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.
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1109/CVPRW63382.2024.00005 Abstract/SummaryThis 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%.
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