发现单目深度模型在模糊输入下会生成虚假3D结构，提出检测与修复方案。

Monocular depth foundation models achieve remarkable generalization by learning large-scale semantic priors, but this creates a critical vulnerability: they hallucinate illusory 3D structures from planar/low-curvature but perceptually ambiguous inputs. We term this failure the 3D Mirage. This paper introduces a novel end-to-end framework to probe, score, and tame this under-quantified safety risk in monocular depth under context variation. To probe, we present 3D-Mirage, the first benchmark to combine context variation and precise annotation for real-world illusions with real object exclusions, multi-surface support; purpose-built to stress-test monocular depth on real-world illusions. To score, we propose a second-order magnitude-based evaluation with two metrics: the Deviation Composite Score (DCS) for high second-order 3D structure and the Confusion Composite Score (CCS) for contextual instability. To tame this failure, we introduce Grounded Self-Distillation, a parameter-efficient strategy on Depth-Anything-V2 baseline that surgically targets and resolves hallucination on illusion ROIs while preserving background knowledge, avoiding catastrophic forgetting. Our work provides an innovative pipeline for diagnosing and addressing this phenomenon, urging a necessary shift in the evaluation of MDE from pixel-wise accuracy to structural and contextual robustness.