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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\n                    <jats:italic toggle=\"yes\">persistent homology<\/jats:italic>\n                    , 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.\n                  <\/jats:p>","DOI":"10.1145\/3811384","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T07:05:51Z","timestamp":1783062351000},"page":"1-16","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Persistence-guided Prescribed Topological Simplification"],"prefix":"10.1145","volume":"45","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0004-7955-0351","authenticated-orcid":false,"given":"Linxuan","family":"Rong","sequence":"first","affiliation":[{"name":"Washington University in St. Louis, SAINT LOUIS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-1848-1012","authenticated-orcid":false,"given":"Tao","family":"Ju","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, SAINT LOUIS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comgeo.2014.08.010"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsc.2016.03.008"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/3115956.3116021"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/PCCGA.2002.1167868"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2004.3"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14079"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comgeo.2006.05.009"},{"key":"e_1_2_1_8_1","volume-title":"International conference on machine learning. 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