{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00217853","sets":["1164:4619:10826:10925"]},"path":["10925"],"owner":"44499","recid":"217853","title":["深層学習を用いた三次元点群処理入門"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-05-05"},"_buckets":{"deposit":"6f5d9aa3-78c9-43c0-a491-c00952ce81f9"},"_deposit":{"id":"217853","pid":{"type":"depid","value":"217853","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"深層学習を用いた三次元点群処理入門","author_link":["565203"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"深層学習を用いた三次元点群処理入門"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":" CVIMチュートリアル ","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-05-05","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"早稲田大学／オムロンサイニックエックス株式会社"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/217853/files/IPSJ-CVIM22230040.pdf","label":"IPSJ-CVIM22230040.pdf"},"date":[{"dateType":"Available","dateValue":"2024-05-05"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM22230040.pdf","filesize":[{"value":"578.7 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"65a280d5-af13-4ff5-84f6-1f91aba7b672","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"千葉, 直也"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11131797","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8701","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"三次元点群とは，三次元表面形状を記述する手法の一つであり，物体表面の点の集合としてその物体の形状を表します．三次元センサの普及，計算機の性能向上に伴い，三次元点群を利用したアプリケーションが一般的になりつつあります．三次元点群の実空間スケールのまま形状・位置姿勢などを記述できるという特徴から，これまで（ニューラルネットワークを用いない）三次元点群処理は特にロボットビジョン・SLAM などの文脈で研究が進められてきました．一方で，三次元点群は画像などと異なり整列されていない（順序付けられていない）データ構造であるため，ニューラルネットワークで扱うためには工夫が必要でした．本チュートリアルでは，PointNet と DeepSets の提案以降に発展した，ニューラルネットワークによって三次元点群を取り扱うための手法について紹介します．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-05-05","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"40","bibliographicVolumeNumber":"2022-CVIM-230"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":217853,"updated":"2025-01-19T15:21:26.940381+00:00","links":{},"created":"2025-01-19T01:18:16.860341+00:00"}