Title :
Geometrical and Topological Modelling: A Fast Computation of Spatial 3D TLS Data Selections
Author :
Rodrigues, Jose I. ; Figueiredo, Mauro ; Silvestre, Ivo ; Veiga-Pires, Cristina
Author_Institution :
ISE, Univ. of Algarve, Faro, Portugal
Abstract :
Underground caves and their specific structures are important for geomorphological studies. In this paper we present a new tool to identify and map speleothems by surveying cave chambers interiors. One of the research problems that we had to solve was that we were dealing with a great number of points that resulted from the Laser scan. The cave chamber was surveyed using Terrestrial Laser Scanning to acquire point clouds with high level of detail for 3D model generation. A point cloud of 45 million points was generated. This data set is important for either reconstruction of the 3D model, geomorphological studies or virtual visits to the cave. With this point cloud we generated a 3D-mesh to represent the surface model of the cave chamber. In this paper we present an octree data structure implemented using SQLite relational database management. Furthermore, it is also possible to work with several data models simultaneously, in such a way that it is not limited by computer memory resources. A topological structure of the 3D-mesh was also implemented to get an efficient algorithm to help identifying stalactites and stalagmites from big data 3D models of high detail.
Keywords :
SQL; geometry; geomorphology; relational databases; solid modelling; 3D model generation; SQLite relational database management; geometrical modelling; geomorphological studies; point clouds; spatial 3D TLS data selections; terrestrial laser scanning; topological modelling; underground caves; Computational modeling; Data models; Octrees; Solid modeling; Surface reconstruction; Three-dimensional displays; SQLite; cave model; geometrical models; octree data structure; topological models;
Conference_Titel :
Big Data (BigData Congress), 2015 IEEE International Congress on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4673-7277-0
DOI :
10.1109/BigDataCongress.2015.13