Human observers can perceive the threedimensional (3-D) structure of their environment using various cues, an important one of which is motion parallax. The motion of any points projection on the retina depends both on the points movement in space and on its distance from the eye. Therefore, retinal motion can be used to extract the 3-D structure of the environment and the shape of objects, in a process known as structurefrom- motion (sfm). However, because many combinations of 3-D structure and motion can lead to the same optic flow, sfm is an ill-posed inverse problem. The rigidity assumption is a constraint supposed to formally solve the sfm problem and to account for human performance. Recently, however, a number of psychophysical results, in both moving and stationary human observers, have shown that the rigidity assumption alone cannot account for sfm, but no model is known to account for the new results. Here, we construct a Bayesian model of sfm based on only one new assumption, that of stationarity, coupled with the assumption of rigidity. The predictions of the model, calculated using a new and powerful methodology called Bayesian programming, account for a wide variety of experimental findings.