Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Efficient simplification of point-sampled surfaces
Proceedings of the conference on Visualization '02
Edge-Region-Based Segmentation of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
High fidelity reconstruction of the ancient Egyptian temple of Kalabsha
AFRIGRAPH '04 Proceedings of the 3rd international conference on Computer graphics, virtual reality, visualisation and interaction in Africa
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Web-based 3D Reconstruction Service
Machine Vision and Applications
Journal on Computing and Cultural Heritage (JOCCH)
A benchmark for 3D mesh segmentation
ACM SIGGRAPH 2009 papers
Tree Segmentation from Scanned Scene Data
PMA '09 Proceedings of the 2009 Plant Growth Modeling, Simulation, Visualization, and Applications
Post-processing of scanned 3D surface data
SPBG'04 Proceedings of the First Eurographics conference on Point-Based Graphics
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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.