The third monocular depth estimation challenge

Spencer, J., Tosi, F., Poggi, M., Singh Arora, R., Russell, C., Hadfield, S., Bowden, R., Zhou, G. Y., Li, Z., Rao, Q., Bao, Y., Liu, X., Kim, D., Kim, J., Kim, M., Lavreniuk, M., Li, R., Mao, Q., Wu, J., Zhu, Y., Sun, J., Zhang, Y., Patni, S., Agarwal, A., Arora, C., Sun, P., Jiang, K., Wu, G., Liu, J., Liu, X., Jiang, J., Zhang, X., Wei, J., Wang, F., Tan, Z., Wang, J., Luginov, A., Shahzad, M. ORCID: https://orcid.org/0009-0002-9394-343X, Hosseini, S., Trajcevski, A. and Elder, J. H. (2024) The third monocular depth estimation challenge. In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 17-18 Jun 2024, Seatle, USA. doi: 10.1109/CVPRW63382.2024.00005

Abstract/Summary

This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. As with the previous edition, methods can use any form of supervision, i.e. supervised or self-supervised. The challenge received a total of 19 submissions outperforming the baseline on the test set: 10 among them submitted a report describing their approach, highlighting a diffused use of foundational models such as Depth Anything at the core of their method. The challenge winners drastically improved 3D F-Score performance, from 17.51% to 23.72%.

Item Type	Conference or Workshop Item (Paper)
URI	https://reading-clone.eprints-hosting.org/id/eprint/116215
Item Type	Conference or Workshop Item
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
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