Persistence-guided Prescribed Topological Simplification
Linxuan Rong, Tao Ju
Abstract
We present a new method for simplifying the topology of a 3D shape. Unlike existing methods that either remove all topological features or offer indirect control over the target topology, our method aims at exactly preserving the user-prescribed numbers of topological features of each type (e.g., components, handles, and voids), while making minimal geometric changes. Guided by persistent homology , our method removes features with low persistence by performing either cutting or filling. This is achieved by an algorithm for computing candidate cuts and fills that remove only low-persistence features, an efficient algorithm for selecting an optimal subset of candidates by computing a weighted independent set, and an iterative framework that alternates between candidate computation and selection. Our method is shown to be highly successful in achieving the prescribed topology on a large test suite involving many complex 3D shapes and target topologies.
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