The Bayesian approach to vision provides a fruitful theoretical framework for integrating different depth modules. In this formulation depth can be represented by one or more surfaces. Prior probabilities, corresponding to natural constraints, can be defined on these surfaces to avoid the ill-posedness of vision. We advocate strong coupling between different depth cues, so that the different modules can interact during computation. This framework is rich enough to accommodate straightforwardly both consonant and contradictory cue integration, by the use of binary decision units. These units can be interpreted in terms of robust statistics. A number of existing psychophysical experiments can be understood within this framework.