Fast Construction of k-Nearest Neighbor Graphs for Point Clouds

Authors:
Michael Connor;Piyush Kumar
Affiliations:
Florida State University, Tallahassee;Florida State University, Tallahassee
Venue:
IEEE Transactions on Visualization and Computer Graphics
Year:
2010

Citing 0
Cited 14

Parallel sorting pattern

Proceedings of the 2010 Workshop on Parallel Programming Patterns
Efficient k-nearest neighbor graph construction for generic similarity measures

Proceedings of the 20th international conference on World wide web
A new algorithm for segmenting warped text-lines in document images

Proceedings of the 2011 ACM Symposium on Applied Computing
Instant approximate 1-center on road networks via embeddings

Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Practical nearest neighbor search in the plane

SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
Dense Neighborhoods on Affinity Graph

International Journal of Computer Vision
Visible neighborhood graph of point clouds

Graphical Models
Fast approximate nearest-neighbor search with k-nearest neighbor graph

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Solution to PDEs using radial basis function finite-differences (RBF-FD) on multiple GPUs

Journal of Computational Physics
A facial composite editor for blendshape characters

Proceedings of the Digital Production Symposium
An algorithm for discovering clusters of different densities or shapes in noisy data sets

Proceedings of the 28th Annual ACM Symposium on Applied Computing
New sequential and parallel algorithms for computing the β-spectrum

FCT'13 Proceedings of the 19th international conference on Fundamentals of Computation Theory
Iterative cage-based registration from multi-view silhouettes

Proceedings of the 10th European Conference on Visual Media Production
Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a parallel algorithm for k-nearest neighbor graph construction that uses Morton ordering. Experiments show that our approach has the following advantages over existing methods: 1) faster construction of k-nearest neighbor graphs in practice on multicore machines, 2) less space usage, 3) better cache efficiency, 4) ability to handle large data sets, and 5) ease of parallelization and implementation. If the point set has a bounded expansion constant, our algorithm requires one-comparison-based parallel sort of points, according to Morton order plus near-linear additional steps to output the k-nearest neighbor graph.