Computer architecture, Pattern recognition 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

Rodríguez-Santiago, A.L.[Armando Levid], Arias-Aguilar, J.A.[José Anibal], Petrilli-Barceló, A.E.[Alberto Elías], Miranda-Luna, R.[Rosebet],
A Simple Methodology for 2d Reconstruction Using a CNN Model,
MCPR20(98-107).
Springer DOI 2007
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

Fang, Z., Chen, X., Chen, Y., Van Gool, L.J.,
Towards Good Practice for CNN-Based Monocular Depth Estimation,
WACV20(1080-1089)
IEEE DOI 2006
Estimation, Training, Computer architecture, Decoding, Analytical models, Image resolution, Network architecture 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.[Sanghoon],
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

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

Shin, D., Ren, Z., Sudderth, E., Fowlkes, C.C.,
3D Scene Reconstruction With Multi-Layer Depth and Epipolar Transformers,
ICCV19(2172-2182)
IEEE DOI 2004
cameras, computational geometry, convolutional neural nets, image colour analysis, image reconstruction, Surface reconstruction 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

Huang, J., Zhou, Y., Funkhouser, T., Guibas, L.,
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., 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

Kaneko, M., Sakurada, K., Aizawa, K.,
TriDepth: Triangular Patch-Based Deep Depth Prediction,
DeepSLAM19(3747-3750)
IEEE DOI 2004
convolutional neural nets, feature extraction, image colour analysis, image reconstruction, single view depth prediction 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

Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., Yang, Y.,
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images,
ICCV19(7587-7596)
IEEE DOI 2004
BibRef
And: NeruArch19(2037-2040)
IEEE DOI 2004
Shape, Training, Solid modeling, Generators, GAN feature extraction, image sequences, object detection, object tracking, pose estimation, rendering (computer graphics), Generative adversarial networks BibRef

Hu, J.J.[Jun-Jie], Zhang, Y.[Yan], Okatani, T.[Takayuki],
Visualization of Convolutional Neural Networks for Monocular Depth Estimation,
ICCV19(3868-3877)
IEEE DOI 2004
computer vision, convolutional neural nets, feature extraction, object detection, convolutional neural networks, Convolutional neural networks 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., Bulň, 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
computer vision, 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
computer vision, image colour analysis, image reconstruction, image representation, image resolution, Image color analysis BibRef

Garg, R., Wadhwa, N., 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

Zhou, Y., Qi, H., Zhai, Y., Sun, Q., Chen, Z., Wei, L., Ma, Y.,
Learning to Reconstruct 3D Manhattan Wireframes From a Single Image,
ICCV19(7697-7706)
IEEE DOI 2004
computer vision, convolutional neural nets, image reconstruction, image representation, learning (artificial intelligence), Image reconstruction 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

Khan, S.H.[Salman H.], Guo, Y.L.[Yu-Lan], Hayat, M.[Munawar], Barnes, N.[Nick],
Unsupervised Primitive Discovery for Improved 3D Generative Modeling,
CVPR19(9731-9740).
IEEE DOI 2002
BibRef

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

Xiang, C.[Chong], Qi, C.R.[Charles R.], Li, B.[Bo],
Generating 3D Adversarial Point Clouds,
CVPR19(9128-9136).
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

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

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

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
BibRef

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
BibRef

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

Atapour-Abarghouei, A.[Amir], Breckon, T.P.[Toby P.],
To Complete or to Estimate, That is the Question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation,
3DV19(183-193)
IEEE DOI 1911
BibRef
And:
Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior,
ICIP19(4295-4299)
IEEE DOI 1910
BibRef
Earlier:
Real-Time Monocular Depth Estimation Using Synthetic Data with Domain Adaptation via Image Style Transfer,
CVPR18(2800-2810)
IEEE DOI 1812
Estimation, Training, Generators, Data models, Laser radar, Training data, Task analysis, Monocular Depth Estimation, 3D Scene Understanding. Adaptation models, Predictive models, Neural networks. Monocular Depth Estimation, Convolutional Neural Networks, Semantic Segmentation 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

Luis, J., Bello, G., Kim, M.,
A Novel Monocular Disparity Estimation Network with Domain Transformation and Ambiguity Learning,
ICIP19(474-478)
IEEE DOI 1910
Monocular disparity estimation, deep convolutional neural networks (DCNN), unsupervised learning BibRef

Kumari, S., Jha, R.R., Bhavsar, A., Nigam, A.,
AUTODEPTH: Single Image Depth Map Estimation via Residual CNN Encoder-Decoder and Stacked Hourglass,
ICIP19(340-344)
IEEE DOI 1910
Depth map estimation, CNN, Residual connection, Encoder-decoder, Hourglass 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

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

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

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

Zhao, S.Y.[Shi-Yu], Zhang, L.[Lin], Shen, Y.[Ying], Zhu, Y.N.[Yong-Ning],
A CNN-Based Depth Estimation Approach with Multi-scale Sub-pixel Convolutions and a Smoothness Constraint,
ACCV18(II:365-380).
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

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

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

Koch, T.[Tobias], Liebel, L.[Lukas], Fraundorfer, F.[Friedrich], Körner, M.[Marco],
Evaluation of CNN-Based Single-Image Depth Estimation Methods,
DeepLearn-G18(III:331-348).
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
BibRef

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

Kniaz, V.V., Remondino, F., Knyaz, V.A.,
Generative Adversarial Networks for Single Photo 3D Reconstruction,
3DARCH19(403-408).
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

Hu, J., Ozay, M., Zhang, Y., Okatani, T.,
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps With Accurate Object Boundaries,
WACV19(1043-1051)
IEEE DOI 1904
convolutional neural nets, feature extraction, image fusion, image reconstruction, image resolution, inference mechanisms, Image edge detection 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, Two dimensional displays BibRef

Xu, D., Wang, W., Tang, H., Liu, H., Sebe, N., Ricci, E.,
Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation,
CVPR18(3917-3925)
IEEE DOI 1812
Estimation, Predictive models, Task analysis, Computer architecture, Semantics, Computational modeling, Fuses 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.,
Monocular Depth Prediction Using Generative Adversarial Networks,
DeepSLAM18(413-4138)
IEEE DOI 1812
Image reconstruction, Generators, Training, Generative adversarial networks, Estimation, Cameras BibRef

He, L., Yu, M., Wang, G.,
Spindle-Net: CNNs for Monocular Depth Inference with Dilation Kernel Method,
ICPR18(2504-2509)
IEEE DOI 1812
Convolution, Image resolution, Kernel, Feature extraction, Neural networks, Computer architecture, Task analysis 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
computer vision, 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], 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., Li, Y., You, S., Lu, F.,
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

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

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

Jaritz, M., Charette, R.D., Wirbel, E., Perrotton, X., Nashashibi, F.,
Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation,
3DV18(52-60)
IEEE DOI 1812
computer vision, feature extraction, image colour analysis, image segmentation, learning (artificial intelligence), RGB+sparse depth fusion 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

Jiang, L.[Li], Shi, S.[Shaoshuai], 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

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

Yang, F.T.[Feng-Ting], Zhou, Z.[Zihan],
Recovering 3D Planes from a Single Image via Convolutional Neural Networks,
ECCV18(X: 87-103).
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

da Silveira, T.L.T., Dal'aqua, L.P., Jung, C.R.,
Indoor Depth Estimation from Single Spherical Images,
ICIP18(2935-2939)
IEEE DOI 1809
Estimation, Cameras, Distortion, Image color analysis, Training, Convolutional neural networks, Solid modeling, Spherical images, BibRef

Moukari, M., Picard, S., Simoni, L., Jurie, F.,
Deep Multi-Scale Architectures for Monocular Depth Estimation,
ICIP18(2940-2944)
IEEE DOI 1809
Training, Estimation, Decoding, Computer architecture, Semantics, Spatial resolution, Task analysis, monocular depth estimation, CNN architecture 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, computer vision, 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
BibRef

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
computer vision, 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

Johnston, A., Garg, R., Carneiro, G., Reid, I.D.[Ian D.],
Scaling CNNs for High Resolution Volumetric Reconstruction from a Single Image,
DeepLearn-G17(930-939)
IEEE DOI 1802
Convolution, Deconvolution, Discrete cosine transforms, Image reconstruction, Shape, Solid modeling, Training 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
computer vision, 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

Hua, Y., Tian, H., Cai, A., Shi, P.,
Cross-modal correlation learning with deep convolutional architecture,
VCIP15(1-4)
IEEE DOI 1605
Analytical models BibRef

Tian, H.[Hu], Zhuang, B.[Bojin], Hua, Y.[Yan], Cai, A.[Anni],
Depth inference with convolutional neural network,
VCIP14(169-172)
IEEE DOI 1504
BibRef
Earlier:
Depth extraction from a single image by sampling based on distance metric learning,
ICIP14(2017-2021)
IEEE DOI 1502
feature extraction. Estimation. Mahalanobis distance rather than Euclidean distance between images. depth fusion. 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
Depth Ordering, Single View 3D Reconstruction .


Last update:Sep 24, 2020 at 19:44:22