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Efficient Geometry-based Similarity Search of 3D Spatial Databases

机译:3D空间数据库的高效基于几何的相似性搜索

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Searching a database of 3D-volume objects for objects which are similar to a given 3D search object is an important problem which arises in number of database applications-for example, in Medicine and CAD. In this paper, we present a new geometry-based solution to the problem of searching for similar 3D-volume objects. The problem is motivated from a real application in the medical domain where volume similarity is used as a basis for surgery decisions. Our solution for an efficient similarity search on large databases of 3D volume objects is based on a new geometric index structure. The basic idea of our new approach is to use the concept of hierarchical approximations of the 3D objects to speed up the search process. We formally show the correctness of our new approach and introduce two instantiations of our general idea, which are based on cuboid and octree approximations. We finally provide a performance evaluation of our new index structure revealing significant performance improvements over existing approaches.
机译:在类似于给定3D搜索对象的对象中搜索3D卷对象的数据库是数据库应用程序数量的重要问题 - 例如,在医学和CAD中。在本文中,我们为搜索类似的3D卷对象的问题提出了一种新的基于几何的解决方案。问题是在医学领域的真实应用中的激励,其中体积相似度被用作外科决策的基础。我们在3D卷对象的大型数据库上进行高效相似性搜索的解决方案基于新的几何索引结构。我们新方法的基本思想是使用3D对象的分层近似的概念来加速搜索过程。我们正式展示了我们新方法的正确性,并介绍了我们一般思想的两个实例化,这是基于长方体和八角型近似的。我们终于提供了对我们新的指数结构进行了绩效评估,揭示了对现有方法的显着性能改进。

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