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Graph."],"published-print":{"date-parts":[[2018,12,31]]},"abstract":"<jats:p>\n                    We introduce SCORES, a\n                    <jats:italic>recursive neural network<\/jats:italic>\n                    for\n                    <jats:italic>shape composition.<\/jats:italic>\n                    Our network takes as input sets of parts from two or more source 3D shapes and a rough initial placement of the parts. It outputs an optimized part structure for the composed shape, leading to high-quality geometry construction. A unique feature of our composition network is that it is not merely learning how to connect parts. Our goal is to produce a coherent and\n                    <jats:italic>plausible<\/jats:italic>\n                    3D shape, despite large incompatibilities among the input parts. The network may significantly alter the geometry and structure of the input parts and\n                    <jats:italic>synthesize<\/jats:italic>\n                    a novel shape structure based on the inputs, while adding or removing parts to minimize a structure plausibility loss. We design SCORES as a\n                    <jats:italic>recursive autoencoder<\/jats:italic>\n                    network. During encoding, the input parts are recursively grouped to generate a root code. During synthesis, the root code is decoded, recursively, to produce a new, coherent part assembly. Assembled shape structures may be novel, with little global resemblance to training exemplars, yet have plausible substructures. SCORES therefore learns a hierarchical\n                    <jats:italic>substructure shape prior<\/jats:italic>\n                    based on per-node losses. It is trained on structured shapes from ShapeNet, and is applied iteratively to reduce the plausibility loss. We show results of shape composition from multiple sources over different categories of man-made shapes and compare with state-of-the-art alternatives, demonstrating that our network can significantly expand the range of composable shapes for assembly-based modeling.\n                  <\/jats:p>","DOI":"10.1145\/3272127.3275008","type":"journal-article","created":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T14:16:10Z","timestamp":1543414570000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":39,"title":["SCORES"],"prefix":"10.1145","volume":"37","author":[{"given":"Chenyang","family":"Zhu","sequence":"first","affiliation":[{"name":"Simon Fraser University and National University of Defense Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Xu","sequence":"additional","affiliation":[{"name":"National University of Defense Technology and Princeton University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siddhartha","family":"Chaudhuri","sequence":"additional","affiliation":[{"name":"Adobe Research and IIT Bombay"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renjiao","family":"Yi","sequence":"additional","affiliation":[{"name":"Simon Fraser University and National University of Defense Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Simon Fraser University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,12,4]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601102"},{"key":"e_1_2_2_2_1","unstructured":"Sara Ball. 1985. 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