110 lines
3.9 KiB
C
110 lines
3.9 KiB
C
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/*
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This file is part of Nori, a simple educational ray tracer
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Copyright (c) 2015 by Wenzel Jakob
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Nori is free software; you can redistribute it and/or modify
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it under the terms of the GNU General Public License Version 3
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as published by the Free Software Foundation.
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Nori is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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#pragma once
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#include <nori/object.h>
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#include <memory>
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NORI_NAMESPACE_BEGIN
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class ImageBlock;
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/**
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* \brief Abstract sample generator
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*
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* A sample generator is responsible for generating the random number stream
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* that will be passed an \ref Integrator implementation as it computes the
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* radiance incident along a specified ray.
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*
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* The most simple conceivable sample generator is just a wrapper around the
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* Mersenne-Twister random number generator and is implemented in
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* <tt>independent.cpp</tt> (it is named this way because it generates
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* statistically independent random numbers).
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*
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* Fancier samplers might use stratification or low-discrepancy sequences
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* (e.g. Halton, Hammersley, or Sobol point sets) for improved convergence.
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* Another use of this class is in producing intentionally correlated
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* random numbers, e.g. as part of a Metropolis-Hastings integration scheme.
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*
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* The general interface between a sampler and a rendering algorithm is as
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* follows: Before beginning to render a pixel, the rendering algorithm calls
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* \ref generate(). The first pixel sample can now be computed, after which
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* \ref advance() needs to be invoked. This repeats until all pixel samples have
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* been exhausted. While computing a pixel sample, the rendering
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* algorithm requests (pseudo-) random numbers using the \ref next1D() and
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* \ref next2D() functions.
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*
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* Conceptually, the right way of thinking of this goes as follows:
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* For each sample in a pixel, a sample generator produces a (hypothetical)
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* point in an infinite dimensional random number hypercube. A rendering
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* algorithm can then request subsequent 1D or 2D components of this point
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* using the \ref next1D() and \ref next2D() functions. Fancy implementations
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* of this class make certain guarantees about the stratification of the
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* first n components with respect to the other points that are sampled
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* within a pixel.
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*/
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class Sampler : public NoriObject {
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public:
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/// Release all memory
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virtual ~Sampler() { }
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/// Create an exact clone of the current instance
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virtual std::unique_ptr<Sampler> clone() const = 0;
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/**
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* \brief Prepare to render a new image block
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*
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* This function is called when the sampler begins rendering
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* a new image block. This can be used to deterministically
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* initialize the sampler so that repeated program runs
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* always create the same image.
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*/
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virtual void prepare(const ImageBlock &block) = 0;
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/**
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* \brief Prepare to generate new samples
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*
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* This function is called initially and every time the
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* integrator starts rendering a new pixel.
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*/
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virtual void generate() = 0;
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/// Advance to the next sample
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virtual void advance() = 0;
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/// Retrieve the next component value from the current sample
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virtual float next1D() = 0;
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/// Retrieve the next two component values from the current sample
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virtual Point2f next2D() = 0;
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/// Return the number of configured pixel samples
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virtual size_t getSampleCount() const { return m_sampleCount; }
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/**
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* \brief Return the type of object (i.e. Mesh/Sampler/etc.)
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* provided by this instance
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* */
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EClassType getClassType() const { return ESampler; }
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protected:
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size_t m_sampleCount;
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};
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NORI_NAMESPACE_END
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