A 2D Advancing-Front Delaunay Mesh Refinement Algorithm
Shankar Prasad Sastry

TL;DR
This paper introduces a generalized 2D advancing-front Delaunay mesh refinement algorithm that produces size-optimal meshes with controlled angles by solving ODEs for segment splitting and using off-center Steiner vertices.
Contribution
It extends Chew's algorithm to generate size-optimal Delaunay meshes with angle bounds, handling constrained PSLGs and local feature size adaptation.
Findings
Produces size-optimal Delaunay meshes with minimum angles less than π/6.
Maintains angle bounds even with small input angles.
Uses ODE-based segment splitting for local feature size adaptation.
Abstract
I present a generalization of Chew's first algorithm for Delaunay mesh refinement. In his algorithm, Chew splits the line segments of the input planar straight line graph (PSLG) into shorter subsegments whose lengths are nearly identical. The constrained Delaunay triangulation of the subsegments is refined based on the length of the radii of the circumcircles of the triangles. This algorithm produces a uniform mesh, whose minimum angle can be at most . My algorithm generates both truly Delaunay and constrained Delaunay size-optimal meshes. In my algorithm, I split the line segments of the input PSLG such that their lengths are asymptotically proportional to the local feature size (LFS) by solving ordinary differential equations (ODEs) that map points from a closed 1D interval to points on the input line segments in the PSLG. I then refine the Delaunay triangulation (truly or…
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Taxonomy
TopicsComputational Geometry and Mesh Generation · Remote Sensing and LiDAR Applications · Robotic Path Planning Algorithms
