Fetching the paper…
Reading the bibliography…
Learning to estimate 3D geometry in a single frame and optical flow from consecutive frames by watching unlabeled videos via deep convolutional network has made significant progress recently.
B. K. Horn and B. G. Schunck, “Determining optical flow,” Artificial intelligence , vol. 17, no. 1-3, pp. 185–203, 1981
1981
Earlier work this paper cites.
M. J. Black and P. Anandan, “The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields,” Computer vision and image understanding , vol. 63, no. 1, pp. 75–104, 1996
1996
Earlier work this paper cites.
S. Vedula, S. Baker, P. Rander, R. Collins, and T. Kanade, “Three-dimensional scene flow,” in Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on , vol. 2. IEEE, 1999, pp. 722–729
1999
Earlier work this paper cites.
G. N. DeSouza and A. C. Kak, “Vision for mobile robot navigation: A survey,” IEEE transactions on pattern analysis and machine intelligence , vol. 24, no. 2, pp. 237–267, 2002
2002
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli et al. , “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
S. Vedula, P. Rander, R. Collins, and T. Kanade, “Three-dimensional scene flow,” IEEE transactions on pattern analysis and machine intelligence , vol. 27, no. 3, pp. 475–480, 2005
2005
Earlier work this paper cites.
E. Prados and O. Faugeras, “Shape from shading,” Handbook of mathematical models in computer vision , pp. 375–388, 2006
2006
Earlier work this paper cites.
A. Saxena, S. H. Chung, and A. Y. Ng, “Learning depth from single monocular images,” in Advances in neural information processing systems , 2006, pp. 1161–1168
2006
Earlier work this paper cites.
D. Hoiem, A. A. Efros, and M. Hebert, “Recovering surface layout from an image.” in ICCV , 2007
2007
Earlier work this paper cites.
L. Torresani, A. Hertzmann, and C. Bregler, “Nonrigid structure-from-motion: Estimating shape and motion with hierarchical priors,” IEEE transactions on pattern analysis and machine intelligence , vol. 30, no. 5, pp. 878–892, 2008
2008
Earlier work this paper cites.
A. Saxena, M. Sun, and A. Y. Ng, “Make3d: Learning 3d scene structure from a single still image,” IEEE transactions on pattern analysis and machine intelligence , vol. 31, no. 5, pp. 824–840, 2009
2009
Earlier work this paper cites.
J. Taylor, A. D. Jepson, and K. N. Kutulakos, “Non-rigid structure from locally-rigid motion,” in Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on . IEEE, 2010, pp. 2761–2768
2010
Earlier work this paper cites.
T. Brox and J. Malik, “Object segmentation by long term analysis of point trajectories,” in European conference on computer vision . Springer, 2010, pp. 282–295
2010
Earlier work this paper cites.
R. A. Newcombe, S. Lovegrove, and A. J. Davison, “DTAM: dense tracking and mapping in real-time,” in ICCV , 2011
2011
Earlier work this paper cites.
C. Wu et al. , “Visualsfm: A visual structure from motion system,” 2011
2011
Earlier work this paper cites.
M. Bleyer, C. Rhemann, and C. Rother, “Patchmatch stereo-stereo matching with slanted support windows.” in Bmvc , vol. 11, 2011, pp. 1–11
2011
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in CVPR , 2012
2012
Earlier work this paper cites.
D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black, “A naturalistic open source movie for optical flow evaluation,” in European Conf. on Computer Vision (ECCV) , ser. Part IV, LNCS 7577, A. Fitzgibbon et al. (Eds.), Ed. Springer-Verlag, Oct. 2012, pp. 611–625
2012
Earlier work this paper cites.
A. G. Schwing, S. Fidler, M. Pollefeys, and R. Urtasun, “Box in the box: Joint 3d layout and object reasoning from single images,” in ICCV , 2013
2013
Earlier work this paper cites.
C. Vogel, K. Schindler, and S. Roth, “Piecewise rigid scene flow,” in Computer Vision (ICCV), 2013 IEEE International Conference on . IEEE, 2013, pp. 1377–1384
2013
Earlier work this paper cites.
D. Eigen, C. Puhrsch, and R. Fergus, “Depth map prediction from a single image using a multi-scale deep network,” in NIPS , 2014
2014
Earlier work this paper cites.
J. Engel, T. Schöps, and D. Cremers, “Lsd-slam: Large-scale direct monocular slam,” in ECCV , 2014
2014
Earlier work this paper cites.
Y. Dai, H. Li, and M. He, “A simple prior-free method for non-rigid structure-from-motion factorization,” International Journal of Computer Vision , vol. 107, no. 2, pp. 101–122, 2014
2014
Earlier work this paper cites.
F. Srajer, A. G. Schwing, M. Pollefeys, and T. Pajdla, “Match box: Indoor image matching via box-like scene estimation,” in 3DV , 2014
2014
Earlier work this paper cites.
K. Karsch, C. Liu, and S. B. Kang, “Depth transfer: Depth extraction from video using non-parametric sampling,” IEEE transactions on pattern analysis and machine intelligence , vol. 36, no. 11, pp. 2144–2158, 2014
2014
Earlier work this paper cites.
L. Ladicky, J. Shi, and M. Pollefeys, “Pulling things out of perspective,” in CVPR , 2014
2014
Earlier work this paper cites.
B. L. Ladicky, Zeisl, M. Pollefeys et al. , “Discriminatively trained dense surface normal estimation,” in ECCV , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
A. Faktor and M. Irani, “Video segmentation by non-local consensus voting.” in BMVC , vol. 2, no. 7, 2014, p. 8
2014
Earlier work this paper cites.
2014
Cited alongside, same era.
M. Menze and A. Geiger, “Object scene flow for autonomous vehicles,” in CVPR , 2015
2015
Cited alongside, same era.
R. Mur-Artal, J. M. M. Montiel, and J. D. Tardos, “Orb-slam: a versatile and accurate monocular slam system,” IEEE Transactions on Robotics , vol. 31, no. 5, pp. 1147–1163, 2015
2015
Cited alongside, same era.
N. Kong and M. J. Black, “Intrinsic depth: Improving depth transfer with intrinsic images,” in ICCV , 2015
2015
Cited alongside, same era.
X. Wang, D. Fouhey, and A. Gupta, “Designing deep networks for surface normal estimation,” in CVPR , 2015
2015
Cited alongside, same era.
Z. Ren, J. Yan, B. Ni, B. Liu, X. Yang, and H. Zha, “Unsupervised deep learning for optical flow estimation.” in AAAI , 2017, pp. 1495–1501
2017
Later among the works it cites.
S. Kumar, Y. Dai, and H. Li, “Monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames,” ICCV , 2017
2017
Later among the works it cites.
J. Li, R. Klein, and A. Yao, “A two-streamed network for estimating fine-scaled depth maps from single rgb images,” in ICCV , 2017
2017
Later among the works it cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969
2017
Later among the works it cites.
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox, “Flownet 2.0: Evolution of optical flow estimation with deep networks,” in CVPR , 2017. [Online]. Available: http://lmb.informatik.uni-freiburg.de//Publications/2017/IMKDB17
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. Eigen and R. Fergus, “Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture,” in ICCV , 2015
2015
Cited alongside, same era.
P. Wang, X. Shen, Z. Lin, S. Cohen, B. L. Price, and A. L. Yuille, “Towards unified depth and semantic prediction from a single image,” in CVPR , 2015
2015
Cited alongside, same era.
F. Liu, C. Shen, and G. Lin, “Deep convolutional neural fields for depth estimation from a single image,” in CVPR , June 2015
2015
Cited alongside, same era.
B. Li, C. Shen, Y. Dai, A. van den Hengel, and M. He, “Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs,” in CVPR , 2015
2015
Cited alongside, same era.
A. Dosovitskiy, P. Fischer, E. Ilg, P. Häusser, C. Hazırbaş, V. Golkov, P. v.d. Smagt, D. Cremers, and T. Brox, “Flownet: Learning optical flow with convolutional networks,” in IEEE International Conference on Computer Vision (ICCV) , 2015. [Online]. Available: http://lmb.informatik.uni-freiburg.de/Publications/2015/DFIB15
2015
Cited alongside, same era.
K. Fragkiadaki, P. Arbelaez, P. Felsen, and J. Malik, “Learning to segment moving objects in videos,” in CVPR , 2015, pp. 4083–4090
2015
Cited alongside, same era.
J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid, “Epicflow: Edge-preserving interpolation of correspondences for optical flow,” in CVPR , 2015, pp. 1164–1172
2015
Cited alongside, same era.
2017
Later among the works it cites.
A. Behl, O. H. Jafari, S. K. Mustikovela, H. A. Alhaija, C. Rother, and A. Geiger, “Bounding boxes, segmentations and object coordinates: How important is recognition for 3d scene flow estimation in autonomous driving scenarios?” in CVPR , 2017
2017
Later among the works it cites.
J. S. Yoon, F. Rameau, J. Kim, S. Lee, S. Shin, and I. S. Kweon, “Pixel-level matching for video object segmentation using convolutional neural networks,” in 2017 IEEE International Conference on Computer Vision (ICCV) . IEEE, 2017, pp. 2186–2195
2017
Later among the works it cites.
Y. Kuznietsov, J. Stuckler, and B. Leibe, “Semi-supervised deep learning for monocular depth map prediction,” 2017
2017
Later among the works it cites.
Z. Yang, P. Wang, Y. Wang, W. Xu, and R. Nevatia, “Lego: Learning edge with geometry all at once by watching videos,” in CVPR , 2018
2018
Closest in time.
Y. Wang, Y. Yang, Z. Yang, P. Wang, L. Zhao, and W. Xu, “Occlusion aware unsupervised learning of optical flow,” in CVPR , 2018
2018
Closest in time.
Z. Yang, P. Wang, Y. Wang, W. Xu, and R. Nevatia, “Every pixel counts: Unsupervised geometry learning with holistic 3d motion understanding,” ECCV Workshop of VNAD , 2018
2018
Closest in time.
Z. Yang, P. Wang, W. Xu, L. Zhao, and N. Ram, “Unsupervised learning of geometry from videos with edge-aware depth-normal consistency,” in AAAI , 2018
2018
Closest in time.
Z. Yin and J. Shi, “Geonet: Unsupervised learning of dense depth, optical flow and camera pose,” CVPR , 2018
2018
Closest in time.
2018
Closest in time.
Y. Zou, Z. Luo, and J.-B. Huang, “Df-net: Unsupervised joint learning of depth and flow using cross-task consistency,” in European Conference on Computer Vision , 2018
2018
Closest in time.
2018
Closest in time.
X. Cheng, P. Wang, and R. Yang, “Depth estimation via affinity learned with convolutional spatial propagation network,” ECCV , 2018
2018
Closest in time.
C. Wang, J. M. Buenaposada, R. Zhu, and S. Lucey, “Learning depth from monocular videos using direct methods,” in CVPR , 2018
2018
Closest in time.
R. Li, S. Wang, Z. Long, and D. Gu, “Undeepvo: Monocular visual odometry through unsupervised deep learning,” ICRA , 2018
2018
Closest in time.
R. Mahjourian, M. Wicke, and A. Angelova, “Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints,” CVPR , 2018
2018
Closest in time.
D. Sun, X. Yang, M.-Y. Liu, and J. Kautz, “Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8934–8943
2018
Closest in time.
S. Meister, J. Hur, and S. Roth, “UnFlow: Unsupervised learning of optical flow with a bidirectional census loss,” in AAAI , 2018
2018
Closest in time.
W. Wang, J. Shen, R. Yang, and F. Porikli, “Saliency-aware video object segmentation,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 1, pp. 20–33, 2018
2018
Closest in time.
J. Janai, F. Guney, A. Ranjan, M. Black, and A. Geiger, “Unsupervised learning of multi-frame optical flow with occlusions,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 690–706
2018
Closest in time.
V. Casser, S. Pirk, R. Mahjourian, and A. Angelova, “Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos,” in AAAI , 2019
2019
Closest in time.
Y. Wang, P. Wang, Z. Yang, C. Luo, Y. Yang, and W. Xu, “Unos: Unified unsupervised optical-flow and stereo-depth estimation by watching videos,” in CVPR , 2019
2019
Closest in time.
P. Tokmakov, C. Schmid, and K. Alahari, “Learning to segment moving objects,” International Journal of Computer Vision , vol. 127, no. 3, pp. 282–301, 2019
2019
Closest in time.
C. Wang, J. M. Buenaposada, R. Zhu, and S. Lucey, “Learning depth from monocular videos using direct methods,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 2022–2030
2030
Closest in time.