  The field of functional recognition or affordance estimation from images has
seen a revival in recent years. As originally proposed by Gibson, the
affordances of a scene were directly perceived from the ambient light: in other
words, functional properties like sittable were estimated directly from
incoming pixels. Recent work, however, has taken a mediated approach in which
affordances are derived by first estimating semantics or geometry and then
reasoning about the affordances. In a tribute to Gibson, this paper explores
his theory of affordances as originally proposed. We propose two approaches for
direct perception of affordances and show that they obtain good results and can
out-perform mediated approaches. We hope this paper can rekindle discussion
around direct perception and its implications in the long term.
