将空间匹配拓展到三维，支持带高度的实体搜索
3D Spatial Pattern Matching
- 提出三维空间模式匹配新方法，支持高程信息
- 在合成与汉堡真实建筑数据集上验证，提供基线结果
- 适合城市规划、智能导航等需要三维定位的场景
空间模式匹配是将查询实体和约束与数据库中的实体及关系进行匹配的过程，广泛应用于相似区域搜索、住房市场搜索、地标搜索和道路网络匹配。现有方法均基于二维空间框架，实体位于笛卡尔平面上，关系也局限于二维。然而，在搜索具有高度的实际物体时，该框架存在显著局限。为此，本文将空间模式匹配扩展至三维空间，提出问题的广义定义。设计了一种能够处理距离关系的子图匹配算法，并发布了两个三维空间模式匹配数据集：一个合成数据集和一个包含德国汉堡市真实三维建筑数据的实测数据集。在两个数据集上测试了所提算法，提供了未来研究可参照的基准结果。
原文摘要 · Abstract (English)
Spatial pattern matching is the process of matching query entities and constraints with database entities and relations. It has many applications, including similar region search, housing market search, landmark search, and road network matching. To our knowledge, all existing spatial pattern matching approaches frame the problem in a 2 dimensional space, where entities lie in a cartesian plane and relationships defined between them are contained in 2 dimensions. However, this problem framing has significant limitations when searching for real world entities that have height in addition to position. To address this limitation, we extend spatial pattern matching to 3 dimensions and provide a generalized definition of the problem. We describe a subgraph matching algorithm capable of resolving 3D spatial patterns over distance relations and release two 3D spatial pattern matching datasets, one synthetic and one containing real 3D building data from the city of Hamburg, Germany. We test our subgraph matching algorithm on both datasets and present results as a baseline for future methods to build upon.
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