ReFlex: Faithful Panorama Reconstruction from a Single Image with Reflections

Reflections reveal scene content beyond the camera's direct field of view, but faithfully recovering this hidden information is challenging as reflective observations are heterogeneous, incomplete, and physically modulated. Existing methods either rely on explicit reflection modeling with privileged geometric priors or defer to generative synthesis that may hallucinate plausible but unfaithful content. To address these challenges, we propose \textbf{ReFlex}, an observation-constrained framework that casts faithful panorama reconstruction as a controlled inverse problem. We first develop an \emph{Observation-Consistent Estimator} with hierarchical token prediction and signed routing to integrate heterogeneous reflection cues while stabilizing global structure. Subsequently, we propose a \emph{Reliability-Guided Refiner}, which estimates spatial reliability from token predictions and guides a frozen diffusion model to refine uncertain regions without overwriting observation-supported structures. To validate the effectiveness of ReFlex, we further build ReFlex-Bench, a paired benchmark with 110,760 reflective RGB inputs and aligned target panoramas across diverse scenes, reflective geometries, and observation regimes. Experiments show that ReFlex delivers superior reconstruction fidelity without normals, field-of-view annotations, masks, or other privileged information, and obtains the best performance on eight of nine downstream full-view perception protocols.