Low discrepancy (ldsampler)

ldsampler_a.png ldsampler_b.png
(a) A projection of the first 1024 points onto the first two dimensions. (b) A projection of the first 1024 points onto the 32 and 33th dimension, which look almost identical. However, note that the points have been scrambled to reduce correlations between dimensions.

This plugin implements a simple hybrid sampler that combines aspects of a Quasi-Monte Carlo sequence
with a pseudorandom number generator based on a technique proposed by Kollig and Keller
[28]. It is a good and fast general-purpose sample generator and therefore chosen as the default option
in Mitsuba. Some of the QMC samplers in the following pages can generate even better distributed
samples, but this comes at a higher cost in terms of performance.
Roughly, the idea of this sampler is that all of the individual 2D sample dimensions are first filled
using the same (0, 2)-sequence, which is then randomly scrambled and permuted using numbers
generated by a Mersenne Twister pseudorandom number generator [40]. Note that due to internal
storage costs, low discrepancy samples are only provided up to a certain dimension, after which independent
sampling takes over. The name of this plugin stems from the fact that (0, 2) sequences
minimize the so-called star disrepancy, which is a quality criterion on their spatial distribution. By
now, the name has become slightly misleading since there are other samplers in Mitsuba that just as
much try to minimize discrepancy, namely the sobol and halton plugins.
Like the independent sampler,multicore and network renderingswill generally produce different
images in subsequent runs due to the nondeterminism introduced by the operating system scheduler.

Parameter Type Description
sampleCount integer Number of samples per pixel; should be a power of two (e.g.
1, 2, 4, 8, 16, etc.), or it will be rounded up to the next one
(Default: 4)
dimension integer Effective dimension, up to which low discrepancy samples
are provided. The number here is to be interpreted as the
number of subsequent 1D or 2D sample requests that can be
satisfied using “good” samples. Higher high values increase
both storage and computational costs. (Default: 4)

  • 最終更新:2014-07-25 11:01:53