{"created":"2025-01-19T01:17:27.204593+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00216942","sets":["1164:4619:10826:10881"]},"path":["10881"],"owner":"44499","recid":"216942","title":["高周波形状復元のための微分可能レンダラーを用いたデータ拡張の最適化"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-03-03"},"_buckets":{"deposit":"45dd5bb1-1ba3-4ac4-ae21-d8815daf6032"},"_deposit":{"id":"216942","pid":{"type":"depid","value":"216942","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"高周波形状復元のための微分可能レンダラーを用いたデータ拡張の最適化","author_link":["561055","561057","561056"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"高周波形状復元のための微分可能レンダラーを用いたデータ拡張の最適化"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"セッション2-B","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-03-03","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"九州大学"},{"subitem_text_value":"九州大学"},{"subitem_text_value":"九州大学"}]},"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/216942/files/IPSJ-CVIM22229011.pdf","label":"IPSJ-CVIM22229011.pdf"},"date":[{"dateType":"Available","dateValue":"2024-03-03"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM22229011.pdf","filesize":[{"value":"3.4 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"9ca1da1a-5a31-44a8-9822-4e1f56cb7775","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":"時枝, 康大"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"岩口, 尭史"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"川崎, 洋"}],"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":"構造化光を用いたアクティブステレオ法によるワンショット 3 次元形状復元は，計測結果が疎であり，高周波な形状は復元できないという問題がある．深層学習を用いて画像と疎な形状を入力して高密度な物体表面の形状を推定する手法が提案されているが，学習のためには実際に計測した 3 次元形状データセットから高周波形状を学習に用いるか，CG によるシミュレーションにおいて周波数や振幅などのパラメータを調整して実データに近いデータを作成することが必要になる．本論文では，CG におけるパラメータ調整の問題を，最近提案された深層学習における勾配降下法を用いたデータ拡張の最適化を用いて解消する手法を提案する．その際，従来のデータ拡張手法では，アフィン変換や色変換など基本的な画像処理のみしか適用できなかったのに対して，微分可能レンダラーを用いて 3 次元形状とその配置を変化させてレンダリングすることで，より広範な学習データを作成することができる．実験では，本手法により高周波形状復元のための深層学習が効率化されたことを実データによる実験で確認した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","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":"2022-03-03","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"11","bibliographicVolumeNumber":"2022-CVIM-229"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"links":{},"id":216942,"updated":"2025-01-19T15:40:47.919521+00:00"}