Spatial Access Methods for Organizing Laserscanner Data

Laserscanning produces large sets of multidimensional point data, which demand for an effective and efficient organization and storage. Adequate data structures must perform specific spatial queries and operations in order to support the computation and / or the construction of surface models. Because of their increasing size, it is not advisable to organize the clouds of points by mainmemory data structures. Such an approach would lead to long loading times and misses scalability. Instead, persistent data structures are desirable. In this paper, the usage of multidimensional spatial access methods are investigated for organizing laserscanner data. Such access methods have originally been developed for storing and indexing geographic data in spatial database systems and Geographical Information Systems. Point access methods based on hierarchical hash trees are one important class of such access methods. Typical examples for hash trees are the BANG file and the buddy tree. Rectangle access methods are another class of relevant access methods. The R-tree and its variants are the most important representative of this class. R-trees are typically used by commercial spatial database systems. All data structures mentioned before are fully dynamic, i.e. they support arbitrary sequences of insertions, modifications and deletions. They allow a persistent storage of multidimensional points and preserve spatial proximity locally, i.e. within (database or file) blocks. The performance of the above point and rectangle access methods is investigated and compared for storing and querying large clouds of points representing buildings. The examination identifies those access methods that allow a fast construction of the data structure as well as an efficient support of relevant spatial queries. For some queries, however, a local preservation of spatial proximity is not sufficient. The extraction of points for overview purposes is an example for such a query. Therefore, different approaches for a global preservation of spatial proximity are introduced and experimentally investigated.