Spatial sorting: An efficient strategy for approximate nearest neighbor searching

Many graphics and also non-graphics applications need efficient techniques to find the nearest neighbors of a given query point. There are two approaches to address this problem: space-partitioning and data-partitioning. We present a data-partitioning error-controlled strategy for solving the nearest neighbor search (NNS) problem using spatial sorting as the basic building block. We improve on the neighborhood grid method by doing an extensive study on novel spatial sorting strategies for bidimensional NNS, providing significant performance and precision gains over previous works. Experiments demonstrate that, for many dense 2D point distributions, our solution is competitive with more complex and traditional techniques, such as k-d trees and index sorting. We also show comparable results for the 3D case. Our primary contribution is a dynamic, simple to implement, memory efficient, and highly parallelizable solution for low-dimensional approximate nearest neighbor search. Graphical abstractThis paper discusses in depth the problem of spatially sorting points in 2D and 3D, aiming to perform a fast, dynamic, and memory-efficient approximate nearest neighbor search.Display Omitted HighlightsWe analyze in depth the problem of spatially sorting points in two and three dimensions.We present novel and efficient spatial sorting algorithms.The performance and precision for nearest neighbor search (NNS) is evaluated.We show that spatial sorting is competitive against current NNS techniques.