Point Cloud Segmentation for Cultural Heritage Sites

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Date
2011
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Over the past few years, the acquisition of 3D point information representing the structure of real-world objects has become common practice in many areas. This is particularly true in the Cultural Heritage (CH) domain, where point clouds reproducing important and usually unique artifacts and sites of various sizes and geometric complexities are acquired. Specialized software is then usually used to process and organise this data. This paper addresses the problem of automatically organising this raw data by segmenting point clouds into meaningful subsets. This organisation over raw data entails a reduction in complexity and facilitates the post-processing effort required to work with the individual objects in the scene. This paper describes an efficient two-stage segmentation algorithm which is able to automatically partition raw point clouds. Following an intial partitioning of the point cloud, a RanSaC-based plane fitting algorithm is used in order to add a further layer of abstraction. A number of potential uses of the newly processed point cloud are presented; one of which is object extraction using point cloud queries. Our method is demonstrated on three point clouds ranging from 600K to 1.9M points. One of these point clouds was acquired from the pre-historic temple of Mnajdra consistsing of multiple adjacent complex structures.
Description

        
@inproceedings{
:10.2312/VAST/VAST11/041-048
, booktitle = {
VAST: International Symposium on Virtual Reality, Archaeology and Intelligent Cultural Heritage
}, editor = {
Franco Niccolucci and Matteo Dellepiane and Sebastian Pena Serna and Holly Rushmeier and Luc Van Gool
}, title = {{
Point Cloud Segmentation for Cultural Heritage Sites
}}, author = {
Spina, Sandro
and
Debattista, Kurt
and
Bugeja, Keith
and
Chalmers, Alan
}, year = {
2011
}, publisher = {
The Eurographics Association
}, ISSN = {
1811-864X
}, ISBN = {
978-3-905674-34-7
}, DOI = {
/10.2312/VAST/VAST11/041-048
} }
Citation