Tag: Blue Noise

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Maximal Poisson disk sampling:
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.

Figure 1. Poisson disc sampling based on a modified version of Bridson’s algorithm. This modified algorithm runs in linear time and is up to 20x faster than the original algorithm

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an improved version of Bridson’s algorithm”

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An alternate canonical grid layout with uniform projections


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.

Figure 1. A canonical grid layout whose projections are uniform and its closest neighbor distance characteristics are optimal.

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A new method to construct isotropic blue-noise sample point sets 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 $0.965/n$.

Figure 1. Examples of point sets with $n^2$ points for $n=8,12,16,24,32,40$.
The minimum nearest neighbor distance is guaranteed to exceed $0.707/n$ and the average distance is $0.965/n$. Their blue noise sample point distributions are isotropic and their 1-D projections are exactly uniformly distributed. Futhermore each set can be directly and exactly constructed through just two permutations of the $n$-dimensional vector $\{1,2,3,…,n\}$. This therefore, presents a new way of directly constructing tileable isotropic blue noise without the need for iterative methods such as best candidate or simulated annealing

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A simple method to construct isotropic quasirandom blue noise point sequences


I describe a simple method for constructing a sequence of points, that is based on a low discrepancy quasirandom sequence but exhibits enhanced isotropic blue noise properties. This results in fast convergence rates with minimal aliasing artifacts.

Figure 1. The first 100, 200, 500, 1000, 2000 and 5000 sample points of the proposed point sequence (eqn 11) that is progressive, non-stochastic, exhibits near isotropic blue noise characteristics with fast QMC convergence rates with reduced artifacts. It is based on a new simple low discrepancy quasirandom sequence, $R_2$.

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Evenly Distributing Points in a Triangle.

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. 

Figure 1. A new direct method for constructing an open (infinite) low discrepancy quasirandom sequence over an triangle of arbitrary shape and size. Shown are the point distributions for twelve random triangles for the first 150 points.

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