Fetching the paper…
Reading the bibliography…
In recent times, monocular depth estimation (MDE) has experienced significant advancements in performance, largely attributed to the integration of innovative architectures, i.e., convolutional neural networks (CNNs) and Transformers.
1904
Earlier work this paper cites.
1905
Earlier work this paper cites.
1910
Earlier work this paper cites.
2003
Earlier work this paper cites.
2009
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 2009 IEEE Conference on Computer Vision and Pattern Recognition , 2009, pp. 248–255
2009
Earlier work this paper cites.
2009
Earlier work this paper cites.
2010
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition , 2012, pp. 3354–3361
2012
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in ECCV , 2014
2014
Earlier work this paper cites.
J. Petit, B. Stottelaar, and M. Feiri, “Remote attacks on automated vehicles sensors : Experiments on camera and lidar,” 2015
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
C. Yan, “Can you trust autonomous vehicles : Contactless attacks against sensors of self-driving vehicle,” 2016
2016
Earlier work this paper cites.
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter, “Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 1528–1540. [Online]. Available: https://doi.org/10.1145/2976749.2978392
2016
Cited alongside, same era.
K. Tateno, F. Tombari, I. Laina, and N. Navab, “Cnn-slam: Real-time dense monocular slam with learned depth prediction,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 6565–6574, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J. Shen, J. Y. Won, Z. Chen, and Q. A. Chen, “Drift with devil: Security of Multi-Sensor fusion based localization in High-Level autonomous driving under GPS spoofing,” in 29th USENIX Security Symposium (USENIX Security 20) . USENIX Association, Aug. 2020, pp. 931–948. [Online]. Available: https://www.usenix.org/conference/usenixsecurity20/presentation/shen
2020
Later among the works it cites.
Y. Jia, Y. Lu, J. Shen, Q. A. Chen, H. Chen, Z. Zhong, and T. Wei, “Fooling detection alone is not enough: Adversarial attack against multiple object tracking,” in ICLR , 2020
2020
Later among the works it cites.
R. Duan, X. Ma, Y. Wang, J. Bailey, A. K. Qin, and Y. Yang, “Adversarial camouflage: Hiding physical-world attacks with natural styles,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Los Alamitos, CA, USA: IEEE Computer Society, jun 2020, pp. 997–1005. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/CVPR42600.2020.00108
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.
H. Shin, D. Kim, Y. Kwon, and Y. Kim, “Illusion and dazzle: Adversarial optical channel exploits against lidars for automotive applications,” Cryptology ePrint Archive, Paper 2017/613, 2017, https://eprint.iacr.org/2017/613 . [Online]. Available: https://eprint.iacr.org/2017/613
2017
Cited alongside, same era.
T. Trippel, O. Weisse, W. Xu, P. Honeyman, and K. Fu, “Walnut: Waging doubt on the integrity of mems accelerometers with acoustic injection attacks,” in 2017 IEEE European Symposium on Security and Privacy (EuroS&P) , 2017, pp. 3–18
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Y. Tu, Z. Lin, I. Lee, and X. Hei, “Injected and delivered: Fabricating implicit control over actuation systems by spoofing inertial sensors,” in 27th USENIX Security Symposium (USENIX Security 18) . Baltimore, MD: USENIX Association, Aug. 2018, pp. 1545–1562. [Online]. Available: https://www.usenix.org/conference/usenixsecurity18/presentation/tu
2018
Cited alongside, same era.
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, F. Tramèr, A. Prakash, T. Kohno, and D. Song, “Physical adversarial examples for object detectors,” in Proceedings of the 12th USENIX Conference on Offensive Technologies , ser. WOOT’18. USA: USENIX Association, 2018, p. 1
2018
Cited alongside, same era.
A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok, “Synthesizing robust adversarial examples,” in ICML , 2018
2018
Cited alongside, same era.
Y. Wang, W.-L. Chao, D. Garg, B. Hariharan, M. Campbell, and K. Q. Weinberger, “Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 8437–8445
2019
Cited alongside, same era.
2020
Later among the works it cites.
X. Yang, J. Chen, Y. Dang, H. Luo, Y. Tang, C. Liao, P. Chen, and K.-T. Cheng, “Fast depth prediction and obstacle avoidance on a monocular drone using probabilistic convolutional neural network,” IEEE Transactions on Intelligent Transportation Systems , vol. 22, no. 1, pp. 156–167, 2021
2021
Later among the works it cites.
F. Wimbauer, N. Yang, L. von Stumberg, N. Zeller, and D. Cremers, “Monorec: Semi-supervised dense reconstruction in dynamic environments from a single moving camera,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 6108–6118
2021
Later among the works it cites.
Y.-C.-T. Hu, J.-C. Chen, B.-H. Kung, K.-L. Hua, and D. S. Tan, “Naturalistic physical adversarial patch for object detectors,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 7828–7837
2021
Later among the works it cites.
J. Watson, O. M. Aodha, V. Prisacariu, G. Brostow, and M. Firman, “The temporal opportunist: Self-supervised multi-frame monocular depth,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Los Alamitos, CA, USA: IEEE Computer Society, jun 2021, pp. 1164–1174. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/CVPR46437.2021.00122
2021
Later among the works it cites.
Y.-C.-T. Hu, J.-C. Chen, B.-H. Kung, K.-L. Hua, and D. S. Tan, “Naturalistic physical adversarial patch for object detectors,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 7828–7837
2021
Later among the works it cites.
S. Komkov and A. Petiushko, “Advhat: Real-world adversarial attack on arcface face id system,” in 2020 25th International Conference on Pattern Recognition (ICPR) . Los Alamitos, CA, USA: IEEE Computer Society, jan 2021, pp. 819–826. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/ICPR48806.2021.9412236
2021
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
“Andrej karpathy - ai for full-self driving at tesla,” https://youtu.be/hx7BXih7zx8 , accessed: March 1, 2023
2023
Closest in time.
“Tesla ai day 2021,” https://www.youtube.com/live/j0z4FweCy4M?feature=share , accessed: March 1, 2023
2023
Closest in time.
A. Guesmi, R. Ding, M. A. Hanif, I. Alouani, and M. Shafique, “Dap: A dynamic adversarial patch for evading person detectors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 24 595–24 604
2024
Closest in time.
2024
Closest in time.
A. Guesmi, M. A. Hanif, B. Ouni, and M. Shafique, “Saam: Stealthy adversarial attack on monocular depth estimation,” IEEE Access , vol. 12, pp. 13 571–13 585, 2024
2024
Closest in time.