Visual Affordance and Function Understanding: A Survey
It provides a comprehensive overview for researchers in robotics and computer vision, highlighting challenges in visual affordance and function understanding, but it is incremental as it synthesizes existing literature without introducing new methods.
This survey addresses the problem of enabling robots to understand object affordances and functionalities in visual domains, summarizing the state of the art, open problems, and research gaps in areas such as affordance detection and functional scene understanding.
Nowadays, robots are dominating the manufacturing, entertainment and healthcare industries. Robot vision aims to equip robots with the ability to discover information, understand it and interact with the environment. These capabilities require an agent to effectively understand object affordances and functionalities in complex visual domains. In this literature survey, we first focus on Visual affordances and summarize the state of the art as well as open problems and research gaps. Specifically, we discuss sub-problems such as affordance detection, categorization, segmentation and high-level reasoning. Furthermore, we cover functional scene understanding and the prevalent functional descriptors used in the literature. The survey also provides necessary background to the problem, sheds light on its significance and highlights the existing challenges for affordance and functionality learning.