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• W2012604613 startingPage "1" @default.
• W2012604613 abstract "Computing similarities or distances between 3D shapes is a crucial building block for numerous tasks, including shape retrieval, exploration and classification. Current state-of-the-art distance measures mostly consider the overall appearance of the shapes and are less sensitive to fine changes in shape structure or geometry. We present shape edit distance (SHED) that measures the amount of effort needed to transform one shape into the other, in terms of re-arranging the parts of one shape to match the parts of the other shape, as well as possibly adding and removing parts. The shape edit distance takes into account both the similarity of the overall shape structure and the similarity of individual parts of the shapes. We show that SHED is favorable to state-of-the-art distance measures in a variety of applications and datasets, and is especially successful in scenarios where detecting fine details of the shapes is important, such as shape retrieval and exploration." @default.
• W2012604613 created "2016-06-24" @default.
• W2012604613 creator A5036688260 @default.
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• W2012604613 date "2015-11-02" @default.
• W2012604613 modified "2023-09-27" @default.
• W2012604613 title "SHED" @default.
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• W2012604613 doi "https://doi.org/10.1145/2816795.2818116" @default.
• W2012604613 hasPublicationYear "2015" @default.
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