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Self-supervised multi-frame monocular depth estimation relies on the geometric consistency between successive frames under the assumption of a static scene.
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Chen, Y., Schmid, C., Sminchisescu, C.: Self-supervised learning with geometric constraints in monocular video: Connecting flow, depth, and camera. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7063–7072 (2019)
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Yin, W., Liu, Y., Shen, C., Yan, Y.: Enforcing geometric constraints of virtual normal for depth prediction. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 5684–5693 (2019)
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Watson, J., Mac Aodha, O., Prisacariu, V., Brostow, G., Firman, M.: The temporal opportunist: Self-supervised multi-frame monocular depth. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1164–1174 (2021)
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Zhao, C., Zhang, Y., Poggi, M., Tosi, F., Guo, X., Zhu, Z., Huang, G., Tang, Y., Mattoccia, S.: Monovit: Self-supervised monocular depth estimation with a vision transformer. In: 2022 international conference on 3D vision (3DV). pp. 668–678. IEEE (2022)
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Bae, J., Moon, S., Im, S.: Deep digging into the generalization of self-supervised monocular depth estimation. In: Proceedings of the AAAI conference on artificial intelligence. vol. 37, pp. 187–196 (2023)
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Bangunharcana, A., Magd, A., Kim, K.S.: Dualrefine: Self-supervised depth and pose estimation through iterative epipolar sampling and refinement toward equilibrium. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 726–738 (2023)
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2024
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