A simple optical flow model explains why certain object viewpoints are special.

A core challenge in perception is recognizing objects across the highly variable retinal input that occurs when objects are viewed from different directions (e.g. front versus side views). It has long been known that certain views are of particular importance, but it remains unclear why. We reasoned that characterizing the computations underlying visual comparisons between objects could explain the privileged status of certain qualitatively special views. We measured pose discrimination for a wide range of objects, finding large variations in performance depending on the object and the viewing angle, with front and back views yielding particularly good discrimination. Strikingly, a simple and biologically plausible computational model based on measuring the projected three-dimensional optical flow between views of objects accurately predicted both successes and failures of discrimination performance. This provides a computational account of why certain views have a privileged status.