One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models

A faithful 3D world representation should account for layeredgeometry, where a single camera ray may contain multiple visible and ge-ometrically valid surfaces. Monocular depth estimation, however, reducesthis structure to one scalar depth per pixel. Transparent scenes makethis ambiguity measurable: the same ray can pass through foregroundglass and observe the background, turning the supervised target into aconvention of annotation, data, and training rather than a scene-intrinsictruth. A learned predictor exposes this convention as its depth-layerpreference. We introduce MultiDepth-3k (MD-3k), a sparse two-layer or-dinal benchmark for measuring depth-layer preference and multi-layerspatial relationship accuracy (ML-SRA). On MD-3k, leading depth foun-dation models exhibit diverse layer preferences under standard RGBinput, showing that the same layered geometry can be resolved differentlyacross models. We further find that Laplacian Visual Prompting (LVP),a training-free spectral input transformation, can substantially changethe reported layer for certain frozen models. The strongest RGB/LVPpair, DAv2-L, reaches 75.5% ML-SRA. These results suggest that depthfoundation models may express complementary geometric hypotheses thatstandard RGB inference leaves unexpressed. We invite the community torethink depth supervision and evaluation through an ambiguity-awarelens, where multiple valid 3D interpretations are treated as geometricstructure to be measured, preserved, and expressed.