TY - JOUR
T1 - A fast all nearest neighbor algorithm for applications involving large point-clouds
JF - Computers & Graphics
Y1 - 2007
A1 - Sankaranarayanan,Jagan
A1 - Samet, Hanan
A1 - Varshney, Amitabh
KW - All nearest neighbor algorithm
KW - Disk-based data structures
KW - Incremental neighbor finding algorithm
KW - k Nearest neighbors
KW - kNN Algorithm
KW - Locality
KW - Neighbor finding
KW - Neighborhood
KW - Point-cloud graphics
KW - Point-cloud operations
AB - 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.
VL - 31
SN - 0097-8493
UR - http://www.sciencedirect.com/science/article/pii/S0097849306002378
CP - 2
M3 - 10.1016/j.cag.2006.11.011
ER -