Choi, S.H.[Sung-Hwan],
Min, D.B.[Dong-Bo],
Ham, B.[Bumsub],
Kim, Y.J.[Young-Jung],
Oh, C.[Changjae],
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
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.[Yunzhou],
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
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.,
Breckon, T.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
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
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
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
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
Pilzer, A.[Andrea],
Xu, D.[Dan],
Puscas, M.M.[Mihai M.],
Ricci, E.[Elisa],
Sebe, N.[Nicu],
Unsupervised Adversarial Depth Estimation Using Cycled Generative
Networks,
3DV18(587-595)
IEEE DOI
1812
cameras, estimation theory, stereo image processing,
unsupervised learning, supervised regression,
Adversarial Learning
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
Cheng, X.J.[Xin-Jing],
Wang, P.[Peng],
Yang, R.G.[Rui-Gang],
Depth Estimation via Affinity Learned with Convolutional Spatial
Propagation Network,
ECCV18(XVI: 108-125).
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
Jung, H.,
Kim, Y.,
Min, D.,
Oh, C.,
Sohn, K.,
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
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.[Gangyi],
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
Visa, G.P.[Guillem Palou],
Salembier, P.[Philippe],
Precision-Recall-Classification Evaluation Framework:
Application to Depth Estimation on Single Images,
ECCV14(I: 648-662).
Springer DOI
1408
depth ordering on single images. Segment then order.
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
Three-Dimensional Reconstruction from Different Views .