A new approach to shape estimation from shading input has been recently introduced through the processes of disparity-based photometric stereo (DBPS) and Green's function shape from shading (GSFS). Both processes start from a pair of constraints - a matching constraint and a photometric constraint - to arrive at a closed-form expression for the depth function of the imaged surface. In DBPS, the matching equation is solved for the disparity map, given a pair of photometric stereo images, while in GSFS, which extends the previous approach to single-input reconstruction, that equation is solved for the matching image, via Green's function. Adopting a similar framework, we have recently used photometric and matching constraints for deriving a new approach to the photometric-motion shape estimation problem. Here we show how we can extend that process to the single-input case, via Green's function. This yields high quality shape-from-shading estimates, even from real input obtained under complex illumination.