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Graph."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:p>\n            We present\n            <jats:italic>TreePartNet<\/jats:italic>\n            , a neural network aimed at reconstructing tree geometry from point clouds obtained by scanning real trees. Our key idea is to learn a natural\n            <jats:italic>neural decomposition<\/jats:italic>\n            exploiting the assumption that a tree comprises locally cylindrical shapes. In particular, reconstruction is a two-step process. First, two networks are used to detect priors from the point clouds. One detects semantic branching points, and the other network is trained to learn a cylindrical representation of the branches. In the second step, we apply a neural merging module to reduce the cylindrical representation to a final set of generalized cylinders combined by branches. We demonstrate results of reconstructing realistic tree geometry for a variety of input models and with varying input point quality, e.g., noise, outliers, and incompleteness. We evaluate our approach extensively by using data from both synthetic and real trees and comparing it with alternative methods.\n          <\/jats:p>","DOI":"10.1145\/3478513.3480486","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T18:28:45Z","timestamp":1639160925000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":47,"title":["TreePartNet"],"prefix":"10.1145","volume":"40","author":[{"given":"Yanchao","family":"Liu","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences and Shenzhen University and NLPR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianwei","family":"Guo","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bedrich","family":"Benes","sequence":"additional","affiliation":[{"name":"Purdue University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Deussen","sequence":"additional","affiliation":[{"name":"SIAT and University of Konstanz, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaopeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,12,10]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"International Conference on Machine Learning (ICML)","volume":"80","author":"Achlioptas Panos","year":"2018","unstructured":"Panos Achlioptas , Olga Diamanti , Ioannis Mitliagkas , and Leonidas Guibas . 2018 . 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