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• W3015841438 abstract "Most objects are composed of semantically distinctive parts that are more or less geometrically distinctive as well. Points on the object relevant for a certain robot operation are usually determined by various physical properties of the object, such as its dimensions or weight distribution, and by the purpose of object parts. A robot operation defined for a particular part of a representative object can be transferred and adapted to other instances of the same object class by detecting the corresponding components. In this paper, a method for semantic association of the object’s components within the object class is proposed. It is suitable for real-time robotic tasks and requires only a few previously annotated representative models. The proposed approach is based on the component association graph and a novel descriptor that describes the geometrical arrangement of the components. The method is experimentally evaluated on a challenging benchmark dataset." @default.
• W3015841438 created "2020-04-17" @default.
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• W3015841438 date "2020-04-11" @default.
• W3015841438 modified "2023-10-18" @default.
• W3015841438 title "Semantic Component Association within Object Classes Based on Convex Polyhedrons" @default.
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• W3015841438 doi "https://doi.org/10.3390/app10082641" @default.
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