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New method predicts relative depth using pre-trained models

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel method for predicting relative depth in monocular images, specifically for football scenarios. Their approach utilizes the zero-shot capabilities of large-scale pre-trained models to infer metric depth, which aids in more accurate relative depth estimation. This technique was applied to the 2025 SoccerNet Monocular Depth Estimation Competition Challenge, achieving a score of 2.68 x 10^-3 on the challenge set. AI

IMPACT This method could improve depth estimation in specialized visual domains, aiding applications like sports analytics and augmented reality.

RANK_REASON The cluster contains a research paper detailing a new method for a specific computer vision task.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Xiaoyang Bi, Shuaikun Liu, Zhaohong Liu, Yuxin Yang, Zhe Zhao, Mengshi Qi, Liang Liu, Huadong Ma ·

    Leveraging Metric Depth for Relative Depth Prediction

    arXiv:2606.10628v1 Announce Type: new 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 add…

  2. arXiv cs.CV TIER_1 English(EN) · Huadong Ma ·

    Leveraging Metric Depth for Relative Depth Prediction

    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 powerf…

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