Learning Action Maps of Large Environments via First-Person Vision
Learning Action Maps of Large Environments via First-Person Vision
When people observe and interact with physical spaces, they are able to associate functionality to regions in the environment. Our goal is to automate dense functional understanding of large spaces by leveraging sparse activity demonstrations recorded from an ego-centric viewpoint. The method we describe enables functionality estimation in large scenes …