Efficient barycentric point sampling on meshes

08/24/2017
by   Jamie Portsmouth, et al.
0

We present an easy-to-implement and efficient analytical inversion algorithm for the unbiased random sampling of a set of points on a triangle mesh whose surface density is specified by barycentric interpolation of non-negative per-vertex weights. The correctness of the inversion algorithm is verified via statistical tests, and we show that it is faster on average than rejection sampling.

READ FULL TEXT
research
10/19/2020

GAMesh: Guided and Augmented Meshing for Deep Point Networks

We present a new meshing algorithm called guided and augmented meshing, ...
research
03/24/2023

Random sampling and unisolvent interpolation by almost everywhere analytic functions

We prove a.s. (almost sure) unisolvency of interpolation by continuous r...
research
05/04/2000

Connectivity Compression for Irregular Quadrilateral Meshes

Applications that require Internet access to remote 3D datasets are ofte...
research
09/22/2021

Differentiable Surface Triangulation

Triangle meshes remain the most popular data representation for surface ...
research
10/04/2019

The Bayesian Inversion Problem for Thermal Average Sampling of Quantum Systems

In this article, we propose a novel method for sampling potential functi...
research
08/03/2023

LEAVEN – Lightweight Surface and Volume Mesh Sampling Application for Particle-based Simulations

We present an easy-to-use and lightweight surface and volume mesh sampli...
research
02/05/2020

Polygon Extraction from Triangular Meshes

This paper presents Polylidar, an efficient algorithm to extract non-con...

Please sign up or login with your details

Forgot password? Click here to reset