{"id":218557,"created":"2025-01-19T01:18:55.457975+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218557","sets":["1164:3206:10884:10949"]},"path":["10949"],"owner":"44499","recid":"218557","title":["微分可能レンダリングによるガウシアンフィルタリングのパラメータ推定"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-06-20"},"_buckets":{"deposit":"def22dd9-a194-46ab-b25f-da9299f446b2"},"_deposit":{"id":"218557","pid":{"type":"depid","value":"218557","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"微分可能レンダリングによるガウシアンフィルタリングのパラメータ推定","author_link":["568489","568490"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"微分可能レンダリングによるガウシアンフィルタリングのパラメータ推定"}]},"item_type_id":"4","publish_date":"2022-06-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"和歌山大学"},{"subitem_text_value":"和歌山大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Wakayama University","subitem_text_language":"en"},{"subitem_text_value":"Wakayama University","subitem_text_language":"en"}]},"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/218557/files/IPSJ-CG22186004.pdf","label":"IPSJ-CG22186004.pdf"},"date":[{"dateType":"Available","dateValue":"2024-06-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CG22186004.pdf","filesize":[{"value":"803.7 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"28"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"95e65916-465b-4f80-82a1-c5e347ec230e","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":"北林, 悠河"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"岩崎, 慶"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10100541","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":"2188-8949","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，並列計算機の急速な発展を背景に，機械学習をレンダリングパイプラインに取り込む形式で特殊化した，微分可能レンダラと呼ばれるレンダリングパラメータ推定手法が注目されている．Laine らは，プリミティブと呼ばれる処理単位を定義し，それらを組み合わせてパイプラインを構築する，モジュール式の微分可能レンダリングパイプラインを提案した．これにより，柔軟性・汎用性に優れた微分可能レンダラの構築を可能とした．本稿では，Laine らが提案したモジュール式の微分可能レンダリングパイプラインを拡張し，ガウシアンフィルタのフィルタ強度を推定する手法を提案する．連続的なパラメータでガウシアンフィルタのフィルタ強度を定義し，勾配法によって高精度に学習が行えることを示す．また，他のレンダリングパラメータ学習とガウシアンフィルタとの親和性についても検証する．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータグラフィックスとビジュアル情報学（CG）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-06-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicVolumeNumber":"2022-CG-186"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T15:07:32.878048+00:00","links":{}}