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Graph."],"published-print":{"date-parts":[[2019,12,31]]},"abstract":"<jats:p>\n            We introduce RPM-Net, a deep learning-based approach which simultaneously infers\n            <jats:italic>movable parts<\/jats:italic>\n            and hallucinates their\n            <jats:italic>motions<\/jats:italic>\n            from a single, un-segmented, and possibly partial, 3D point cloud shape. RPM-Net is a novel Recurrent Neural Network (RNN), composed of an encoder-decoder pair with interleaved Long Short-Term Memory (LSTM) components, which together predict a temporal sequence of\n            <jats:italic>pointwise displacements<\/jats:italic>\n            for the input point cloud. At the same time, the displacements allow the network to learn movable parts, resulting in a motion-based shape segmentation. Recursive applications of RPM-Net on the obtained parts can predict finer-level part motions, resulting in a hierarchical object segmentation. Furthermore, we develop a separate network to estimate part mobilities, e.g., per-part motion parameters, from the segmented motion sequence. Both networks learn deep predictive models from a training set that exemplifies a variety of mobilities for diverse objects. We show results of simultaneous motion and part predictions from synthetic and real scans of 3D objects exhibiting a variety of part mobilities, possibly involving multiple movable parts.\n          <\/jats:p>","DOI":"10.1145\/3355089.3356573","type":"journal-article","created":{"date-parts":[[2019,11,8]],"date-time":"2019-11-08T20:27:58Z","timestamp":1573244878000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":44,"title":["RPM-Net"],"prefix":"10.1145","volume":"38","author":[{"given":"Zihao","family":"Yan","sequence":"first","affiliation":[{"name":"Shenzhen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruizhen","family":"Hu","sequence":"additional","affiliation":[{"name":"Shenzhen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingguang","family":"Yan","sequence":"additional","affiliation":[{"name":"Shenzhen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luanmin","family":"Chen","sequence":"additional","affiliation":[{"name":"Shenzhen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Van Kaick","sequence":"additional","affiliation":[{"name":"Carleton University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Simon Fraser University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,11,8]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1006\/cviu.1995.1050"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2014.6907311"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.1994.351268"},{"key":"e_1_2_2_4_1","volume-title":"Proc. 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