Leveraging Metric Depth for Relative Depth Prediction
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.