The convex hull of a set of points is the smallest convex set that contains the points. This article presents a practical convex hull algorithm that combines the two-dimensional Quickhull Algorithm with the general-dimension Beneath-Beyond Algorithm. It is similar to the randomized, incremental

> (right) of the line. In particular, for S upper we have S = S upper [ fu; vg with p 1 = u and p 2 = v; for S lower we set S = S lower [ fv; ug with p 1 = v and p 2 = u. Now we apply the following recursive method to S and (p 1 ; p 2 ): We determine the point pivot 2 S (called the pivot point) with the largest distance from line (p 1 ; p 2 ) (see Figure 2, left hand side), i.e. which maximizes the cross product #define cross(pivot,p1,p2) " ((x[p1]-x[pivot])*(y[p2]-y[pivot]) - (y[p1]-y[pivot])*(x[p2]-x[pivot])) Obviously, pivot belongs to the conve

step into a permutation step that is well-suited for graphics hardware. Our convex hull algorithm of choice is Quickhull. Our parallel Quickhull implementation (for both 2D and 3D cases) achieves an order of magnitude speedup over standard computational geometry libraries.

In this paper, a new algorithm based on the Quickhull algorithm is proposed to find convex hulls for 3-D objects using neighbor trees. The neighbor tree is the data structure by which all visible facets to the selected furthest outer point can be found. The neighboring sequence of ridges

and compared. The first three algorithms are the Brute Force, the Gift Wrap and the QuickHull algorithm. The fourth one is a hybrid approach that combines the QuickHull and the Gift Wrap algorithms. Simulations were done in the medical environment, and algorithms are tested with the model of 3D wrist and knee

This paper will present the implementation and comparison of new parallel algorithms for the convex hull problem. These algorithms are a parallel adaptation of the Jarvis March and the Quickhull algorithms. The computational model selected for these algorithms is the associative computing model

We present a framework for multi-core implementations of divide and conquer algorithms and show its efficiency and ease of use by applying it to the fundamental geometric problem of computing the convex hull of a point set. We concentrate on the Quickhull algorithm introduced in [2]. In general

and Cell Brodband Engine (Cell BE). We have parallelized the quickhull algorithm for two dimensional convex hull. The major advantage of this algorithm is that interprocessor communication cost is highly reduced. 1.

. It runs in 2-d, 3-d, 4-d, and higher dimensions. It implements the Quickhull algorithm for computing the convex hull. Qhull does not support constrained Delaunay triangulations, or mesh generation of non-convex objects, but the package does include some R functions that allow for this. Currently

We study strategies for local load balancing of irregular parallel divide-andconquer algorithms such as Quicksort and Quickhull in SPMD-parallel environments such as MPI and Fork that allow to exploit nested parallelism by dynamic group splitting.