The recognition of objects that are imaged by viewing through a refractive interface has long been considered a difficult goal, due to spatial distortions that result from interfacial refraction. Although not insoluble via approximation, trans-interfacial stereophotogrammetry has not been addressed in the open literature. However, our previously-reported research has presented feasible, computationally efficient methods for restoring imagery obtained by monocular trans-interfacial viewing. The restoration results exhibit visually acceptable degradation due to information loss inherent in the restoration algorithm and (by design) a partial knowledge of the interfacial optical parameters. In particular, we have shown that our methods can facilitate low-distortion viewing of objects that are submerged at shallow optical depths (less than one caustic surface) in moderately clear water. In practice, our techniques require a partial knowledge of the sea topography, which we propose to obtain through time- domain reflectometry, or via stereophotogrammetry of the air-water interface. In this paper, we extend our previous work in monocular trans-interfacial imaging to address the problem of determining the range-to-target in trans-interfacial stereoscopic imagery. Note that our previous scenarios for interfacial viewing, which assumed bistatic sensing of interfacial topography, required at least three high-resolution cameras. Unfortunately, such techniques present infeasible bandwidth requirements for operationally-available transmission channels. Additionally, sensor design is complex, and performance can degrade significantly in the presence of slight optical misalignment. Thus, we restrict our discussion to the problem of stereo vision through the interface using only two cameras that exhibit moderate resolution. Analyses emphasize determination of the constraints upon sensor configuration and sea state under which such methods are applicable, as well as the prediction of spatial localization errors in the presence of typical sensor parameter errors and limiting cases.