TY - JOUR
T1 - Disk Density Tuning of a Maximal Random Packing
AU - Ebeida, Mohamed S.
AU - Rushdi, Ahmad A.
AU - Awad, Muhammad A.
AU - Mahmoud, Ahmed H.
AU - Yan, Dongming
AU - English, Shawn A.
AU - Owens, John D.
AU - Bajaj, Chandrajit L.
AU - Mitchell, Scott A.
PY - 2016/8/1
Y1 - 2016/8/1
N2 - We introduce an algorithmic framework for tuning the spatial density of disks in a maximal random packing, without changing the sizing function or radii of disks. Starting from any maximal random packing such as a Maximal Poisson-disk Sampling (MPS), we iteratively relocate, inject (add), or eject (remove) disks, using a set of three successively more-aggressive local operations. We may achieve a user-defined density, either more dense or more sparse, almost up to the theoretical structured limits. The tuned samples are conflict-free, retain coverage maximality, and, except in the extremes, retain the blue noise randomness properties of the input. We change the density of the packing one disk at a time, maintaining the minimum disk separation distance and the maximum domain coverage distance required of any maximal packing. These properties are local, and we can handle spatially-varying sizing functions. Using fewer points to satisfy a sizing function improves the efficiency of some applications. We apply the framework to improve the quality of meshes, removing non-obtuse angles; and to more accurately model fiber reinforced polymers for elastic and failure simulations.
AB - We introduce an algorithmic framework for tuning the spatial density of disks in a maximal random packing, without changing the sizing function or radii of disks. Starting from any maximal random packing such as a Maximal Poisson-disk Sampling (MPS), we iteratively relocate, inject (add), or eject (remove) disks, using a set of three successively more-aggressive local operations. We may achieve a user-defined density, either more dense or more sparse, almost up to the theoretical structured limits. The tuned samples are conflict-free, retain coverage maximality, and, except in the extremes, retain the blue noise randomness properties of the input. We change the density of the packing one disk at a time, maintaining the minimum disk separation distance and the maximum domain coverage distance required of any maximal packing. These properties are local, and we can handle spatially-varying sizing functions. Using fewer points to satisfy a sizing function improves the efficiency of some applications. We apply the framework to improve the quality of meshes, removing non-obtuse angles; and to more accurately model fiber reinforced polymers for elastic and failure simulations.
UR - http://www.scopus.com/inward/record.url?scp=84982124830&partnerID=8YFLogxK
U2 - 10.1111/cgf.12981
DO - 10.1111/cgf.12981
M3 - Article
C2 - 27563162
AN - SCOPUS:84982124830
VL - 35
SP - 259
EP - 269
JO - Computer Graphics Forum
JF - Computer Graphics Forum
SN - 0167-7055
IS - 5
ER -