Shape-based comparison of 3D models and its applications

基于形状的3D模型比较及其应用

• 批准号: 17500066
• 负责人: OHBUCHI Ryutarou
• 金额: $ 2.41万
• 依托单位: University of Yamanashi
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2005
• 资助国家: 日本
• 起止时间: 2005 至 2007
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17500066/
• 关键词: Multimedia information retrieval; 3D geometric modeling; Machine learning; 3D computer graphics; Data mining; Image. text. sneech recognition; I Database; Human machine interaction; 3次元形状データベース; 学習アルゴリズム

Our research has produced the following to contributions:・ Retrieval method based on semantic classes: Shape as well as semantics play equally important roles in comparing 3D models by their "shape". Tb incorporate semantics associated with 3D mcdri9 into their "shape-based" comparison, we have developed a learning-based method. The method learns, offline, a set of multiple semantic labels attached to models in a 3D model database 'lb efficiently learn labels from a relatively small set of labeled mark's we introduced a semi-supervised approach to learning, which employs both labeled and unlabeled 3D models for the learning. Our empirical evaluation showed that the proposed approach does improve the retrieval performance of several 3D model retrieval benchmarks significantly. The method is believed to be the first to successfully learn semantic classes for 3D model retrieval. We believe that, fit the SHREC 2006 benchmark, the said method is most likely the best performing method as of … More March 2008.・ Local, multi-male, image features for retrieval: Shape comparison methods can be classified by their ketones into two globalleatme based methods and Iccalleatme based methods. The proposed method employs the latest local fratare. The method uses a set of local, multi-scale, image-based features extracted from a 3D model in compare the chap, of the model The method has an advantage over global-feature based methods for articulated models and models having global deformations Experimental evaluation of the method using a databa.se of articulated 3D model is showed that the method performs comparably to the best known method. Furthermore, our method is more versatile than the competing method in that our method can handle diverse 3D shape representations.Noteworthy achievement ; We entered the CAD model track of the Shape Retrieval (latest (SHREC) 2007, a 3D model retrieval contest, and won the track, proving the superiority of our algorithm. The winning algorithm is what we proposed in 2006 that employs unsupervised distance metric learning. We are planning to enter the SHREC 2008 contest, as our method based on semantic classes (above) Ear outperforms our own method based on distance metric learning, which won the SHREC 2007 CAD track. Less

·基于语义类别的检索方法：形状和语义在3D模型的形状比较中起着同等重要的作用。将与3Dmcdri9相关的语义融入到他们的基于形状的比较中，我们开发了一种基于学习的方法。该方法离线学习附加到3D模型数据库中的模型的多个语义标签的集合。从相对较小的已标记标记集合中高效地学习标签。我们引入了半监督学习方法，其使用已标记和未标记的3D模型来进行学习。我们的实验评估表明，该方法确实显著提高了几个3D模型检索基准的检索性能。该方法被认为是第一个成功学习3D模型检索的语义类的方法。我们认为，在符合SHREC2006基准的情况下，上述方法很可能是截至…时性能最好的方法更多的是2008年3月。·用于检索的局部、多男性图像特征：形状比较方法可按其酮分为两种基于GLOBALLEATME的方法和基于Iccalleatme的方法。所提出的方法使用了最新的本地兄弟会。该方法使用从3D模型中提取的一组局部、多尺度、基于图像的特征来比较模型的CHAP，对于关节模型和具有全局变形的模型，该方法优于基于全局特征的方法。使用关节3D模型数据库对该方法进行了实验评估。此外，我们的方法比竞争的方法更具通用性，因为我们的方法可以处理不同的3D形状表示。值得注意的是，我们进入了3D模型检索大赛(最新的(SHREC)2007)的CAD模型赛道，并赢得了比赛，证明了我们算法的优越性。获胜的算法是我们在2006年提出的采用无监督距离度量学习的算法。我们计划参加SHREC 2008大赛，因为我们基于语义类(上图)EAR的方法优于我们自己的基于距离度量学习的方法，后者赢得了SHREC 2007 CAD Track。较少

项目成果

多重フーリエスペクトル表現に基づく3次元モデルの形状類似検索

基于多个傅立叶光谱表示的 3D 模型形状相似度搜索

• 发表时间: 2008
• 期刊: 電子情報通信学会論文誌 J91-D
• 作者: 立間 淳司;関 洋平;青野 雅樹;大渕 竜太郎
• 通讯作者: 大渕 竜太郎

多重解像度表現を用いたポリゴンスープ3次元モデルの形状類似比較

使用多分辨率表示的多边形汤 3D 模型的形状相似度比较

• 发表时间: 2005
• 期刊: 画像電子学会誌 34巻4号
• 作者: 武井 毅;大渕 竜太郎
• 通讯作者: 大渕 竜太郎

3D Model Retrieval Based on Multi-Fourier Spectra Representation

基于多重傅里叶谱表示的3D模型检索

• 期刊: IEICE TRANSACTIONS on Information and Systems (Japanese Edition) Vol.J91-D, No.1
• 作者: Atsushi;Tatsuma;Yohei;Seki;Masaki;Aono;Ryutarou;Ohbuchi
• 通讯作者: Ohbuchi

「研究成果報告書概要(和文)」より

摘自《研究结果报告摘要（日文）》

• 发表时间: 2005
• 作者: Kawauchi;et. al.;Nishimura et al.;Dezawa et al.;Yoshizawa et al.;星野 幹雄;星野 幹雄
• 通讯作者: 星野 幹雄

顕著度を考慮した多視点画像特徴量を用いた三次元形状類似検索

考虑显着性的多视图图像特征的3D形状相似度搜索

• 发表时间: 2007
• 期刊: 情報科学技術レターズ 6
• 作者: 長田 邦男;坂野 智久;大渕 竜太郎
• 通讯作者: 大渕 竜太郎