  This paper develops and evaluates a novel method that allows for the
detection of affordances in a scalable and multiple-instance manner on visually
recovered pointclouds. Our approach has many advantages over alternative
methods, as it is based on highly parallelizable, one-shot learning that is
fast in commodity hardware. The approach is hybrid in that it uses a geometric
representation together with a state-of-the-art deep learning method capable of
identifying 3D scene saliency. The geometric component allows for a compact and
efficient representation, boosting the performance of the deep network
architecture which proved insufficient on its own. Moreover, our approach
allows not only to predict whether an input scene affords or not the
interactions, but also the pose of the objects that allow these interactions to
take place. Our predictions align well with crowd-sourced human judgment as
they are preferred with 87% probability, show high rates of improvement with
almost four times (4x) better performance over a deep learning-only baseline
and are seven times (7x) faster than previous art.
