A list of path tracing shaders

I have gathered a list of path tracing shaders on ShaderToy.

Path tracing is a surprisingly simple technique to render realistic images. This would be my definition if you are unfamiliar with the term. But if you already have experience with various ray tracing techniques, I would probably say that path tracing is a remarkably elegant solution to the rendering equation. You can implement a toy path tracer in a weekend or, if you’ve already done it a few times before, within 25 minutes.

Recently I was documenting myself on path tracing, and some of the techniques that can be used, like next event estimation, bidirectional path tracing, Russian roulette, etc. This is a case where ShaderToy can be an invaluable source of examples and information, and so I was browsing path tracing shaders there. As the number of open tabs was starting to get impractical, I decided to use the “playlist” feature of ShaderToy to bookmark them all.

You can find the list here: Path tracing, on ShaderToy.

The examples of path tracers listed include very naive implementations, hacky ones, rendering features like advanced BRDF, volumetric lighting or spectral rendering, or various noise reduction techniques such as next event estimation, bidirectional path tracing, multiple importance sampling, accumulation over frames with temporal reprojection, screen space blue noise, or convolutional neural network based denoising.

Some of those shaders are meant to be artworks, but even the technical experimentation ones look nice, because the global illumination inherent to path tracing tends to generate images that are pretty.

Screenshot of the list on ShaderToy, with various kinds of path tracers visible.

Various links on ray tracing

Here are some links related to ray tracing, and more specifically, path tracing.

Some ray tracing related projects or blogs:

Some major publications:

  • The rendering equation, SIGGRAPH 1986, James T. Kajiya. From the paper:

    We present an integral equation which generalizes a variety of known rendering algorithms.
    We mention that the idea behind the rendering equation is hardly new.
    However, the form in which we present this equation is well suited for computer graphics, and we believe that this form has not appeared before.

  • Bi-directional path tracing, Compugraphics 1993, Eric P. Lafortune and Yves D. Willems. From the paper:

    The basic idea is that particles are shot at the same time from a selected light source and from the viewing point, in much the same way. All hit points on respective particle paths are then connected using shadow rays and the appropriate contributions are added to the flux of pixel  in question.

  • Optimally Combining Sampling Techniques for Monte Carlo Rendering, SIGGRAPH 1995, Eric Veach and Leonidas J. Guibas. From the abstract:

    We present a powerful alternative for constructing robust Monte Carlo estimators, by combining samples from several distributions in a way that is provably good.

  • Metropolis Light Transport, SIGGRAPH 1997, Eric Veach and Leonidas J. Guibas. From the abstract:

    To render an image, we generate a sequence of light transport paths by randomly mutating a single current path (e.g. adding a new vertex to the path).

  • Robust Monte Carlo methods for light transport simulation, 1998, Erich Veach PhD thesis (432 pages pdf): it presents bidirectional path tracing, and introduces Metropolis Light Transport and Multiple Importance Sampling. From the abstract:

    Our statistical contributions include a new technique called multiple importance sampling, which can greatly increase the robustness of Monte Carlo integration. It uses more than one sampling technique to evaluate an integral, and then combines these samples in a way that is provably close to optimal. This leads to estimators that have low variance for a broad class of integrands. We also describe a new variance reduction technique called efficiency-optimized Russian roulette.


    The second algorithm we describe is Metropolis light transport, inspired by the Metropolis sampling method from computational physics. Paths are generated by following a random walk through path space, such that the probability density of visiting each path is proportional to the contribution it makes to the ideal image.


On a slightly different topic, fxguide had a great series of articles on the state of rendering in the film industry, which I previously mentioned.

The rendering tools in the film industry

Here is a list of articles published by fxguide, giving fascinating insights into the tools used by the film industry in terms of rendering.

  • Ben Snow: the evolution of ILM’s lighting tools (January 2011)
    A presentation of the evolution of the technology and tools used at Industrial Light and Magic, over the course of the years and movies, from the mid-90s to nowadays.
  • Monsters University: rendering physically based monsters (June 2013)
  • The Art of Rendering (April 2012)
    A description of the different techniques used in high end rendering and the major engines.
  • The State of Rendering (July 2013): part 1, part 2
    A lengthy overview of the state of the art in high end rendering, comparing the different tools and rendering solutions available, their approach and design choices, strengths and weaknesses as well as the consequences in terms of quality, scalability and render time.

(Brace yourselves for the massive tag list hereafter.)