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],
Lathuilière, S.,
Xu, D.[Dan],
Puscas, M.M.[Mihai M.],
Ricci, E.[Elisa],
Sebe, N.[Nicu],
Progressive Fusion for Unsupervised Binocular Depth Estimation Using
Cycled Networks,
PAMI(42), No. 10, October 2020, pp. 2380-2395.
IEEE DOI
2009
BibRef
Earlier: A1, A3, A4, A5, A6, Only:
Unsupervised Adversarial Depth Estimation Using Cycled Generative
Networks,
3DV18(587-595)
IEEE DOI
1812
Estimation, Training, Deep learning, Cameras, Solid modeling,
Predictive models, Network architecture, Stereo depth estimation,
cycle network.
cameras, estimation theory, stereo image processing,
unsupervised learning, supervised regression,
Adversarial Learning
BibRef
Puscas, M.M.[Mihai Marian],
Xu, D.[Dan],
Pilzer, A.[Andrea],
Sebe, N.[Niculae],
Structured Coupled Generative Adversarial Networks for Unsupervised
Monocular Depth Estimation,
3DV19(18-26)
IEEE DOI
1806
Estimation, Generative adversarial networks, Couplings, Generators,
Task analysis, Testing, Unsupervised,
unsupervised monocular depth estimation
BibRef
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
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.,
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
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
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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 .