<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Journal Article</dc:product_type><dc:title>Learning Canonical Embeddings for Unsupervised Shape Correspondence With Locally Linear Transformations</dc:title><dc:creator>He, Pan; Emami, Patrick; Ranka, Sanjay; Rangarajan, Anand</dc:creator><dc:corporate_author/><dc:editor/><dc:description/><dc:publisher>IEEE</dc:publisher><dc:date>2023-12-01</dc:date><dc:nsf_par_id>10537168</dc:nsf_par_id><dc:journal_name>IEEE Transactions on Pattern Analysis and Machine Intelligence</dc:journal_name><dc:journal_volume>45</dc:journal_volume><dc:journal_issue>12</dc:journal_issue><dc:page_range_or_elocation>14872 to 14887</dc:page_range_or_elocation><dc:issn>0162-8828</dc:issn><dc:isbn/><dc:doi>https://doi.org/10.1109/TPAMI.2023.3307592</dc:doi><dcq:identifierAwardId>1922782</dcq:identifierAwardId><dc:subject>Implicit correspondence learning, locally linear
transformations, point cloud reconstruction, probability density
functions, unsupervised shape correspondence.</dc:subject><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>