This research addresses the challenge of improving monocular depth estimation (MDE) using unlabeled data through a novel distillation framework. The core innovation is Cross-Context Distillation, which combines local and global depth cues to enhance pseudo-label quality and model accuracy. A multi-teacher distillation approach further leverages complementary strengths of different depth estimation models for more robust predictions. The paper systematically analyzes the impact of various depth normalization strategies on pseudo-label distillation, revealing that Cross-Context Distillation significantly outperforms existing methods on benchmark datasets.Experiments validate the effectiveness of their approach, improving both fine details and overall depth consistency in MDE.