vastyellownew,github

In this paper, a novel tree structure, namely semi-convex hull tree, is proposed, and based on it a fast nearest neighbor (NN) query algorithm for large scale data is given. In the novel tree, each node represents a convex set, in fact semi-convex hull, which consists of some linear constraints. The new NN algorithm ﬁlters large amounts of redundant distance computations by quadratic programming (QP). In order to perform the proposed algorithm on GPUs, a simpliﬁed quadratic programming is also proposed to retrieve approximate lower bound of the distance from a query point to a node. In addition, a sample based version of the proposed algorithm is developed as well, which accelerates the original semi-convex hull tree much in large scale data. Experiments conducted on diﬀerent GPUs show that the proposed algorithm is promising, which yields valuable improvements and superiority to the other NN query methods, including k-d tree, cover tree etc.

sde

OpenGL and WebGL based kernel density estimation library

sdf-generator

Create a signed distance function to a closed surface using the algebraic point set surface method, for use as an initial condition in fluid dynamics simulations.

sdfgan

3D Signed Distance Function Based Generative Adversarial Networks

semi-convex_hull_tree

A fast exact nearest neighbor search algorithm over large scale data is proposed based on semi-convex hull tree, where each node represents a semi-convex hull, which is made of a set of hyper planes. When performing the task of nearest neighbor queries, unnecessary distance computations can be greatly reduced by quadratic programming. GPUs are also used to accelerate the query process. Experiments conducted on both Intel(R) HD Graphics 4400 and Nvidia Geforce GTX1050 TI, as well as theoretical analysis show that the proposed algorithm yields signiﬁcant improvements and outperforms current k-d tree based nearest neighbor query algorithms and others.