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Out‐of‐Core GPU ‐based Change Detection in Massive 3 D Point Clouds
Author(s) -
Richter Rico,
Kyprianidis Jan Eric,
Döllner Jürgen
Publication year - 2013
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/j.1467-9671.2012.01362.x
Subject(s) - computer science , rendering (computer graphics) , preprocessor , point cloud , visualization , computation , data structure , computer graphics (images) , data mining , artificial intelligence , algorithm , programming language
If sites, cities, and landscapes are captured at different points in time using technology such as LiDAR , large collections of 3 D point clouds result. Their efficient storage, processing, analysis, and presentation constitute a challenging task because of limited computation, memory, and time resources. In this work, we present an approach to detect changes in massive 3 D point clouds based on an out‐of‐core spatial data structure that is designed to store data acquired at different points in time and to efficiently attribute 3 D points with distance information. Based on this data structure, we present and evaluate different processing schemes optimized for performing the calculation on the CPU and GPU . In addition, we present a point‐based rendering technique adapted for attributed 3 D point clouds, to enable effective out‐of‐core real‐time visualization of the computation results. Our approach enables conclusions to be drawn about temporal changes in large highly accurate 3 D geodata sets of a captured area at reasonable preprocessing and rendering times. We evaluate our approach with two data sets from different points in time for the urban area of a city, describe its characteristics, and report on applications.