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        <datestamp>2025-01-22T06:44:03Z</datestamp>
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          <dc:title>材質感モデルの自動生成</dc:title>
          <dc:title xml:lang="en">Modeling Indoor Scene by Determining its Reflection Parameters</dc:title>
          <jpcoar:creator>
            <jpcoar:creatorName>原田, 貴昭</jpcoar:creatorName>
            <jpcoar:creatorName>原, 健二</jpcoar:creatorName>
            <jpcoar:creatorName>中澤, 篤志</jpcoar:creatorName>
            <jpcoar:creatorName>斎藤, 博昭</jpcoar:creatorName>
            <jpcoar:creatorName>池内, 克史</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Takaaki, Harada</jpcoar:creatorName>
            <jpcoar:creatorName xml:lang="en">Kenji, Hara</jpcoar:creatorName>
            <jpcoar:creatorName xml:lang="en">Atsushi, Nakazawa</jpcoar:creatorName>
            <jpcoar:creatorName xml:lang="en">Hiroaki, Saito</jpcoar:creatorName>
            <jpcoar:creatorName xml:lang="en">Katsushi, Ikeuchi</jpcoar:creatorName>
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          <datacite:description descriptionType="Other">本稿では，屋内環境を対象として，距離画像とカラー画像から材質感が得られる幾何／光学モデルを自動生成する手法を提案する．屋内環境を対象とする場合，膨大なデータ量と計測時の空間的な制約が問題となる．そこで，大規模データに対応した幾何モデル化手法と1枚画像を入力とする光学モデル化手法を用いる．また，テクスチャマッピングを行うために幾何モデルと複数カラー画像の同時位置合わせ手法を用いる．これらの手法を統合することにより，リアルな材質感モデルの生成が可能となる．</datacite:description>
          <datacite:description descriptionType="Other">To realistically model indoor scene is crucial and challenging problem. Indoor scene has a large number of three-dimensional range data and spatial restriction in two-dimensional photographing, as well as inevitable specularities in certain parts. In this paper we intend to deal with such a problem. Principally, our method use three-dimensional (3D) geometrical data and ordinary two-dimensional (2D) color images. The 3D geometrical data is created using parallel alignment and merging of a large number of range data. We texture the 3D data with 2D color images using simultaneous registering technique. Then, we estimate the reflection parameters of the diffuse and specular reflection components from a single image where specularities occur. Finally, using all acquired and estimated data, we can generate synthetic images of real objects realistically.</datacite:description>
          <dc:publisher xml:lang="ja">情報処理学会</dc:publisher>
          <datacite:date dateType="Issued">2004-01-22</datacite:date>
          <dc:language>jpn</dc:language>
          <dc:type rdf:resource="http://purl.org/coar/resource_type/c_18gh">technical report</dc:type>
          <jpcoar:identifier identifierType="URI">https://ipsj.ixsq.nii.ac.jp/records/52531</jpcoar:identifier>
          <jpcoar:sourceIdentifier identifierType="NCID">AA11131797</jpcoar:sourceIdentifier>
          <jpcoar:sourceTitle>情報処理学会研究報告コンピュータビジョンとイメージメディア（CVIM）</jpcoar:sourceTitle>
          <jpcoar:volume>2004</jpcoar:volume>
          <jpcoar:issue>6(2003-CVIM-142)</jpcoar:issue>
          <jpcoar:pageStart>45</jpcoar:pageStart>
          <jpcoar:pageEnd>52</jpcoar:pageEnd>
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            <jpcoar:extent>1.1 MB</jpcoar:extent>
            <datacite:date dateType="Available">2006-01-22</datacite:date>
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