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首页> 外文期刊>Visualization and Computer Graphics, IEEE Transactions on >Active Exploration of Large 3D Model Repositories
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Active Exploration of Large 3D Model Repositories

机译：积极探索大型3D模型资料库

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摘要

With broader availability of large-scale 3D model repositories, the need for efficient and effective exploration becomes more and more urgent. Existing model retrieval techniques do not scale well with the size of the database since often a large number of very similar objects are returned for a query, and the possibilities to refine the search are quite limited. We propose an interactive approach where the user feeds an active learning procedure by labeling either entire models or parts of them as “like” or “dislike” such that the system can automatically update an active set of recommended models. To provide an intuitive user interface, candidate models are presented based on their estimated relevance for the current query. From the methodological point of view, our main contribution is to exploit not only the similarity between a query and the database models but also the similarities among the database models themselves. We achieve this by an offline pre-processing stage, where global and local shape descriptors are computed for each model and a sparse distance metric is derived that can be evaluated efficiently even for very large databases. We demonstrate the effectiveness of our method by interactively exploring a repository containing over 100 K models.
机译：随着大型3D模型资料库的广泛普及，对有效探索的需求变得越来越紧迫。现有的模型检索技术不能很好地适应数据库的规模，因为通常会为查询返回大量非常相似的对象，并且改进搜索的可能性非常有限。我们提出了一种交互式方法，用户可以通过将整个模型或模型的一部分标记为“喜欢”或“不喜欢”来提供主动学习程序，以便系统可以自动更新一组主动推荐模型。为了提供直观的用户界面，将基于候选模型与当前查询的估计相关性来显示候选模型。从方法论的角度来看，我们的主要贡献不仅在于利用查询与数据库模型之间的相似性，还在于利用数据库模型本身之间的相似性。我们通过离线预处理阶段来实现这一目标，在该阶段中，将为每个模型计算全局和局部形状描述符，并导出一个稀疏距离度量标准，即使对于非常大的数据库也可以有效地对其进行评估。我们通过交互式探索包含100多个K模型的存储库来证明我们方法的有效性。

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