Evidence-Budgeted Light Transport Representation Learning: Wall-Reflected Screen Content Recovery

Can an ordinary camera recover hidden screen content from the diffuse light that the screen casts onto a wall?We address this question with Evidence-Budgeted Light Transport Representation Learning (EBLTR). Wall transport preserves screen information unevenly across locations, scales, and frequency bands, making the reliability of surviving evidence central to reconstruction.Our Light Transport Observability Representation (LTOR) combines frequency-separated responses, local wall statistics, and multilevel features to estimate this evidence.Evidence-Budgeted Learning (EBL) converts the estimate into a local correction budget, so the amount of refinement follows the available observational support.A frozen screen-to-wall surrogate further checks observation consistency during training.Across four screen-content categories and varied observation conditions, EBLTR outperforms 19 reconstruction baselines and recovers screen colour, regional layout, and local structural cues.The reconstructions reduce erroneous edges, textures, interface contours, and text strokes that lack wall-observation support.These results demonstrate the preliminary feasibility of recovering hidden screen content from ordinary-camera recordings of wall illumination, with reconstructed detail guided by the evidence that survives light transport.