Fetching the paper…
Reading the bibliography…
Self-supervised monocular depth estimation has shown impressive results in static scenes.
Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli et al. , “Image Quality Assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. , vol. 13, no. 4, 2004
2004
Earlier work this paper cites.
H. Hirschmuller, “Accurate and efficient stereo processing by semi-global matching and mutual information,” in IEEE Conf. Comput. Vis. Pattern Recog. , vol. 2, 2005, pp. 807–814
2005
Earlier work this paper cites.
A. Saxena, S. H. Chung, and A. Y. Ng, “Learning depth from single monocular images,” in Adv. Neural Inform. Process. Syst. , 2006
2006
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2009
2009
Earlier work this paper cites.
R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon, “Kinectfusion: Real-time dense surface mapping and tracking,” in IEEE international symposium on mixed and augmented reality . IEEE, 2011, pp. 127–136
2011
Earlier work this paper cites.
H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao, “Deep ordinal regression network for monocular depth estimation,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2018, pp. 2002–2011
2011
Earlier work this paper cites.
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, “Indoor segmentation and support inference from rgbd images,” in Eur. Conf. Comput. Vis. , 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 Eur. Conf. Comput. Vis. , Oct. 2012, pp. 611–625
2012
Earlier work this paper cites.
J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, “A benchmark for the evaluation of rgb-d slam systems,” in Int. Conf. Intelligent Robots and Systems , 2012
2012
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets Robotics: The kitti dataset,” International Journal of Robotics Research (IJRR) , 2013
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 Adv. Neural Inform. Process. Syst. , 2014
2014
Earlier work this paper cites.
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 Trans. Pattern Anal. Mach. Intell. , 2014
2014
Earlier work this paper cites.
M. Liu, M. Salzmann, and X. He, “Discrete-continuous depth estimation from a single image,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2014
2014
Earlier work this paper cites.
L. Ladicky, J. Shi, and M. Pollefeys, “Pulling things out of perspective,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2014
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) . Springer, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
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 IEEE Conf. Comput. Vis. Pattern Recog. , 2015
2015
Earlier work this paper cites.
P. Wang, X. Shen, Z. Lin, S. Cohen, B. Price, and A. L. Yuille, “Towards unified depth and semantic prediction from a single image,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2015
2015
Earlier work this paper cites.
D. Eigen and R. Fergus, “Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture,” in Int. Conf. Comput. Vis. , 2015
2015
Earlier work this paper cites.
F. Liu, C. Shen, G. Lin, and I. Reid, “Learning depth from single monocular images using deep convolutional neural fields,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 38, no. 10, 2016
2016
Earlier work this paper cites.
R. Garg, V. K. BG, G. Carneiro, and I. Reid, “Unsupervised cnn for single view depth estimation: Geometry to the rescue,” in Eur. Conf. Comput. Vis. Springer, 2016
2016
Earlier work this paper cites.
W. Chen, Z. Fu, D. Yang, and J. Deng, “Single-image depth perception in the wild,” Adv. Neural Inform. Process. Syst. , 2016
2016
Earlier work this paper cites.
J. L. Schönberger, E. Zheng, M. Pollefeys, and J.-M. Frahm, “Pixelwise view selection for unstructured multi-view stereo,” in Eur. Conf. Comput. Vis. , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2016, pp. 770–778
2016
Earlier work this paper cites.
A. Roy and S. Todorovic, “Monocular depth estimation using neural regression forest,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2016
2016
Cited alongside, same era.
A. Chakrabarti, J. Shao, and G. Shakhnarovich, “Depth from a single image by harmonizing overcomplete local network predictions,” in Adv. Neural Inform. Process. Syst. , 2016
2016
Cited alongside, same era.
S.-J. Park, K.-S. Hong, and S. Lee, “Rdfnet: Rgb-d multi-level residual feature fusion for indoor semantic segmentation,” in Int. Conf. Comput. Vis. , 2017, pp. 4980–4989
2017
Cited alongside, same era.
T. Zhou, M. Brown, N. Snavely, and D. G. Lowe, “Unsupervised learning of depth and ego-motion from video,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2017
2017
Cited alongside, same era.
C. Godard, O. Mac Aodha, and G. J. Brostow, “Unsupervised monocular depth estimation with left-right consistency,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2017
V. Guizilini, R. Ambrus, S. Pillai, A. Raventos, and A. Gaidon, “3d packing for self-supervised monocular depth estimation,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2020
2020
Later among the works it cites.
V. Guizilini, R. Hou, J. Li, R. Ambrus, and A. Gaidon, “Semantically-guided representation learning for self-supervised monocular depth,” in Int. Conf. Learn. Represent. , 2020
2020
Later among the works it cites.
M. Klingner, J.-A. Termöhlen, J. Mikolajczyk, and T. Fingscheidt, “Self-supervised monocular depth estimation: Solving the dynamic object problem by semantic guidance,” in Eur. Conf. Comput. Vis. Springer, 2020, pp. 582–600
2020
Later among the works it cites.
H. Li, A. Gordon, H. Zhao, V. Casser, and A. Angelova, “Unsupervised monocular depth learning in dynamic scenes,” in Conference on Robot Learning , 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in NIPS-W , 2017
2017
Cited alongside, same era.
J. Li, R. Klein, and A. Yao, “A two-streamed network for estimating fine-scaled depth maps from single rgb images,” in Int. Conf. Comput. Vis. , 2017
2017
Cited alongside, same era.
Z. Yin and J. Shi, “GeoNet: Unsupervised learning of dense depth, optical flow and camera pose,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2018
2018
Cited alongside, same era.
Y. Zou, Z. Luo, and J.-B. Huang, “DF-Net: Unsupervised joint learning of depth and flow using cross-task consistency,” in Eur. Conf. Comput. Vis. , 2018
2018
Cited alongside, same era.
H. Zhan, R. Garg, C. Saroj Weerasekera, K. Li, H. Agarwal, and I. Reid, “Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2018
2018
Cited alongside, same era.
R. Mahjourian, M. Wicke, and A. Angelova, “Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2018
2018
Cited alongside, same era.
K. Xian, C. Shen, Z. Cao, H. Lu, Y. Xiao, R. Li, and Z. Luo, “Monocular relative depth perception with web stereo data supervision,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2018, pp. 311–320
2018
Cited alongside, same era.
W. Yin, J. Zhang, O. Wang, S. Niklaus, L. Mai, S. Chen, and C. Shen, “Learning to recover 3d scene shape from a single image,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2020
2020
Later among the works it cites.
R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, “Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,” IEEE Trans. Pattern Anal. Mach. Intell. , 2020
2020
Later among the works it cites.
X. Luo, J.-B. Huang, R. Szeliski, K. Matzen, and J. Kopf, “Consistent video depth estimation,” ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH) , 2020
2020
Later among the works it cites.
K. Xian, J. Zhang, O. Wang, L. Mai, Z. Lin, and Z. Cao, “Structure-guided ranking loss for single image depth prediction,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2020
2020
Later among the works it cites.
T. Koch, L. Liebel, M. Körner, and F. Fraundorfer, “Comparison of monocular depth estimation methods using geometrically relevant metrics on the ibims-1 dataset,” Computer Vision and Image Understanding , 2020
2020
Later among the works it cites.
W. Zhao, S. Liu, Y. Shu, and Y.-J. Liu, “Towards better generalization: Joint depth-pose learning without posenet,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2020
2020
Later among the works it cites.
Z. Yu, L. Jin, and S. Gao, “P 2
2020
Later among the works it cites.
J. Lambert, Z. Liu, O. Sener, J. Hays, and V. Koltun, “Mseg: A composite dataset for multi-domain semantic segmentation,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2020, pp. 2879–2888
2020
Later among the works it cites.
J.-W. Bian, H. Zhan, N. Wang, Z. Li, L. Zhang, C. Shen, M.-M. Cheng, and I. Reid, “Unsupervised scale-consistent depth learning from video,” Int. J. Comput. Vis. , 2021
2021
Later among the works it cites.
T. Zhou, D.-P. Fan, M.-M. Cheng, J. Shen, and L. Shao, “Rgb-d salient object detection: A survey,” Computational Visual Media , pp. 1–33, 2021
2021
Later among the works it cites.
J.-W. Bian, H. Zhan, and I. Reid, “Nvss: High-quality novel view selfie synthesis,” in International Conference on 3D Vision (3DV) , 2021
2021
Later among the works it cites.
S. Lee, S. Im, S. Lin, and I. S. Kweon, “Learning monocular depth in dynamic scenes via instance-aware projection consistency,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , 2021
2021
Later among the works it cites.
W. Yin, J. Zhang, O. Wang, S. Niklaus, L. Mai, S. Chen, and C. Shen, “Learning to recover 3d scene shape from a single image,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2021, pp. 204–213
2021
Later among the works it cites.
J.-W. Bian, H. Zhan, N. Wang, T.-J. Chin, C. Shen, and I. Reid, “Auto-rectify network for unsupervised indoor depth estimation,” IEEE Trans. Pattern Anal. Mach. Intell. , 2021
2021
Later among the works it cites.
R. Ranftl, A. Bochkovskiy, and V. Koltun, “Vision transformers for dense prediction,” ArXiv preprint , 2021
2021
Later among the works it cites.
P. Ji, R. Li, B. Bhanu, and Y. Xu, “Monoindoor: Towards good practice of self-supervised monocular depth estimation for indoor environments,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2021
2021
Later among the works it cites.
Z. Feng, L. Yang, L. Jing, H. Wang, Y. Tian, and B. Li, “Disentangling object motion and occlusion for unsupervised multi-frame monocular depth,” in Eur. Conf. Comput. Vis. Springer, 2022, pp. 228–244
2022
Closest in time.
R. Wang, Z. Yu, and S. Gao, “Planedepth: Self-supervised depth estimation via orthogonal planes,” in IEEE Conf. Comput. Vis. Pattern Recog. , June 2023, pp. 21 425–21 434
2023
Closest in time.
H. Si, B. Zhao, D. Wang, Y. Gao, M. Chen, Z. Wang, and X. Li, “Fully self-supervised depth estimation from defocus clue,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2023, pp. 9140–9149
2023
Closest in time.
A. Bangunharcana, A. M. Aly, and K. Kim, “Dualrefine: Self-supervised depth and pose estimation through iterative epipolar sampling and refinement toward equilibrium,” in IEEE Conf. Comput. Vis. Pattern Recog. IEEE, 2023
2023
Closest in time.
Z. Li and N. Snavely, “Megadepth: Learning single-view depth prediction from internet photos,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2018, pp. 2041–2050
2050
Closest in time.