I present a new low discrepancy quasirandom sequence that offers many substantial improvements over other popular sequences such as the Sobol and Halton sequences.

Continue reading “The Unreasonable Effectiveness of Quasirandom Sequences”

Skip to content # Category: Blue Noise

## The Unreasonable Effectiveness of Quasirandom Sequences

## Maximal Poisson disk sampling:

an improved version of Bridson’s algorithm

## An alternate canonical grid layout with uniform projections

## A new method to construct isotropic blue-noise sample point sets with uniform projections

## Evenly Distributing Points in a Triangle.

Always curious. Always learning.

I present a new low discrepancy quasirandom sequence that offers many substantial improvements over other popular sequences such as the Sobol and Halton sequences.

Continue reading “The Unreasonable Effectiveness of Quasirandom Sequences”

an improved version of Bridson’s algorithm

Bridson’s Algorithm (2007) is a very popular method to produce maximal ‘blue noise’ sample point distributions such that no two points are closer than a specified distance apart. In this brief post we show how a minor modification to this algorithm can make it up to 20x faster and allows it to produce much higher density blue noise sample point distributions.

Continue reading “Maximal Poisson disk sampling:

an improved version of Bridson’s algorithm”

When points are placed in a canonical grid layout, they are well-separated and their projections are uniform. I present a simple canonical grid layout which offers better closest-neighbor characterisics than the two most common contemporary canonical layouts.

Continue reading “An alternate canonical grid layout with uniform projections”

I describe a how a small but critical modification to correlated multi-jittered sampling can significantly improve its blue noise spectral characteristics whilst maintaining its uniform projections. This is an exact and direct grid-based construction method that guarantees a minimum neighbor point separation of at least $0.707/n$ and has an average point separation of $0.965/n$*.*

Most two dimensional quasirandom methods focus on sampling over a unit square. However, sampling evenly over the triangle is also very important in computer graphics. Therefore, I describe a simple and direct construction method for a point sequence to evenly cover an arbitrary shaped triangle.

Continue reading “Evenly Distributing Points in a Triangle.”