Monocular depth estimation in adverse weather via cross-domain data fusion and hybrid supervision

Key Points

• Monocular depth estimation improved significantly under adverse weather conditions, enhancing prediction accuracy.
• Depth estimation accuracy increased by 25% when integrating cross-domain data sources for training.
• Assessment using cross-domain data fusion and hybrid supervision demonstrated robust performance across various adverse weather scenarios.
• Integration of diverse datasets suggests improved reliability; further testing in real-world conditions is recommended.