Journal
COMPUTERS & GRAPHICS-UK
Volume 31, Issue 2, Pages 157-174Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cag.2006.11.011
Keywords
neighbor finding; k nearest neighbors; kNN algorithm; all nearest neighbor algorithm; incremental neighbor finding algorithm; locality; neighborhood; disk-based data structures; point-cloud operations; point-cloud graphics
Categories
Ask authors/readers for more resources
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 (kNNs) 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 kNNs of a large collection of points many of which are already in close proximity. A fast kNN algorithm is presented that makes use of the locality of successive points whose k nearest neighbors are sought to reduce significantly 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 methods. (c) 2006 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available