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Despite advancements in self-supervised monocular depth estimation, challenges persist in dynamic scenarios due to the dependence on assumptions about a static world.
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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 , 2019, pp. 8001–8008
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2019
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V. Patil, W. Van Gansbeke, D. Dai, and L. Van Gool, “Don’t forget the past: Recurrent depth estimation from monocular video,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 6813–6820, 2020
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X. Gu, Z. Fan, S. Zhu, Z. Dai, F. Tan, and P. Tan, “Cascade cost volume for high-resolution multi-view stereo and stereo matching,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 2495–2504
2020
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M. Klingner, J.-A. Termöhlen, J. Mikolajczyk, and T. Fingscheidt, “Self-supervised monocular depth estimation: Solving the dynamic object problem by semantic guidance,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XX 16 . Springer, 2020, pp. 582–600
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2020
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S. Li, J. Shi, W. Song, A. Hao, and H. Qin, “Hierarchical object relationship constrained monocular depth estimation.” Pattern Recognition , vol. 120, p. 108116, 2021
2021
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2021
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K. Zhou, L. Hong, C. Chen, H. Xu, C. Ye, Q. Hu, and Z. Li, “Devnet: Self-supervised monocular depth learning via density volume construction,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXIX . Springer, 2022, pp. 125–142
2022
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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 Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXII . Springer, 2022, pp. 228–244
2022
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H. Xu, J. Zhang, J. Cai, H. Rezatofighi, and D. Tao, “Gmflow: Learning optical flow via global matching,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 8121–8130
2022
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K. Zhou, J.-X. Zhong, S. Shin, K. Lu, Y. Yang, A. Markham, and N. Trigoni, “Dynpoint: Dynamic neural point for view synthesis,” Conference on neural information processing systems , 2023
2023
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2023
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R. Li, D. Xue, S. Su, X. He, Q. Mao, Y. Zhu, J. Sun, and Y. Zhang, “Learning depth via leveraging semantics: Self-supervised monocular depth estimation with both implicit and explicit semantic guidance,” Pattern Recognition , vol. 137, p. 109297, 2023
2023
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S. Lee, W. Im, and S.-E. Yoon, “Multi-resolution distillation for self-supervised monocular depth estimation,” Pattern Recognition Letters , vol. 176, pp. 215–222, 2023
2023
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J. L. G. Bello, J. Moon, and M. Kim, “Self-supervised monocular depth estimation with positional shift depth variance and adaptive disparity quantization,” IEEE Transactions on Image Processing , 2024
2024
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