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Accurate depth estimation under adverse night conditions has practical impact and applications, such as on autonomous driving and rescue robots.
N. Kanopoulos, N. Vasanthavada, and R. L. Baker, “Design of an image edge detection filter using the sobel operator,” IEEE Journal of solid-state circuits , vol. 23, no. 2, pp. 358–367, 1988
1988
Earlier work this paper cites.
P. Lichtsteiner and C. Posch, “C. and t. delbruck,“an 128 × \times 128 120 db 15 μ \mu s latency temporal contrast vision sensor,” IEEE Journal of Solid State Circuits , vol. 43, pp. 566–576, 2008
2008
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,” Advances in neural information processing systems , vol. 27, 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 . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
R. Ranftl, V. Vineet, Q. Chen, and V. Koltun, “Dense monocular depth estimation in complex dynamic scenes,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4058–4066
2016
Earlier work this paper cites.
C. Godard, O. Mac Aodha, and G. J. Brostow, “Unsupervised monocular depth estimation with left-right consistency,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017
2017
Earlier work this paper cites.
K. Tateno, F. Tombari, I. Laina, and N. Navab, “Cnn-slam: Real-time dense monocular slam with learned depth prediction,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 6243–6252
2017
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning . PMLR, 2017, pp. 1–16
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Z. Zhu, Y. Chen, and K. Daniilidis, “Realtime time synchronized event-based stereo,” in Proceedings of the European Conference on Computer Vision (ECCV) , September 2018
2018
Earlier work this paper cites.
P. Zama Ramirez, M. Poggi, F. Tosi, S. Mattoccia, and L. Di Stefano, “Geometry meets semantics for semi-supervised monocular depth estimation,” in Asian Conference on Computer Vision . Springer, 2018, pp. 298–313
2018
Earlier work this paper cites.
M. Jaritz, R. De Charette, E. Wirbel, X. Perrotton, and F. Nashashibi, “Sparse and dense data with cnns: Depth completion and semantic segmentation,” in 2018 International Conference on 3D Vision (3DV) . IEEE, 2018, pp. 52–60
2018
Earlier work this paper cites.
Y. Zhou, G. Gallego, H. Rebecq, L. Kneip, H. Li, and D. Scaramuzza, “Semi-dense 3d reconstruction with a stereo event camera,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 235–251
2018
Earlier work this paper cites.
C. Godard, O. Mac Aodha, M. Firman, and G. J. Brostow, “Digging into self-supervised monocular depth estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 3828–3838
2019
Cited alongside, same era.
V. Casser, S. Pirk, R. Mahjourian, and A. Angelova, “Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos,” in Proceedings of the AAAI conference on artificial intelligence , vol. 33, no. 01, 2019, pp. 8001–8008
2019
Cited alongside, same era.
R. Wang, Q. Zhang, C.-W. Fu, X. Shen, W.-S. Zheng, and J. Jia, “Underexposed photo enhancement using deep illumination estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 6849–6857
2019
Cited alongside, same era.
J. Hu, Y. Zhang, and T. Okatani, “Visualization of convolutional neural networks for monocular depth estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 3869–3878
D. Gehrig, M. Rüegg, M. Gehrig, J. Hidalgo-Carrió, and D. Scaramuzza, “Combining events and frames using recurrent asynchronous multimodal networks for monocular depth prediction,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 2822–2829, 2021
2021
Later among the works it cites.
L. He, J. Lu, G. Wang, S. Song, and J. Zhou, “Sosd-net: Joint semantic object segmentation and depth estimation from monocular images,” Neurocomputing , vol. 440, pp. 251–263, 2021
2021
Later among the works it cites.
Y.-f. Zhang, J. Zheng, W. Jia, W. Huang, L. Li, N. Liu, F. Li, and X. He, “Deep rgb-d saliency detection without depth,” IEEE Transactions on Multimedia , vol. 24, pp. 755–767, 2021
2021
Later among the works it cites.
C.-C. Lo and P. Vandewalle, “Depth estimation from monocular images and sparse radar using deep ordinal regression network,” in 2021 IEEE International Conference on Image Processing (ICIP) . IEEE, 2021, pp. 3343–3347
2021
Later among the works it cites.
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2019
Cited alongside, same era.
S. Tulyakov, F. Fleuret, M. Kiefel, P. Gehler, and M. Hirsch, “Learning an event sequence embedding for dense event-based deep stereo,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 1527–1537
2019
Cited alongside, same era.
C. Fu, C. Mertz, and J. M. Dolan, “Lidar and monocular camera fusion: On-road depth completion for autonomous driving,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . IEEE, 2019, pp. 273–278
2019
Cited alongside, same era.
A. Z. Zhu, L. Yuan, K. Chaney, and K. Daniilidis, “Unsupervised event-based learning of optical flow, depth, and egomotion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 989–997
2019
Cited alongside, same era.
X. Li, W. Wang, X. Hu, and J. Yang, “Selective kernel networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 510–519
2019
Cited alongside, same era.
G. Gallego, T. Delbrück, G. Orchard, C. Bartolozzi, B. Taba, A. Censi, S. Leutenegger, A. J. Davison, J. Conradt, K. Daniilidis et al. , “Event-based vision: A survey,” IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 1, pp. 154–180, 2020
2020
Cited alongside, same era.
J. Chen, X. Yang, Q. Jia, and C. Liao, “Denao: Monocular depth estimation network with auxiliary optical flow,” IEEE transactions on pattern analysis and machine intelligence , vol. 43, no. 8, pp. 2598–2610, 2020
2020
Cited alongside, same era.
S. Zhu, G. Brazil, and X. Liu, “The edge of depth: Explicit constraints between segmentation and depth,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 13 116–13 125
2020
Cited alongside, same era.
J.-T. Lin, D. Dai, and L. Van Gool, “Depth estimation from monocular images and sparse radar data,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 10 233–10 240
2020
Cited alongside, same era.
Y. Jiang, X. Gong, D. Liu, Y. Cheng, C. Fang, X. Shen, J. Yang, P. Zhou, and Z. Wang, “Enlightengan: Deep light enhancement without paired supervision,” IEEE Transactions on Image Processing , vol. 30, pp. 2340–2349, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
T. Shimada, H. Nishikawa, X. Kong, and H. Tomiyama, “Pix2pix-based monocular depth estimation for drones with optical flow on airsim,” Sensors , vol. 22, no. 6, p. 2097, 2022
2022
Later among the works it cites.
S. Abdulwahab, H. A. Rashwan, M. A. Garcia, A. Masoumian, and D. Puig, “Monocular depth map estimation based on a multi-scale deep architecture and curvilinear saliency feature boosting,” Neural Computing and Applications , pp. 1–18, 2022
2022
Later among the works it cites.
J. Long, J. Huang, and S. Wang, “Radar fusion monocular depth estimation based on dual attention,” in International Conference on Adaptive and Intelligent Systems . Springer, 2022, pp. 166–179
2022
Later among the works it cites.
M. Cui, Y. Zhu, Y. Liu, Y. Liu, G. Chen, and K. Huang, “Dense depth-map estimation based on fusion of event camera and sparse lidar,” IEEE Transactions on Instrumentation and Measurement , vol. 71, pp. 1–11, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
Z. Li, “Monocular depth estimation toolbox,” https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox, 2022
2022
Later among the works it cites.