摘要:我们提出一个合作抽象方法,它采用3D对象的集合作为输入,并产生相互一致的和单独的盟友身份保留每个对象的抽象。在一般情况下,抽象的形状,可以保留其主要特点是一个简单的版本。我们假设不存在单一的一个对象的抽象。相反,有各种可能的抽象,和一个容许一个连接只能选择与其他对象的抽象。为此,我们引入了一个新的方法,分层产生的频谱形状集合中的每个模型的抽象。计算适当的抽象级别为每个模型形状的简化和集间的一致性集体最大化,而单个形状特性得以保存。Abstract:We present a co-abstraction method that takes as input a collection of 3D objects, and produces a mutually consistent and individu-ally identity-preserving abstraction of each object. In general, an abstraction is a simpler version of a shape that preserves its main characteristics. We hypothesize, however, that there is no single abstraction of an object. Instead, there is a variety of possible abstractions, and an admissible one can only be chosen conjointly with other objects’ abstractions. To this end, we introduce a new approach that hierarchically generates a spec***m of abstractions for each model in a shape collection. Given the spectra, we compute the appropriate abstraction level for each model such that shape simplification and inter-set consistency are collectively maximized,while individual shape identities are preserved.
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