Fetching the paper…
Reading the bibliography…
Aerial scene understanding systems face stringent payload restrictions and must often rely on monocular depth estimation for modeling scene geometry, which is an inherently ill-posed problem.
T. G. Farr and M. Kobrick, “Shuttle Radar Topography Mission produces a wealth of data,” Eos, Transactions American Geophysical Union , vol. 81, no. 48, pp. 583–585, 2000
2000
Earlier work this paper cites.
S. Katz, A. Tal, and R. Basri, “Direct visibility of point sets,” in ACM SIGGRAPH 2007 papers , 2007, pp. 24–es
2007
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 Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2002–2011
2011
Earlier work this paper cites.
T. Tachikawa, M. Kaku, A. Iwasaki, D. B. Gesch, M. J. Oimoen, Z. Zhang, J. J. Danielson, T. Krieger, B. Curtis, J. Haase et al. , “ASTER global digital elevation model version 2-summary of validation results,” NASA, Tech. Rep., 2011
2011
Earlier work this paper cites.
T. Tadono, H. Ishida, F. Oda, S. Naito, K. Minakawa, and H. Iwamoto, “Precise global DEM generation by ALOS PRISM,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences , vol. 2, pp. 71–76, 2014
2014
Earlier work this paper cites.
W. Zhang, J. Qi, P. Wan, H. Wang, D. Xie, X. Wang, and G. Yan, “An easy-to-use airborne LiDAR data filtering method based on cloth simulation,” Remote sensing , vol. 8, no. 6, p. 501, 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 European conference on computer vision . Springer, 2016, pp. 740–756
2016
Earlier work this paper cites.
J. L. Schönberger and J.-M. Frahm, “Structure-from-Motion Revisited,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 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 European Conference on Computer Vision (ECCV) , 2016
2016
Earlier work this paper cites.
T. Zhou, M. Brown, N. Snavely, and D. G. Lowe, “Unsupervised learning of depth and ego-motion from video,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1851–1858
2017
Earlier work this paper cites.
P. Rizzoli, M. Martone, C. Gonzalez, C. Wecklich, D. B. Tridon, B. Bräutigam, M. Bachmann, D. Schulze, T. Fritz, M. Huber et al. , “Generation and performance assessment of the global TanDEM-X digital elevation model,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 132, pp. 119–139, 2017
2017
Earlier work this paper cites.
S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: High-fidelity visual and physical simulation for autonomous vehicles,” in Field and service robotics . Springer, 2018, pp. 621–635
2018
Earlier work this paper cites.
B. Wessel, M. Huber, C. Wohlfart, U. Marschalk, D. Kosmann, and A. Roth, “Accuracy assessment of the global TanDEM-X digital elevation model with GPS data,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 139, pp. 171–182, 2018
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
Earlier work this paper cites.
M. Fonder and M. Van Droogenbroeck, “Mid-air: A multi-modal dataset for extremely low altitude drone flights,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops , 2019, pp. 0–0
2019
Earlier work this paper cites.
J. Bian, Z. Li, N. Wang, H. Zhan, C. Shen, M.-M. Cheng, and I. Reid, “Unsupervised scale-consistent depth and ego-motion learning from monocular video,” Advances in neural information processing systems , vol. 32, pp. 35–45, 2019
2019
Earlier work this paper cites.
R. Zhang, Z. Cao, S. Yang, L. Si, H. Sun, L. Xu, and F. Sun, “Cognition-driven structural prior for instance-dependent label transition matrix estimation,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–14, 2024
2019
Earlier work this paper 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 transactions on pattern analysis and machine intelligence , vol. 44, no. 3, pp. 1623–1637, 2020
2020
Earlier work this paper cites.
Y. Lyu, G. Vosselman, G.-S. Xia, A. Yilmaz, and M. Y. Yang, “UAVid: A semantic segmentation dataset for UAV imagery,” ISPRS journal of photogrammetry and remote sensing , vol. 165, pp. 108–119, 2020
2020
Earlier work this paper cites.
L. Madhuanand, F. Nex, M. Yang, N. Paparoditis, C. Mallet, F. Lafarge, F. Remondino, I. Toschi, T. Fuse et al. , “Deep learning for monocular depth estimation from UAV images,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences , vol. 2, 2020
2020
Cited alongside, same era.
F. Xue, G. Zhuo, Z. Huang, W. Fu, Z. Wu, and M. H. Ang, “Toward hierarchical self-supervised monocular absolute depth estimation for autonomous driving applications,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 2330–2337
2020
Cited alongside, same era.
V. Guizilini, R. Ambrus, S. Pillai, A. Raventos, and A. Gaidon, “3d packing for self-supervised monocular depth estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 2485–2494
2020
Cited alongside, same era.
W. Wang, D. Zhu, X. Wang, Y. Hu, Y. Qiu, C. Wang, Y. Hu, A. Kapoor, and S. Scherer, “TartanAir: A dataset to push the limits of visual SLAM,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020
V. Licăret, V. Robu, A. Marcu, D. Costea, E. Sluşanschi, R. Sukthankar, and M. Leordeanu, “Ufo depth: Unsupervised learning with flow-based odometry optimization for metric depth estimation,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 6526–6532
2022
Later among the works it cites.
S. F. Bhat, I. Alhashim, and P. Wonka, “Localbins: Improving depth estimation by learning local distributions,” in European Conference on Computer Vision . Springer, 2022, pp. 480–496
2022
Later among the works it cites.
M. Fonder, D. Ernst, and M. Van Droogenbroeck, “Parallax inference for robust temporal monocular depth estimation in unstructured environments,” Sensors , vol. 22, no. 23, pp. 1–22, November 2022. [Online]. Available: https://doi.org/10.3390/s22239374
2022
Later among the works it cites.
K. Swami, A. Muduli, U. Gurram, and P. Bajpai, “Do what you can, with what you have: Scale-aware and high quality monocular depth estimation without real world labels,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 988–997
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
Z. Teed and J. Deng, “Raft: Recurrent all-pairs field transforms for optical flow,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16 . Springer, 2020, pp. 402–419
2020
Cited alongside, same era.
M. A. Akhloufi, A. Couturier, and N. A. Castro, “Unmanned aerial vehicles for wildland fires: Sensing, perception, cooperation and assistance,” Drones , vol. 5, no. 1, p. 15, 2021
2021
Cited alongside, same era.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
Cited alongside, same era.
H. Florea, V.-C. Miclea, and S. Nedevschi, “WildUAV: Monocular UAV dataset for depth estimation tasks,” in 2021 IEEE 17th International Conference on Intelligent Computer Communication and Processing (ICCP) . IEEE, 2021, pp. 291–298
2021
Cited alongside, same era.
R. Ranftl, A. Bochkovskiy, and V. Koltun, “Vision transformers for dense prediction,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 12 179–12 188
2021
Cited alongside, same era.
S. F. Bhat, I. Alhashim, and P. Wonka, “Adabins: Depth estimation using adaptive bins,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 4009–4018
2021
Cited alongside, same era.
V.-C. Miclea and S. Nedevschi, “Monocular depth estimation with improved long-range accuracy for UAV environment perception,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–15, 2021
2021
Cited alongside, same era.
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,” International Journal of Computer Vision , pp. 1–17, 2021
2021
Cited alongside, same era.
2022
Later among the works it cites.
V. Guizilini, K.-H. Lee, R. Ambruş, and A. Gaidon, “Learning optical flow, depth, and scene flow without real-world labels,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 3491–3498, 2022
2022
Later among the works it cites.
M. Lyu, Y. Zhao, C. Huang, and H. Huang, “Unmanned aerial vehicles for search and rescue: A survey,” Remote Sensing , vol. 15, no. 13, p. 3266, 2023
2023
Later among the works it cites.
V. Guizilini, I. Vasiljevic, D. Chen, R. Ambru
2023
Later among the works it cites.
2023
Later among the works it cites.
V.-C. Miclea and S. Nedevschi, “Dynamic semantically guided monocular depth estimation for uav environment perception,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Later among the works it cites.
U. Shin, K. Park, B.-U. Lee, K. Lee, and I. S. Kweon, “Self-supervised monocular depth estimation from thermal images via adversarial multi-spectral adaptation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 5798–5807
2023
Later among the works it cites.
L. Yang, B. Kang, Z. Huang, X. Xu, J. Feng, and H. Zhao, “Depth anything: Unleashing the power of large-scale unlabeled data,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024, pp. 10 371–10 381
2024
Closest in time.
L. Yang, B. Kang, Z. Huang, Z. Zhao, X. Xu, J. Feng, and H. Zhao, “Depth anything v2,” in The Thirty-eighth Annual Conference on Neural Information Processing Systems , 2024. [Online]. Available: https://openreview.net/forum?id=cFTi3gLJ1X
2024
Closest in time.
L. Piccinelli, Y.-H. Yang, C. Sakaridis, M. Segu, S. Li, L. V. Gool, and F. Yu, “UniDepth: Universal monocular metric depth estimation,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024, pp. 10 106–10 116
2024
Closest in time.
H. Li, Y. Ma, Y. Gu, K. Hu, Y. Liu, and X. Zuo, “Radarcam-depth: Radar-camera fusion for depth estimation with learned metric scale,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024, pp. 10 665–10 672
2024
Closest in time.
Y. Pan, B. Liu, Z. Liu, H. Shen, J. Xu, W. Fu, and T. Yang, “MoNA Bench: A benchmark for monocular depth estimation in navigation of autonomous unmanned aircraft system,” Drones , vol. 8, no. 2, 2024. [Online]. Available: https://www.mdpi.com/2504-446X/8/2/66
2024
Closest in time.
M. Hermann, M. Weinmann, F. Nex, E. Stathopoulou, F. Remondino, B. Jutzi, and B. Ruf, “Depth estimation and 3D reconstruction from UAV-borne imagery: Evaluation on the UseGeo dataset,” ISPRS Open Journal of Photogrammetry and Remote Sensing , p. 100065, 2024
2024
Closest in time.
GPL software, CloudCompare (version 2.13) , 2024. [Online]. Available: http://www.cloudcompare.org
2024
Closest in time.
QGIS Development Team, QGIS Geographic Information System , QGIS Association, 2024. [Online]. Available: https://www.qgis.org
2024
Closest in time.
R. Zhang, J. Tan, Z. Cao, L. Xu, Y. Liu, L. Si, and F. Sun, “Part-aware correlation networks for few-shot learning,” IEEE Transactions on Multimedia , 2024
2024
Closest in time.