Inferring Objects Around Corners from Indirect Wall Light: Structure-Preserving Silhouette Recovery with RUBIN

We ask whether a conventional RGB camera can reveal a target silhouette from indirect wall light around a corner. We propose RUBIN, a structure-preserving framework that addresses acquisition-related radiance variation and boundary ambiguity caused by diffuse transport. Radiance-Consistent Structural Learning (RCSL) constructs position-preserving radiance views and aligns global representations, co-located local features, and dense structural relations. Variance and covariance regularization preserve descriptor diversity and reduce redundancy, while an acquisition-factor branch discourages encoding of sampled acquisition factors. Uncertainty-Gated Boundary Region Refinement (UGBR) combines coarse region predictions with multiscale boundary evidence and weights relational corrections using region uncertainty and boundary agreement. Experiments on five measured datasets show stronger overall silhouette recovery than twenty representative methods. Ablations support the complementary roles of RCSL and UGBR, and observation-variation experiments examine the shape information retained as wall-light patterns change. The recovered foreground extent, major components, and basic contours support preliminary hidden-target shape inference from wall light in this setup.