We describe a novel algorithm for bulk-loading an index with high-dimensional data and apply it to the problem of volumetric shape matching. Our matching and packing algorithm is a general approach for packing data according to a similarity metric. First, an approximate \(k\)-nearest neighbor graph is constructed using vantage-point initialization, an improvement to previous work that decreases construction time while improving the quality of approximation. Then, graph matching is iteratively performed to pack related items closely together. The end result is a dense index with good performance. We define a new query specification for shape matching that uses minimum and maximum shape constraints to explicitly specify the spatial requirements of the desired shape. This specification provides a natural language for performing volumetric shape matching and is readily supported by the geometry-based similarity search (GSS) tree, an indexing structure that maintains explicit representations of volumetric shape. We describe our implementation of a GSS tree for volumetric shape matching and provide a comprehensive evaluation of parameter sensitivity, performance, and scalability. Compared to previous bulk-loading algorithms, we find that matching and packing can construct a GSS tree index in the same amount of time that is denser, flatter, and better performing, with an observed average performance improvement of 2X.

中文翻译：

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使用匹配和填充索引体积形状。

我们描述了一种将高维数据批量装入索引的新颖算法，并将其应用于体积形状匹配问题。我们的匹配和打包算法是根据相似性指标打包数据的通用方法。首先，使用有利点初始化构造近似\（k \）-最近邻图，这是对先前工作的改进，它减少了构建时间，同时提高了近似质量。然后，迭代执行图匹配以将相关项紧密打包在一起。最终结果是具有良好性能的密集索引。我们定义了一个新的形状匹配查询规范，该规范使用最小和最大形状约束来明确指定所需形状的空间要求。该规范提供了一种用于执行体积形状匹配的自然语言，并且容易得到基于几何的相似性搜索（GSS）树的支持，该树是维护体积形状的显式表示的索引结构。我们描述了用于体积形状匹配的GSS树的实现，并提供了对参数敏感性，性能和可伸缩性的全面评估。