Learning Affordance Maps by Observing Interactions
نویسندگان
چکیده
We address the problem of predicting affordances for dense 3D geometry scans of real-world scenes. Using an RGBD camera setup we observe people interacting with objects and learn the correlation between body part positions and interacted geometry. We encode this information as affordance maps over 3D geometry and predict affordances for novel scenes where no observations are available.
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