Optical-Evidence Predictive Representation Learning for Hidden Screen Reconstruction from Diffuse Wall Reflections

Passive hidden-screen reconstruction aims to recover the visual content of a display that lies completely outside the camera field of view from the diffuse light it casts on a visible wall. This inverse problem is challenging because indirect propagation attenuates color, luminance, and local structure at different rates, while wall reflectance, ambient illumination, and camera exposure introduce observation-dependent interference. The resulting ambiguity also encourages learned decoders to complete missing content from data priors rather than from evidence in the current observation. We propose Optical Evidence Predictive Representation Learning (OEPRL), an observation-grounded reconstruction framework that organizes wall-derived evidence before using it to correct screen-space features. Reliability-Calibrated Optical Evidence Representation (RCER) constructs an 11-channel multiscale evidence tensor, calibrates global photometric conditioning using exposure and contrast statistics, and extracts positioned local evidence tokens from informative wall regions. Observation-Consistent Optical Predictive Learning (OCPL) then predicts global and regional summaries of the selected wall evidence, establishes soft correspondences between screen and wall representations, and applies bounded global-to-local corrections to the decoder. On the Browser, Figure, Game, and Lock benchmarks, OEPRL ranks first in all 12 LPIPS, SSIM, and PSNR comparisons. On Browser, it improves over the strongest competing results by 0.0148 LPIPS, 0.0235 SSIM, and 0.714 dB PSNR. Module ablations and perturbation experiments further support the complementary contributions of evidence calibration and predictive correction.