Shading conveys information on 3-D shape and the process of recovering this information is called shape-from-shading (SFS). This thesis divides the process of human SFS into two functional sub-units (luminance disambiguation and shape computation) and studies them individually. Based on results of a series of psychophysical experiments it is proposed that the interaction between first- and second-order channels plays an important role in disambiguating luminance. Based on this idea, two versions of a biologically plausible model are developed to explain the human performances observed here and elsewhere. An algorithm sharing the same idea is also developed as a solution to the problem of intrinsic image decomposition in the field of image processing. With regard to the shape computation unit, a link between luminance variations and estimated surface norms is identified by testing participants on simple gratings with several different luminance profiles. This methodology is unconventional but can be justified in the light of past studies of human SFS. Finally a computational algorithm for SFS containing two distinct operating modes is proposed. This algorithm is broadly consistent with the known psychophysics on human SFS.