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arXiv · 2606.10628

Leveraging Metric Depth for Relative Depth Prediction

Xiaoyang Bi·Shuaikun Liu·Zhaohong Liu·Yuxin Yang·Zhe Zhao·Mengshi Qi·Liang Liu·Huadong Ma
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Abstract

We present our solution to the 2025 SoccerNet Monocular Depth Estimation Competition Challenge. Predicting the relative depth in football scenarios is challenging, especially with only thousands of training samples available. To address this issue, our method leverages the powerful zero-shot capabilities of models pretrained on large-scale datasets to learn metric depth for effective relative depth prediction, achieving a score of $2.68 \times 10^{-3}$ on the challenge set.

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Xiaoyang Bi, Shuaikun Liu, Zhaohong Liu, Yuxin Yang, Zhe Zhao, Mengshi Qi, Liang Liu, Huadong Ma. 2026-06-09. Leveraging Metric Depth for Relative Depth Prediction. https://arxiv.org/abs/2606.10628

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