https://github.com/pgrit/var-aware-mis-pbrt

Implementation of the paper "Variance-Aware Multiple Importance Sampling" for bidirectional path tracing in PBRT.
https://github.com/pgrit/var-aware-mis-pbrt

Last synced: 2 months ago
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Implementation of the paper "Variance-Aware Multiple Importance Sampling" for bidirectional path tracing in PBRT.

• Host: GitHub
• URL: https://github.com/pgrit/var-aware-mis-pbrt
• Owner: pgrit
• License: bsd-2-clause
• Created: 2019-01-22T15:19:12.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-08-28T10:28:53.000Z (over 5 years ago)
• Last Synced: 2023-08-02T04:55:23.776Z (over 1 year ago)
• Language: C++
• Homepage:
• Size: 5.55 MB
• Stars: 35
• Watchers: 1
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
Variance-aware MIS weights in PBRTv3

===================================================

This repository implements the approach discussed in the

"Variance-Aware Multiple Importance Sampling" paper.

The core of the implementation is in `src/util/samis.h` and `src/util/samis.cpp`:

The class `SAMISRectifier` implements the rectification as a black box: given the outcome of paths

sampled from the first iteration, it computes the required variance estimates and the resulting factors.

For reference value computations, the files `src/util/varestim.h` and `src/util/varestim.cpp` provide a utility

class to compute accurate estimates of the variance factors given a large number of samples.

The implementation required minor changes in the bidirectional path tracer integrator

(`src/integrators/bdpt.cpp` and `bdpt.h`) to separate the rendering into multiple iterations

and to look-up and multiply with the propper variance factors during the MIS computation.

Furthermore, the weighted combination of the first iteration with the following one also

required a small addition to the `Film` class in `src/core/film.cpp` and `src/core/film.h`.

The defensive sampling application is implemented in a new integrator, see `src/integrators/guideddi.cpp` and

`src/integrators/guideddi.h`. The implementation is analogous to the one from the "Optimal Multiple Importance Sampling"

paper by Kondapaneni et al.

For build instructions, documentation, test scenes, etc., refer to [the original PBRTv3 repository](https://github.com/mmp/pbrt-v3/)

or the repository created by [Benedikt Bitterli](https://benedikt-bitterli.me/resources/).

All results in the paper were generated from (sometimes slightly modified versions of) the scenes from those repositories.

Testing

---------

The folder `test` contains a number of Python scripts to render comparison images with various approaches.

The `test/runtests_bdpt.py` script loads a number of PBRT scene files (specified in the script) and replaces

the integrator and sampler definitions by the appropriate methods, as given in the beginning of the script.

The `test/runtests_di.py` script does the same for the defensive sampling application. For the comparison to the

optimal MIS weights, the path to the source code from that paper needs to be hard-coded in the script (by modifying

the `optimal_mis_executable` variable in the beginning.)