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Ji, D.H.[Ding-Huang],
Dunn, E.[Enrique],
Frahm, J.M.[Jan-Michael],
Self-Expressive Dictionary Learning for Dynamic 3D Reconstruction,
PAMI(40), No. 9, September 2018, pp. 2223-2237.
IEEE DOI
1808
BibRef
Earlier:
Sparse Dynamic 3D Reconstruction from Unsynchronized Videos,
ICCV15(4435-4443)
IEEE DOI
1602
BibRef
Earlier: A2, A3, A4, Only:
3D Reconstruction of Dynamic Textures in Crowd Sourced Data,
ECCV14(I: 143-158).
Springer DOI
1408
Trajectory, Image reconstruction,
Cameras, Sequential analysis, Dynamics, Streaming media,
dynamic 3D reconstruction
BibRef
Ji, D.H.[Ding-Huang],
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Frahm, J.M.[Jan-Michael],
Spatio-Temporally Consistent Correspondence for Dense Dynamic Scene
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1611
BibRef
Schonberger, J.L.[Johannes L.],
Frahm, J.M.[Jan-Michael],
Structure-from-Motion Revisited,
CVPR16(4104-4113)
IEEE DOI
1612
BibRef
Schonberger, J.L.[Johannes L.],
Radenovic, F.[Filip],
Chum, O.[Ondrej],
Frahm, J.M.[Jan-Michael],
From single image query to detailed 3D reconstruction,
CVPR15(5126-5134)
IEEE DOI
1510
BibRef
Schonberger, J.L.[Johannes L.],
Berg, A.C.[Alexander C.],
Frahm, J.M.[Jan-Michael],
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GCPR15(53-64).
Springer DOI
1511
Award, GCPR, HM.
BibRef
And:
PAIGE: PAirwise Image Geometry Encoding for improved efficiency in
Structure-from-Motion,
CVPR15(1009-1018)
IEEE DOI
1510
BibRef
Heinly, J.[Jared],
Schonberger, J.L.[Johannes L.],
Dunn, E.[Enrique],
Frahm, J.M.[Jan-Michael],
Reconstructing the world* in six days,
CVPR15(3287-3295)
IEEE DOI
1510
Large scale for millions of images.
BibRef
Heinly, J.[Jared],
Dunn, E.[Enrique],
Frahm, J.M.[Jan-Michael],
Recovering Correct Reconstructions from Indistinguishable Geometry,
3DV14(377-384)
IEEE DOI
1503
BibRef
And:
Correcting for Duplicate Scene Structure in Sparse 3D Reconstruction,
ECCV14(IV: 780-795).
Springer DOI
1408
Cameras.
repeating structures.
BibRef
Mandal, M.,
Dhar, V.,
Mishra, A.,
Vipparthi, S.K.,
3DFR: A Swift 3D Feature Reductionist Framework for Scene Independent
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SPLetters(26), No. 12, December 2019, pp. 1882-1886.
IEEE DOI
2001
feature extraction, image motion analysis, image sequences,
learning (artificial intelligence), neural nets, reductionist
BibRef
Zhou, H.Z.[Hui-Zhong],
Ummenhofer, B.[Benjamin],
Brox, T.[Thomas],
DeepTAM: Deep Tracking and Mapping with Convolutional Neural Networks,
IJCV(128), No. 3, March 2020, pp. 756-769.
Springer DOI
2003
Code, Tracking.
WWW Link.
BibRef
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Nadaoka, K.[Kazuo],
Nakamura, T.[Takashi],
Towards Benthic Habitat 3D Mapping Using Machine Learning Algorithms
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RS(12), No. 1, 2020, pp. xx-yy.
DOI Link
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BibRef
Gu, S.H.[Shu-Hang],
Guo, S.[Shi],
Zuo, W.M.[Wang-Meng],
Chen, Y.J.[Yun-Jin],
Timofte, R.[Radu],
Van Gool, L.J.[Luc J.],
Zhang, L.[Lei],
Learned Dynamic Guidance for Depth Image Reconstruction,
PAMI(42), No. 10, October 2020, pp. 2437-2452.
IEEE DOI
2009
Task analysis, Analytical models, Optimization,
Image reconstruction, Training data, Data models, Network architecture
BibRef
Jin, F.S.[Fu-Sheng],
Zhao, Y.[Yu],
Wan, C.B.[Chuan-Bing],
Yuan, Y.[Ye],
Wang, S.[Shuliang],
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Corresponding Constraints,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link
2105
BibRef
Liu, M.Y.[Meng-Yi],
Wang, S.H.[Shu-Hui],
Guo, Y.L.[Yu-Lan],
He, Y.[Yuan],
Xue, H.[Hui],
Pano-SfMLearner: Self-Supervised Multi-Task Learning of Depth and
Semantics in Panoramic Videos,
SPLetters(28), 2021, pp. 832-836.
IEEE DOI
2105
BibRef
Li, Z.Q.[Zheng-Qi],
Dekel, T.[Tali],
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Tucker, R.[Richard],
Snavely, N.[Noah],
Liu, C.[Ce],
Freeman, W.T.[William T.],
MannequinChallenge: Learning the Depths of Moving People by Watching
Frozen People,
PAMI(43), No. 12, December 2021, pp. 4229-4241.
IEEE DOI
2112
BibRef
Earlier:
Learning the Depths of Moving People by Watching Frozen People,
CVPR19(4516-4525).
IEEE DOI
2002
Award, CVPR, HM. Cameras, Visualization, Internet,
Image reconstruction, Natural languages, Training data, Navigation,
dynamic scene reconstruction
BibRef
Nunes, U.M.[Urbano Miguel],
Demiris, Y.F.[Yi-Fannis],
Robust Event-Based Vision Model Estimation by Dispersion Minimisation,
PAMI(44), No. 12, December 2022, pp. 9561-9573.
IEEE DOI
2212
BibRef
Earlier:
Entropy Minimisation Framework for Event-based Vision Model Estimation,
ECCV20(V:161-176).
Springer DOI
2011
BibRef
And:
Online Unsupervised Learning of the 3D Kinematic Structure of
Arbitrary Rigid Bodies,
ICCV19(3808-3816)
IEEE DOI
2004
Dispersion, Estimation, Cameras, Optical imaging, Optimization,
Computational modeling, Motion estimation, Event-based vision,
real-time motion estimation.
feature extraction, image colour analysis, image motion analysis,
unsupervised learning, online unsupervised learning, Tracking
BibRef
Xie, Y.M.[Yi-Ming],
Gadelha, M.[Matheus],
Yang, F.T.[Feng-Ting],
Zhou, X.W.[Xiao-Wei],
Jiang, H.[Huaizu],
PlanarRecon: Realtime 3D Plane Detection and Reconstruction from
Posed Monocular Videos,
CVPR22(6209-6218)
IEEE DOI
2210
Geometry, Image analysis, Fuses, Neural networks, Streaming media,
Real-time systems, 3D from multi-view and sensors,
Scene analysis and understanding
BibRef
Gomes, P.[Pedro],
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Toni, L.[Laura],
Spatio-Temporal Graph-RNN for Point Cloud Prediction,
ICIP21(3428-3432)
IEEE DOI
2201
Geometry, Correlation, Network topology, Simulation, Dynamics,
Transform coding, Point Cloud, Point-based models
BibRef
Li, S.Y.[Si-Yuan],
Luo, Y.[Yue],
Zhu, Y.[Ye],
Zhao, X.[Xun],
Li, Y.[Yu],
Shan, Y.[Ying],
Enforcing Temporal Consistency in Video Depth Estimation,
PBDL21(1145-1154)
IEEE DOI
2112
Measurement, Estimation,
Benchmark testing, Stability analysis, Computational efficiency
BibRef
Graham, B.,
Novotny, D.,
RidgeSfM: Structure from Motion via Robust Pairwise Matching Under
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3DV20(652-662)
IEEE DOI
2102
Cameras, Pipelines, Image reconstruction, Bundle adjustment,
Deep learning, Standards, Task analysis, RidgeSfM,
monocular depth prediction
BibRef
Liu, H.,
Hua, G.,
Huang, W.,
Motion Rectification Network for Unsupervised Learning of Monocular
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ICIP20(2805-2809)
IEEE DOI
2011
Cameras, Dynamics, Pipelines,
Unsupervised learning, Motion estimation.
BibRef
Moreau, A.,
Mancas, M.,
Dutoit, T.,
Unsupervised depth prediction from monocular sequences:
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CRV20(54-61)
IEEE DOI
2006
Monocular depth estimation,
Instance segmentation, Multi-task learning
BibRef
Wu, Z.,
Wu, X.,
Zhang, X.,
Wang, S.,
Ju, L.,
Spatial Correspondence With Generative Adversarial Network:
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ICCV19(7493-7503)
IEEE DOI
2004
cameras, image matching, image motion analysis, image sequences,
learning (artificial intelligence), mobile robots,
Chebyshev approximation
BibRef
Gordon, A.,
Li, H.,
Jonschkowski, R.,
Angelova, A.,
Depth From Videos in the Wild: Unsupervised Monocular Depth Learning
From Unknown Cameras,
ICCV19(8976-8985)
IEEE DOI
2004
cameras, image motion analysis, multimedia Web sites,
object detection, social networking (online),
BibRef
Zhou, J.,
Wang, Y.,
Qin, K.,
Zeng, W.,
Unsupervised High-Resolution Depth Learning From Videos With Dual
Networks,
ICCV19(6871-6880)
IEEE DOI
2004
image resolution, image sampling, image texture, neural nets,
stereo image processing, unsupervised learning, Network architecture
BibRef
Zhang, H.K.[Hao-Kui],
Li, Y.[Ying],
Cao, Y.Z.[Yuan-Zhouhan],
Liu, Y.[Yu],
Shen, C.H.[Chun-Hua],
Yan, Y.L.[You-Liang],
Exploiting Temporal Consistency for Real-Time Video Depth Estimation,
ICCV19(1725-1734)
IEEE DOI
2004
Code, Depth from Motion.
WWW Link. convolutional neural nets, learning (artificial intelligence),
video signal processing,
Streaming media
BibRef
Liu, C.[Chao],
Gu, J.[Jinwei],
Kim, K.[Kihwan],
Narasimhan, S.G.[Srinivasa G.],
Kautz, J.[Jan],
Neural RGB(r)D Sensing: Depth and Uncertainty From a Video Camera,
CVPR19(10978-10987).
IEEE DOI
2002
BibRef
Perrone, J.A.[John A.],
Cree, M.J.[Michael J.],
Hedayati, M.[Mohammad],
Using the Properties of Primate Motion Sensitive Neurons to Extract
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CAIP19(I:600-612).
Springer DOI
1909
BibRef
Gwn, K.,
Reddy, K.,
Giering, M.,
Bernal, E.A.,
Generative Adversarial Networks for Depth Map Estimation from RGB
Video,
PBVS18(1258-12588)
IEEE DOI
1812
Cameras, Estimation, Sensors, Optical imaging, Laser radar, Training
BibRef
Wang, F.E.[Fu-En],
Hu, H.N.[Hou-Ning],
Cheng, H.T.[Hsien-Tzu],
Lin, J.T.[Juan-Ting],
Yang, S.T.[Shang-Ta],
Shih, M.L.[Meng-Li],
Chu, H.K.[Hung-Kuo],
Sun, M.[Min],
Self-supervised Learning of Depth and Camera Motion from 360° Videos,
ACCV18(V:53-68).
Springer DOI
1906
BibRef
Pinard, C.[Clément],
Chevalley, L.[Laure],
Manzanera, A.[Antoine],
Filliat, D.[David],
Learning Structure-from-Motion from Motion,
DeepLearn-G18(III:363-376).
Springer DOI
1905
BibRef
Klodt, M.[Maria],
Vedaldi, A.[Andrea],
Supervising the New with the Old: Learning SFM from SFM,
ECCV18(X: 713-728).
Springer DOI
1810
BibRef
Ummenhofer, B.[Benjamin],
Zhou, H.Z.[Hui-Zhong],
Uhrig, J.,
Mayer, N.,
Ilg, E.,
Dosovitskiy, A.,
Brox, T.[Thomas],
DeMoN: Depth and Motion Network for Learning Monocular Stereo,
CVPR17(5622-5631)
IEEE DOI
1711
Adaptive optics, Cameras, Computer architecture, Estimation,
Optical fiber networks, Optical imaging, Training
BibRef
Schöning, J.[Julius],
Behrens, T.[Thea],
Faion, P.[Patrick],
Kheiri, P.[Peyman],
Heidemann, G.[Gunther],
Krumnack, U.[Ulf],
Structure from Motion by Artificial Neural Networks,
SCIA17(I: 146-158).
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1706
BibRef
Schöning, J.[Julius],
Heidemann, G.[Gunther],
Bio-Inspired Architecture for Deriving 3D Models from Video Sequences,
3DModelApp16(II: 62-76).
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1704
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Kumar, A.C.S.[Arun C. S.],
Bódis-Szomorú, A.[András],
Bhandarkar, S.[Suchendra],
Prasad, M.[Mukta],
Class-Specific Object Pose Estimation and Reconstruction Using 3D Part
Geometry,
DeepLearn16(III: 280-295).
Springer DOI
1611
BibRef
Scheer, J.[Jonas],
Fritz, M.[Mario],
Grau, O.[Oliver],
Learning to Select Long-Track Features for Structure-From-Motion and
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GCPR16(402-413).
Springer DOI
1611
BibRef
Banterle, F.[Francesco],
Gong, R.[Rui],
Corsini, M.[Massimiliano],
Ganovelli, F.[Fabio],
Van Gool, L.J.[Luc J.],
Cignoni, P.[Paolo],
A Deep Learning Method for Frame Selection in Videos for Structure
from Motion Pipelines,
ICIP21(3667-3671)
IEEE DOI
2201
Time-frequency analysis, Structure from motion, Video sequences,
Pipelines, Computer architecture, Streaming media,
Video Processing
BibRef
Breitenstein, M.D.,
Sommerlade, E.,
Leibe, B.,
Van Gool, L.J.,
Reid, I.D.,
Probabilistic Parameter Selection for Learning Scene Structure from
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BMVC08(xx-yy).
PDF File.
0809
BibRef
Tagawa, N.[Norio],
Kawaguchi, J.[Junya],
Naganuma, S.[Shoichi],
Okubo, K.[Kan],
Direct 3-D shape recovery from image sequence based on multi-scale
Bayesian network,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Sun, Y.,
Bayoumi, M.M.,
A simple feedforward neural network architecture for 3-D motion and
structure estimation,
ICIP96(III: 783-786).
IEEE DOI
9610
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Hand-Held Camera Reconstruction, Phone Based Reconstruction, Shape from Motion .