{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T19:17:21Z","timestamp":1769800641468,"version":"3.49.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>3D deep learning performance depends on object representation and local feature extraction. In this work, we present MAT-Net, a neural network which captures local and global features from the Medial Axis Transform (MAT). Different from K-Nearest-Neighbor method which extracts local features by a fixed number of neighbors, our MAT-Net exploits effective modules Group-MAT and Edge-Net to process topological structure. Experimental results illustrate that MAT-Net demonstrates competitive or better performance on 3D shape recognition than state-of-the-art methods, and prove that MAT representation has excellent capacity in 3D deep learning, even in the case of low resolution.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/109","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"774-781","source":"Crossref","is-referenced-by-count":18,"title":["MAT-Net: Medial Axis Transform Network for 3D Object Recognition"],"prefix":"10.24963","author":[{"given":"Jianwei","family":"Hu","sequence":"first","affiliation":[{"name":"School of Software, Tsinghua University, China"},{"name":"Beijing National Research Center for Information Science and Technology (BNRist), China"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, China"},{"name":"Beijing National Research Center for Information Science and Technology (BNRist), China"}]},{"given":"Lihui","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, China"},{"name":"Beijing National Research Center for Information Science and Technology (BNRist), China"}]},{"given":"Yiling","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, China"},{"name":"Beijing National Research Center for Information Science and Technology (BNRist), China"}]},{"given":"Xiaohu","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Texas at Dallas, United States of America"}]},{"given":"Lingjie","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Hong Kong, Hong Kong"}]},{"given":"Wenping","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Hong Kong, Hong Kong"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:51Z","timestamp":1564300011000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/109"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/109","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}