From Wall Glows to Recoverable Screens: Hierarchical Recoverability-Guided Accretive Representation Learning for Indirect Screen Reconstruction

We study whether a hidden display can be recovered from an ordinary camera image of the diffuse glow that it casts onto a visible wall. Spatial propagation and wall reflection mix screen regions and attenuate color, layout, structure, and detail by different amounts at different locations, so each wall glow retains a different set of usable cues. A uniform image-to-image mapping leaves the recoverability and reconstruction strength of these traces unresolved. We introduce Observability-Guided Accretive Representation Learning (OARL) to make these decisions from the observation. Calibrated Hierarchical Recoverability Representation Learning (CHRRL) constructs a shared evidence field, organizes it into color, layout, structure, and detail, and predicts a spatial gate for each type. A paired-versus-nonmatching screen objective calibrates the global evidence representation during training. Observability-Stratified Accretive Reconstruction (OSAR) then follows a fixed color-to-layout-to-structure-to-detail dependency backbone; the gates continuously modulate each stage's residual magnitude at every location, and later stages inherit earlier states. At inference, the complete path uses the current wall-glow image. OARL achieves the best reported values in all 15 category–metric comparisons across five screen-content categories. Component and controlled representation comparisons support hierarchical recoverability estimation and inherited gated reconstruction, while the seven-position camera-translation series shows stable recovery as the observed glow changes. Together, these results provide initial evidence that an ordinary camera viewing a wall glow can recover the principal information of a hidden screen.