Centrala begrepp
EC-Depth introduces a novel two-stage framework for robust depth estimation in challenging scenarios.
Sammanfattning
Self-supervised monocular depth estimation is crucial for autonomous driving and robotics.
Existing methods struggle in challenging scenarios like rainy days.
EC-Depth proposes consistency regularization and Mean Teacher paradigm for accurate depth predictions.
The model outperforms state-of-the-art methods on various benchmarks.
Statistik
自己教師付き単眼深度推定の重要性を強調します。
既存の方法は雨のような厳しいシナリオで苦戦しています。
EC-Depthは一貫性正則化とMean Teacherパラダイムを提案しています。