In this abstract we describe recent [4,7] and latest work on the
determination of affordances in visually perceived 3D scenes. Our method builds
on the hypothesis that geometry on its own provides enough information to
enable the detection of significant interaction possibilities in the
environment. The motivation behind this is that geometric information is
intimately related to the physical interactions afforded by objects in the
world. The approach uses a generic representation for the interaction between
everyday objects such as a mug or an umbrella with the environment, and also
for more complex affordances such as humans Sitting or Riding a motorcycle.
Experiments with synthetic and real RGB-D scenes show that the representation
enables the prediction of affordance candidate locations in novel environments
at fast rates and from a single (one-shot) training example. The determination
of affordances is a crucial step towards systems that need to perceive and
interact with their surroundings. We here illustrate output on two cases for a
simulated robot and for an Augmented Reality setting, both perceiving in an
egocentric manner.

利用几何信息推断三维场景中的互动信息和可能性，并在机器人和增强现实场景中进行实验，结果表明可以在新颖信搏的环境下以快速率进行预测。