Choi, S.H.[Sung-Hwan],
Min, D.B.[Dong-Bo],
Ham, B.[Bumsub],
Kim, Y.J.[Young-Jung],
Oh, C.J.[Chang-Jae],
Sohn, K.H.[Kwang-Hoon],
Depth Analogy: Data-Driven Approach for Single Image Depth Estimation
Using Gradient Samples,
IP(24), No. 12, December 2015, pp. 5953-5966.
IEEE DOI
1512
Poisson distribution
BibRef
Jung, H.,
Kim, Y.J.[Young-Jung],
Min, D.B.[Dong-Bo],
Oh, C.J.[Chang-Jae],
Sohn, K.H.[Kwang-Hoon],
Depth prediction from a single image with conditional adversarial
networks,
ICIP17(1717-1721)
IEEE DOI
1803
Databases, Estimation, Generators, Periodic structures,
Spatial resolution, Training, Depth from a single image,
generative adversarial learning
BibRef
Kim, Y.J.[Young-Jung],
Min, D.B.[Dong-Bo],
Ham, B.[Bumsub],
Sohn, K.H.[Kwang-Hoon],
Fast Domain Decomposition for Global Image Smoothing,
IP(26), No. 8, August 2017, pp. 4079-4091.
IEEE DOI
1707
concave programming, decomposition,
least squares approximations, minimisation,
computational photography application,
edge-preserving smoothing,
BibRef
Kim, Y.J.[Young-Jung],
Jung, H.,
Min, D.B.[Dong-Bo],
Sohn, K.H.[Kwang-Hoon],
Deeply Aggregated Alternating Minimization for Image Restoration,
CVPR17(284-292)
IEEE DOI
1711
Algorithm design and analysis, Data models, Image reconstruction,
Image restoration, Minimization, Neural networks, Optimization
BibRef
Kim, S.[Sunok],
Choi, S.H.[Sung-Hwan],
Sohn, K.H.[Kwang-Hoon],
Learning depth from a single image using visual-depth words,
ICIP15(1895-1899)
IEEE DOI
1512
K-means clustering
BibRef
Kim, Y.J.[Young-Jung],
Choi, S.H.[Sung-Hwan],
Sohn, K.H.[Kwang-Hoon],
Data-driven single image depth estimation using weighted median
statistics,
ICIP14(3808-3812)
IEEE DOI
1502
Based on matches to similar images.
Wrong section, one of several, learn patterns from other images, apply.
BibRef
Herrera, J.L.[Jose L.],
del-Bianco, C.R.[Carlos R.],
García, N.[Narciso],
Automatic Depth Extraction from 2D Images Using a Cluster-Based
Learning Framework,
IP(27), No. 7, July 2018, pp. 3288-3299.
IEEE DOI
1805
BibRef
Earlier:
Learning 3D structure from 2D images using LBP features,
ICIP14(2022-2025)
IEEE DOI
1502
feature extraction, image colour analysis, image convertors,
image filtering, image representation, image segmentation,
machine learning
BibRef
Herrera, J.L.[Jose L.],
Konrad, J.[Janusz],
del-Bianco, C.R.[Carlos R.],
Garcia, N.[Narciso],
Learning-based depth estimation from 2D images using GIST and
saliency,
ICIP15(4753-4757)
IEEE DOI
1512
2D-to-3D Image Conversion; Depth maps; GIST Descriptor; Saliency.
Color
BibRef
Zhang, Z.,
Xu, C.,
Yang, J.,
Gao, J.,
Cui, Z.,
Progressive Hard-Mining Network for Monocular Depth Estimation,
IP(27), No. 8, August 2018, pp. 3691-3702.
IEEE DOI
1806
data mining, estimation theory,
feature extraction, image colour analysis, image resolution,
recursive learning
BibRef
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
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
Dai, R.Y.[Ren-Yue],
Gao, Y.B.[Yong-Bin],
Fang, Z.J.[Zhi-Jun],
Jiang, X.Y.[Xiao-Yan],
Wang, A.[Anjie],
Zhang, J.[Juan],
Zhong, C.S.[Ceng-Si],
Unsupervised learning of depth estimation based on attention model
and global pose optimization,
SP:IC(78), 2019, pp. 284-292.
Elsevier DOI
1909
Depth estimation, Attention model, Global pose optimization
BibRef
Wiles, O.[Olivia],
Zisserman, A.[Andrew],
Learning to Predict 3D Surfaces of Sculptures from Single and Multiple
Views,
IJCV(127), No. 11-12, December 2019, pp. 1780-1800.
Springer DOI
1911
BibRef
Earlier:
Wiles, O.[Olivia],
Zisserman, A.[Andrew],
3D Surface Reconstruction by Pointillism,
DeepLearn-G18(III:263-280).
Springer DOI
1905
BibRef
Moreau, A.[Ambroise],
Mancas, M.[Matei],
Dutoit, T.[Thierry],
Depth prediction from 2D images: A taxonomy and an evaluation study,
IVC(93), 2020, pp. 103825.
Elsevier DOI
2001
Depth prediction, Machine learning, Deep learning
BibRef
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
Rajeswar, S.[Sai],
Mannan, F.[Fahim],
Golemo, F.[Florian],
Parent-Lévesque, J.[Jérôme],
Vazquez, D.[David],
Nowrouzezahrai, D.[Derek],
Courville, A.[Aaron],
Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images Using
a View-Based Representation,
IJCV(128), No. 10-11, November 2020, pp. 2478-2493.
Springer DOI
2009
BibRef
Xu, W.P.[Wan-Peng],
Zou, L.[Ling],
Wu, L.D.[Ling-Da],
Fu, Z.P.[Zhi-Peng],
Self-Supervised Monocular Depth Learning in Low-Texture Areas,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link
2105
BibRef
Madhuanand, L.[Logambal],
Nex, F.[Francesco],
Yang, M.Y.[Michael Ying],
Self-supervised monocular depth estimation from oblique UAV videos,
PandRS(176), 2021, pp. 1-14.
Elsevier DOI
2106
Depth estimation, Monocular, UAV video,
Self-supervised learning, Scene Understanding
BibRef
Zhang, N.[Ning],
Nex, F.[Francesco],
Vosselman, G.[George],
Kerle, N.[Norman],
Lite-Mono: A Lightweight CNN and Transformer Architecture for
Self-Supervised Monocular Depth Estimation,
CVPR23(18537-18546)
IEEE DOI
2309
BibRef
Bian, J.W.[Jia-Wang],
Zhan, H.Y.[Huang-Ying],
Wang, N.Y.[Nai-Yan],
Li, Z.C.[Zhi-Chao],
Zhang, L.[Le],
Shen, C.H.[Chun-Hua],
Cheng, M.M.[Ming-Ming],
Reid, I.D.[Ian D.],
Unsupervised Scale-Consistent Depth Learning from Video,
IJCV(129), No. 9, September 2021, pp. 2548-2564.
Springer DOI
2108
Learn from video input.
BibRef
Hu, N.[Nian],
Zhou, H.[Heyu],
Liu, A.A.[An-An],
Huang, X.D.[Xiang-Dong],
Zhang, S.[Shenyuan],
Jin, G.Q.[Guo-Qing],
Guo, J.[Junbo],
Li, X.[Xuanya],
Collaborative Distribution Alignment for 2D image-based 3D shape
retrieval,
JVCIR(83), 2022, pp. 103426.
Elsevier DOI
2202
3D shape retrieval, Cross-domain retrieval, Multi-view learning
BibRef
Jung, D.K.[Dong-Ki],
Choi, J.[Jaehoon],
Lee, Y.[Yonghan],
Kim, D.[Deokhwa],
Kim, C.[Changick],
Manocha, D.[Dinesh],
Lee, D.H.[Dong-Hwan],
DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes,
ICCV21(12777-12787)
IEEE DOI
2203
Training, Tracking, Dynamics, Estimation, Cameras,
3D from a single image and shape-from-x,
Vision for robotics and autonomous vehicles
BibRef
Peluso, V.[Valentino],
Cipolletta, A.[Antonio],
Calimera, A.[Andrea],
Poggi, M.[Matteo],
Tosi, F.[Fabio],
Aleotti, F.[Filippo],
Mattoccia, S.[Stefano],
Monocular Depth Perception on Microcontrollers for Edge Applications,
CirSysVideo(32), No. 3, March 2022, pp. 1524-1536.
IEEE DOI
2203
Estimation, Cameras, Standards, Power demand, Monitoring,
Microcontrollers, Hardware, depth estimation, deep learning,
micro-controllers
BibRef
Chen, S.[Shu],
Pu, Z.D.[Zheng-Dong],
Fan, X.[Xiang],
Zou, B.[Beiji],
Fixing Defect of Photometric Loss for Self-Supervised Monocular Depth
Estimation,
CirSysVideo(32), No. 3, March 2022, pp. 1328-1338.
IEEE DOI
2203
Cameras, Geometry, Estimation, Optical variables control,
Optical imaging, Deep learning, Photometric consistency, epipolar geometry
BibRef
Nie, W.Z.[Wei-Zhi],
Zhao, Y.[Yue],
Nie, J.[Jie],
Liu, A.A.[An-An],
Zhao, S.C.[Si-Cheng],
CLN: Cross-Domain Learning Network for 2D Image-Based 3D Shape
Retrieval,
CirSysVideo(32), No. 3, March 2022, pp. 992-1005.
IEEE DOI
2203
Shape, Feature extraction, Task analysis, Visualization,
Computer architecture, Image processing, information retrieval,
multimedia computing
BibRef
Hu, N.[Nian],
Huang, X.D.[Xiang-Dong],
Li, W.H.[Wen-Hui],
Li, X.Y.[Xuan-Ya],
Liu, A.A.[An-An],
Cross-Domain Image-Object Retrieval Based on Weighted Optimal
Transport,
MultMed(25), 2023, pp. 9557-9571.
IEEE DOI
2312
BibRef
Ling, C.W.[Chuan-Wu],
Zhang, X.G.[Xiao-Gang],
Chen, H.[Hua],
Unsupervised Monocular Depth Estimation Using Attention and
Multi-Warp Reconstruction,
MultMed(24), 2022, pp. 2938-2949.
IEEE DOI
2206
Estimation, Image reconstruction, Convolution, Task analysis,
Training, Videos, Unsupervised learning, multi-Warp reconstruction
BibRef
Meng, X.Y.[Xu-Yang],
Fan, C.X.[Chun-Xiao],
Ming, Y.[Yue],
Yu, H.[Hui],
CORNet: Context-Based Ordinal Regression Network for Monocular Depth
Estimation,
CirSysVideo(32), No. 7, July 2022, pp. 4841-4853.
IEEE DOI
2207
Estimation, Image reconstruction, Training, Deep learning, Cameras,
Convergence, Monocular depth estimation, ordinal regression,
spatial attention
BibRef
Lee, S.[Seokju],
Rameau, F.[Francois],
Im, S.H.[Sung-Hoon],
Kweon, I.S.[In So],
Self-Supervised Monocular Depth and Motion Learning in Dynamic Scenes:
Semantic Prior to Rescue,
IJCV(130), No. 9, September 2022, pp. 2265-2285.
Springer DOI
2208
BibRef
Liu, S.L.[Sheng-Li],
Zhu, X.W.[Xiao-Wen],
Cao, Z.W.[Ze-Wei],
Wang, G.[Gang],
Deep 1D Landmark Representation Learning for Space Target Pose
Estimation,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Poggi, M.[Matteo],
Tosi, F.[Fabio],
Aleotti, F.[Filippo],
Mattoccia, S.[Stefano],
Real-Time Self-Supervised Monocular Depth Estimation Without GPU,
ITS(23), No. 10, October 2022, pp. 17342-17353.
IEEE DOI
2210
BibRef
Earlier: A1, A3, A2, A4:
On the Uncertainty of Self-Supervised Monocular Depth Estimation,
CVPR20(3224-3234)
IEEE DOI
2008
Estimation, Feature extraction, Real-time systems, Hardware,
Decoding, deep learning, deep architectures, unsupervised learning.
Uncertainty, Task analysis, 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
Zhao, C.Q.[Chao-Qiang],
Poggi, M.[Matteo],
Tosi, F.[Fabio],
Zhou, L.[Lei],
Sun, Q.Y.[Qi-Yu],
Tang, Y.[Yang],
Mattoccia, S.[Stefano],
GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation
for Indoor Scenes,
ICCV23(16163-16174)
IEEE DOI Code:
WWW Link.
2401
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
Li, R.[Runze],
Ji, P.[Pan],
Xu, Y.[Yi],
Bhanu, B.[Bir],
MonoIndoor++: Towards Better Practice of Self-Supervised Monocular
Depth Estimation for Indoor Environments,
CirSysVideo(33), No. 2, February 2023, pp. 830-846.
IEEE DOI
2302
BibRef
Earlier: A2, A1, A4, A3:
MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth
Estimation for Indoor Environments,
ICCV21(12767-12776)
IEEE DOI
2203
Training, Cameras, Pose estimation, Indoor environment, Transformers,
Videos, Monocular depth prediction, self-supervised learning.
Predictive models,
3D from a single image and shape-from-x,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Yang, L.[Lei],
Zhang, X.Y.[Xin-Yu],
Li, J.[Jun],
Wang, L.[Li],
Zhu, M.H.[Ming-Han],
Zhang, C.[Chuang],
Liu, H.P.[Hua-Ping],
Mix-Teaching: A Simple, Unified and Effective Semi-Supervised
Learning Framework for Monocular 3D Object Detection,
CirSysVideo(33), No. 11, November 2023, pp. 6832-6844.
IEEE DOI
2311
BibRef
Sun, L.[Libo],
Bian, J.W.[Jia-Wang],
Zhan, H.Y.[Huang-Ying],
Yin, W.[Wei],
Reid, I.D.[Ian D.],
Shen, C.H.[Chun-Hua],
SC-DepthV3: Robust Self-Supervised Monocular Depth Estimation for
Dynamic Scenes,
PAMI(46), No. 1, January 2024, pp. 497-508.
IEEE DOI
2312
Monocular depth estimation, unsupervised learning, self-supervised learning,
knowledge distillation
BibRef
Lee, S.[Sebin],
Im, W.B.[Woo-Bin],
Yoon, S.E.[Sung-Eui],
Multi-resolution distillation for self-supervised monocular depth
estimation,
PRL(176), 2023, pp. 215-222.
Elsevier DOI
2312
Monocular depth estimation, Self-supervised learning,
Self-distillation, Deep learning
BibRef
Li, G.B.[Guan-Bin],
Huang, R.C.[Ri-Cong],
Li, H.F.[Hao-Feng],
You, Z.Z.[Zun-Zhi],
Chen, W.K.[Wei-Kai],
SENSE: Self-Evolving Learning for Self-Supervised Monocular Depth
Estimation,
IP(33), 2024, pp. 439-450.
IEEE DOI
2401
BibRef
Kim, J.[Junoh],
Gao, R.[Rui],
Park, J.[Jisun],
Yoon, J.[Jinsoo],
Cho, K.[Kyungeun],
Switchable-Encoder-Based Self-Supervised Learning Framework for
Monocular Depth and Pose Estimation,
RS(15), No. 24, 2023, pp. 5739.
DOI Link
2401
BibRef
Shao, S.W.[Shu-Wei],
Pei, Z.C.[Zhong-Cai],
Chen, W.H.[Wei-Hai],
Li, R.[Ran],
Liu, Z.[Zhong],
Li, Z.G.[Zheng-Guo],
URCDC-Depth: Uncertainty Rectified Cross-Distillation With CutFlip
for Monocular Depth Estimation,
MultMed(26), 2024, pp. 3341-3353.
IEEE DOI Code:
WWW Link.
2402
Transformers, Uncertainty, Estimation, Training,
Computational modeling, Task analysis, Data models,
data augmentation
BibRef
Zhou, Z.K.[Zhong-Kai],
Fan, X.[Xinnan],
Shi, P.F.[Peng-Fei],
Xin, Y.Y.X.[Yuan-Yan-Xue],
Wang, X.T.[Xiao-Tian],
R-LKDepth: Recurrent Depth Learning With Larger Kernel,
SPLetters(31), 2024, pp. 601-605.
IEEE DOI
2402
Estimation, Kernel, Feature extraction, Standards, Decoding, Training,
Image resolution, Monocular depth estimation, larger receptive fields
BibRef
Bello, J.L.G.[Juan Luis Gonzalez],
Moon, J.[Jaeho],
Kim, M.C.[Mun-Churl],
Self-Supervised Monocular Depth Estimation With Positional Shift
Depth Variance and Adaptive Disparity Quantization,
IP(33), 2024, pp. 2074-2089.
IEEE DOI
2403
Videos, Cameras, Estimation, Quantization (signal), Training,
Task analysis, Depth from videos,
deep convolutional neural networks
BibRef
Xiang, M.[Mochu],
Dai, Y.C.[Yu-Chao],
Zhang, F.Y.[Fei-Yu],
Shi, J.W.[Jia-Wei],
Tian, X.Y.[Xin-Yu],
Zhang, Z.S.[Zhen-Song],
Towards a Unified Network for Robust Monocular Depth Estimation:
Network Architecture, Training Strategy and Dataset,
IJCV(132), No. 4, April 2024, pp. 1012-1028.
Springer DOI
2404
BibRef
Wang, H.T.[Hao-Tian],
Yang, M.[Meng],
Zheng, N.N.[Nan-Ning],
G2-MonoDepth: A General Framework of Generalized Depth Inference From
Monocular RGB+X Data,
PAMI(46), No. 5, May 2024, pp. 3753-3771.
IEEE DOI
2404
Task analysis, Data models, Estimation, Training, Semantics, Pipelines,
Service robots, Robot, unified model, generalization, depth enhancement
BibRef
Wang, F.[Fei],
Cheng, J.[Jun],
HQDec: Self-Supervised Monocular Depth Estimation Based on a
High-Quality Decoder,
CirSysVideo(34), No. 4, April 2024, pp. 2453-2468.
IEEE DOI
2404
Estimation, Feature extraction, Decoding, Adaptation models, Fuses,
Transformers, Videos, Depth estimation, high-quality decoder,
self-supervised learning
BibRef
Spencer, J.[Jaime],
Hadfield, S.[Simon],
Russell, C.[Chris],
Bowden, R.[Richard],
Kick Back & Relax:
Learning to Reconstruct the World by Watching SlowTV,
ICCV23(15722-15733)
IEEE DOI Code:
WWW Link.
2401
BibRef
Hornauer, J.[Julia],
Holzbock, A.[Adrian],
Belagiannis, V.[Vasileios],
Out-of-Distribution Detection for Monocular Depth Estimation,
ICCV23(1911-1921)
IEEE DOI
2401
BibRef
Cai, S.Q.[Sheng-Qu],
Chan, E.R.[Eric Ryan],
Peng, S.[Songyou],
Shahbazi, M.[Mohamad],
Obukhov, A.[Anton],
Van Gool, L.J.[Luc J.],
Wetzstein, G.[Gordon],
DiffDreamer: Towards Consistent Unsupervised Single-view Scene
Extrapolation with Conditional Diffusion Models,
ICCV23(2139-2150)
IEEE DOI Code:
WWW Link.
2401
BibRef
Guizilini, V.[Vitor],
Vasiljevic, I.[Igor],
Chen, D.[Dian],
Ambrus, R.[Rares],
Gaidon, A.[Adrien],
Towards Zero-Shot Scale-Aware Monocular Depth Estimation,
ICCV23(9199-9209)
IEEE DOI Code:
WWW Link.
2401
BibRef
Mishima, N.[Nao],
Seki, A.[Akihito],
Hiura, S.[Shinsaku],
Self-Supervised Learning for Context-Independent DfD Network using
Multi-View Rank Supervision,
ICIP23(835-839)
IEEE DOI
2312
BibRef
Shin, U.[Ukcheol],
Park, K.Y.[Kwan-Yong],
Lee, B.U.[Byeong-Uk],
Lee, K.[Kyunghyun],
Kweon, I.S.[In So],
Self-supervised Monocular Depth Estimation from Thermal Images via
Adversarial Multi-spectral Adaptation,
WACV23(5787-5796)
IEEE DOI
2302
Training, Data acquisition, Estimation, Self-supervised learning,
Network architecture, Feature extraction,
Applications: Robotics, 3D computer vision
BibRef
Chen, M.H.[Ming-Hui],
Zhang, P.P.[Ping-Ping],
Chen, Z.[Zhuo],
Zhang, Y.[Yun],
Wang, X.[Xu],
Kwong, S.[Sam],
End-To-End Depth Map Compression Framework Via Rgb-To-Depth Structure
Priors Learning,
ICIP22(3206-3210)
IEEE DOI
2211
Image coding, Codecs, Fuses, Redundancy, Rate-distortion,
Feature extraction, Data mining, Depth map compression, feature fusion
BibRef
Lu, J.C.[Jia-Chen],
Zhou, Z.Y.[Zhe-Yuan],
Zhu, X.T.[Xia-Tian],
Xu, H.[Hang],
Zhang, L.[Li],
Learning Ego 3D Representation as Ray Tracing,
ECCV22(XXVI:129-144).
Springer DOI
2211
BibRef
Yu, X.L.[Xuan-Long],
Franchi, G.[Gianni],
Aldea, E.[Emanuel],
On Monocular Depth Estimation and Uncertainty Quantification Using
Classification Approaches for Regression,
ICIP22(1481-1485)
IEEE DOI
2211
Deep learning, Uncertainty, Taxonomy, Estimation, Automobiles,
Depth estimation, Uncertainty Estimation
BibRef
Zhou, K.[Kaichen],
Hong, L.[Lanqing],
Chen, C.[Changhao],
Xu, H.[Hang],
Ye, C.Q.[Chao-Qiang],
Hu, Q.Y.[Qing-Yong],
Li, Z.G.[Zhen-Guo],
DevNet:
Self-supervised Monocular Depth Learning via Density Volume Construction,
ECCV22(XXIX:125-142).
Springer DOI
2211
BibRef
Zhou, Y.[Yunwen],
Kar, A.[Abhishek],
Turner, E.[Eric],
Kowdle, A.[Adarsh],
Guo, C.X.[Chao X.],
DuToit, R.C.[Ryan C.],
Tsotsos, K.[Konstantine],
Learned Monocular Depth Priors in Visual-Inertial Initialization,
ECCV22(XXII:552-570).
Springer DOI
2211
BibRef
Zhou, Z.M.[Zheng-Ming],
Dong, Q.[Qiulei],
Self-distilled Feature Aggregation for Self-supervised Monocular Depth
Estimation,
ECCV22(I:709-726).
Springer DOI
2211
BibRef
Walia, A.[Amanpreet],
Walz, S.[Stefanie],
Bijelic, M.[Mario],
Mannan, F.[Fahim],
Julca-Aguilar, F.[Frank],
Langer, M.[Michael],
Ritter, W.[Werner],
Heide, F.[Felix],
Gated2Gated: Self-Supervised Depth Estimation from Gated Images,
CVPR22(2801-2811)
IEEE DOI
2210
Training, Laser radar, Image resolution, Video sequences, Estimation,
Logic gates, Reflection, 3D from single images,
Self- semi- meta- unsupervised learning
BibRef
Zhao, Z.[Zimeng],
Zuo, B.H.[Bing-Hui],
Xie, W.[Wei],
Wang, Y.G.[Yan-Gang],
Stability-driven Contact Reconstruction From Monocular Color Images,
CVPR22(1633-1643)
IEEE DOI
2210
Shape, Stability criteria, Pipelines, Pattern recognition,
Image reconstruction, Physics, 3D from single images,
Self- semi- meta- unsupervised learning
BibRef
Petrovai, A.[Andra],
Nedevschi, S.[Sergiu],
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for
Improved Monocular Depth Estimation,
CVPR22(1568-1578)
IEEE DOI
2210
Training, Solid modeling, Image resolution, Filtering,
Pose estimation, Self-supervised learning, 3D from single images,
Self- semi- meta- unsupervised learning
BibRef
Kuo, W.C.[Wei-Cheng],
Angelova, A.[Anelia],
Lin, T.Y.[Tsung-Yi],
Dai, A.[Angela],
Patch2CAD: Patchwise Embedding Learning for In-the-Wild Shape
Retrieval from a Single Image,
ICCV21(12569-12579)
IEEE DOI
2203
Geometry, Solid modeling, Shape, Databases, Grounding,
3D from a single image and shape-from-x,
Scene analysis and understanding
BibRef
Persson, P.[Patrik],
Öström, L.[Linn],
Olsson, C.[Carl],
Åström, K.[Kalle],
Parameterization of Ambiguity in Monocular Depth Prediction,
3DV21(761-770)
IEEE DOI
2201
Geometry, Training, Measurement, Image recognition, Neural networks,
Estimation, 3D Reconstruction, Monocular Depth Parameterization,
Machine Learning
BibRef
Hirose, N.[Noriaki],
Taguchi, S.[Shun],
Kawano, K.[Keisuke],
Koide, S.[Satoshi],
Variational Monocular Depth Estimation for Reliability Prediction,
3DV21(637-647)
IEEE DOI
2201
Training, Solid modeling, Uncertainty, Supervised learning,
Estimation, Reliability theory, depth estimation,
self supervised learning
BibRef
Chen, X.Y.[Xing-Yu],
Zhang, R.N.[Ruo-Nan],
Jiang, J.[Ji],
Wang, Y.[Yan],
Li, G.[Ge],
Li, T.H.[Thomas H.],
Self-Supervised Monocular Depth Estimation:
Solving the Edge-Fattening Problem,
WACV23(5765-5775)
IEEE DOI
2302
Measurement, Computational modeling, Estimation, Optimization,
Algorithms: 3D computer vision, Low-level and physics-based vision
BibRef
Li, K.[Keyao],
Li, G.[Ge],
Li, T.H.[Thomas H.],
Rethinking Training Objective for Self-Supervised Monocular Depth
Estimation: Semantic Cues To Rescue,
ICIP21(3308-3312)
IEEE DOI
2201
Training, Integrated optics, Solid modeling, Semantics, Estimation,
Optical variables control, self-supervised learning, semantic cues
BibRef
Jiang, C.W.[Chen-Weinan],
Liu, H.C.[Hai-Chun],
Li, L.Z.[Lan-Zhen],
Pan, C.C.[Chang-Chun],
Attention-Based Self-Supervised Learning Monocular Depth Estimation
With Edge Refinement,
ICIP21(3218-3222)
IEEE DOI
2201
Image edge detection, Neural networks, Brightness, Estimation,
Feature extraction, Videos, self-supervised learning, monocular,
edge refinement
BibRef
Wang, Y.Z.[Yi-Zhi],
Huang, Z.[Zeyu],
Shamir, A.[Ariel],
Huang, H.[Hui],
Zhang, H.[Hao],
Hu, R.Z.[Rui-Zhen],
ARO-Net: Learning Implicit Fields from Anchored Radial Observations,
CVPR23(3572-3581)
IEEE DOI
2309
BibRef
Li, M.Y.[Man-Yi],
Zhang, H.[Hao],
D2IM-Net: Learning Detail Disentangled Implicit Fields from Single
Images,
CVPR21(10241-10250)
IEEE DOI
2111
Surface reconstruction,
Laplace equations, Shape, Decoding, Pattern recognition
BibRef
Bechtold, J.[Jan],
Tatarchenko, M.[Maxim],
Fischer, V.[Volker],
Brox, T.[Thomas],
Fostering Generalization in Single-view 3D Reconstruction by Learning
a Hierarchy of Local and Global Shape Priors,
CVPR21(15875-15884)
IEEE DOI
2111
Training, Shape, Training data, Computer architecture, Network architecture
BibRef
Kluger, F.[Florian],
Ackermann, H.[Hanno],
Brachmann, E.[Eric],
Yang, M.Y.[Michael Ying],
Rosenhahn, B.[Bodo],
Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB
Images,
CVPR21(13065-13074)
IEEE DOI
2111
Training, Measurement, Shape, Annotations, Fitting, Neural networks
BibRef
Lienen, J.[Julian],
Hüllermeier, E.[Eyke],
Ewerth, R.[Ralph],
Nommensen, N.[Nils],
Monocular Depth Estimation via Listwise Ranking using the
Plackett-Luce Model,
CVPR21(14590-14599)
IEEE DOI
2111
Training, Neural networks, Estimation, Training data,
Predictive models, Data models, Probability distribution
BibRef
Yin, W.[Wei],
Zhang, J.M.[Jian-Ming],
Wang, O.[Oliver],
Niklaus, S.[Simon],
Mai, L.[Long],
Chen, S.[Simon],
Shen, C.H.[Chun-Hua],
Learning to Recover 3D Scene Shape from a Single Image,
CVPR21(204-213)
IEEE DOI
2111
Training, Geometry, Shape, Estimation,
Reconstruction algorithms, Predictive models
BibRef
Buquet, J.[Julie],
Zhang, J.S.[Jin-Song],
Roulet, P.[Patrice],
Thibault, S.[Simon],
Lalonde, J.F.[Jean-François],
Evaluating the Impact of Wide-Angle Lens Distortion on Learning-based
Depth Estimation,
OmniCV21(3688-3696)
IEEE DOI
2109
Training, Nonlinear distortion, Neural networks,
Estimation, Tools, Cameras
BibRef
Wang, Y.R.[Yi-Ran],
Li, X.Y.[Xing-Yi],
Shi, M.[Min],
Xian, K.[Ke],
Cao, Z.G.[Zhi-Guo],
Knowledge Distillation for Fast and Accurate Monocular Depth
Estimation on Mobile Devices,
MAI21(2457-2465)
IEEE DOI
2109
Knowledge engineering, Performance evaluation, Training,
Visualization, Neural networks, Estimation
BibRef
Leroy, R.,
Trouvé-Peloux, P.,
Champagnat, F.,
Le Saux, B.,
Carvalho, M.,
Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal
Transport,
MVA21(1-5)
DOI Link
2109
Training, Measurement,
Neural networks, Estimation, Prediction methods
BibRef
Chen, Z.W.[Zi-Wen],
Guo, Z.X.[Zi-Xuan],
Weinman, J.[Jerod],
Improved Point Transformation Methods For Self-Supervised Depth
Prediction,
CRV21(111-118)
IEEE DOI
2108
Learn using stereo pairs.
Training, Machine learning algorithms, Estimation,
Machine learning, Predictive models, Network architecture, neural networks
BibRef
Wang, Y.,
Luo, L.,
Shen, X.,
Mei, X.,
DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues,
3DV20(514-523)
IEEE DOI
2102
Videos, Estimation, Image edge detection,
Training, Optical imaging, Reliability
BibRef
Du, D.,
Zhang, Z.,
Han, X.,
Cui, S.,
Liu, L.,
VIPNet: A Fast and Accurate Single-View Volumetric Reconstruction by
Learning Sparse Implicit Point Guidance,
3DV20(553-562)
IEEE DOI
2102
Shape, Image reconstruction, Topology,
Network topology, Decoding, hybrid shape learning
BibRef
Han, Z.Z.[Zhi-Zhong],
Qiao, G.H.[Guan-Hui],
Liu, Y.S.[Yu-Shen],
Zwicker, M.[Matthias],
SeqXY2SeqZ: Structure Learning for 3d Shapes by Sequentially Predicting
1D Occupancy Segments from 2d Coordinates,
ECCV20(XXIV:607-625).
Springer DOI
2012
BibRef
Wang, J.R.[Jian-Ren],
Fang, Z.Y.[Zhao-Yuan],
GSIR: Generalizable 3d Shape Interpretation and Reconstruction,
ECCV20(XIII:498-514).
Springer DOI
2011
Jointly learn 3D shape interpretation and reconstruction.
BibRef
Li, Y.C.[Yi-Chen],
Mo, K.C.[Kai-Chun],
Shao, L.[Lin],
Sung, M.[Minhyuk],
Guibas, L.J.[Leonidas J.],
Learning 3d Part Assembly from a Single Image,
ECCV20(VI:664-682).
Springer DOI
2011
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
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
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
Huang, J.,
Zhou, Y.,
Funkhouser, T.,
Guibas, L.J.,
FrameNet: Learning Local Canonical Frames of 3D Surfaces From a
Single RGB Image,
ICCV19(8637-8646)
IEEE DOI
2004
augmented reality, computational geometry, computer graphics,
feature extraction, geometry, image colour analysis, Robustness
BibRef
Gadelha, M.[Matheus],
RoyChowdhury, A.[Aruni],
Sharma, G.[Gopal],
Kalogerakis, E.[Evangelos],
Cao, L.L.[Liang-Liang],
Learned-Miller, E.G.[Erik G.],
Wang, R.[Rui],
Maji, S.[Subhransu],
Label-efficient Learning on Point Clouds Using Approximate Convex
Decompositions,
ECCV20(X:473-491).
Springer DOI
2011
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
Zhang, Y.[Yinda],
Wadhwa, N.[Neal],
Orts-Escolano, S.[Sergio],
Häne, C.[Christian],
Fanello, S.[Sean],
Garg, R.[Rahul],
Du2net: Learning Depth Estimation from Dual-cameras and Dual-pixels,
ECCV20(I:582-598).
Springer DOI
2011
BibRef
Garg, R.[Rahul],
Wadhwa, N.[Neal],
Ansari, S.,
Barron, J.,
Learning Single Camera Depth Estimation Using Dual-Pixels,
ICCV19(7627-7636)
IEEE DOI
2004
cameras, image colour analysis, image matching, image sensors,
stereo image processing, supervised learning,
Training
BibRef
Pinheiro, P.O.,
Rostamzadeh, N.,
Ahn, S.,
Domain-Adaptive Single-View 3D Reconstruction,
ICCV19(7637-7646)
IEEE DOI
2004
image reconstruction, image representation,
learning (artificial intelligence),
Decoding
BibRef
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
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
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
Zhi, S.F.[Shuai-Feng],
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
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
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
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
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
Yusiong, J.P.,
Naval, P.,
AsiANet: Autoencoders in Autoencoder for Unsupervised Monocular Depth
Estimation,
WACV19(443-451)
IEEE DOI
1904
image classification, image motion analysis,
learning (artificial intelligence), neural nets,
Network architecture
BibRef
Kumar, A.C.S.[Arun C.S.],
Bhandarkar, S.M.[Suchendra M.],
Prasad, M.[Mukta],
Learning Hierarchical Models for Class-Specific Reconstruction from
Natural Data,
AutoDrive18(1170-11708)
IEEE DOI
1812
Shape, Solid modeling, Image reconstruction, Deformable models,
Strain
BibRef
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
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
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
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
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
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
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
Kurenkov, A.,
Ji, J.,
Garg, A.,
Mehta, V.,
Gwak, J.,
Choy, C.,
Savarese, S.,
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction
from a Single Image,
WACV18(858-866)
IEEE DOI
1806
CAD, augmented reality, image reconstruction,
learning (artificial intelligence), object recognition,
BibRef
Yao, Q.,
Luo, G.,
Zhu, Y.,
Depth estimation for outdoor image using couple dictionary learning
and region detection,
VCIP17(1-4)
IEEE DOI
1804
edge detection, image representation,
image retrieval, learning (artificial intelligence),
single image depth estimation
BibRef
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
Chapter on 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings continues in
Single View 3D Reconstruction, Convolutional Neural Networks, CNN .