A Fast k-Neighborhood Algorithm for Large Point-Clouds

Sankaranarayanan, Jagan; Samet, Hanan; Varshney, Amitabh

A Fast k-Neighborhood Algorithm for Large Point-Clouds

Files

075-084.pdf (296.86 KB)

Date

2006

Authors

Sankaranarayanan, Jagan
Samet, Hanan
Varshney, Amitabh

Publisher

The Eurographics Association

Abstract

Algorithms that use point-cloud models make heavy use of the neighborhoods of the points. These neighborhoods are used to compute the surface normals for each point, mollification, and noise removal. All of these primitive operations require the seemingly repetitive process of finding the k nearest neighbors of each point. These algorithms are primarily designed to run in main memory. However, rapid advances in scanning technologies have made available point-cloud models that are too large to fit in the main memory of a computer. This calls for more efficient methods of computing the k nearest neighbors of a large collection of points many of which are already in close proximity. A fast k nearest neighbor algorithm is presented that makes use of the locality of successive points whose k nearest neighbors are sought to significantly reduce the time needed to compute the neighborhood needed for the primitive operation as well as enable it to operate in an environment where the data is on disk. Results of experiments demonstrate an order of magnitude improvement in the time to perform the algorithm and several orders of magnitude improvement in work efficiency when compared with several prominent existing method.

        @inproceedings{:10.2312/SPBG/SPBG06/075-084
,
booktitle = {Symposium on Point-Based Graphics
},
editor = {Mario Botsch and Baoquan Chen and Mark Pauly and Matthias Zwicker
},
title = {{A Fast k-Neighborhood Algorithm for Large Point-Clouds
}},
author = {Sankaranarayanan, Jagan and 
Samet, Hanan and 
Varshney, Amitabh
},
year = {2006
},
publisher = {The Eurographics Association
},
ISSN = {1811-7813
},
ISBN = {3-905673-32-0
},
DOI = {/10.2312/SPBG/SPBG06/075-084
}
}