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tizian
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Code for "Specular Manifold Sampling for Rendering High-Frequency Caustics and Glints" (SIGGRAPH 2020) by Tizian Zeltner, Iliyan Georgiev, and Wenzel Jakob

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SMS teaser

Specular Manifold Sampling for Rendering High-Frequency Caustics and Glints

Source code of the paper "Specular Manifold Sampling for Rendering High-Frequency Caustics and Glints" by Tizian Zeltner, Iliyan Georgiev, and Wenzel Jakob from SIGGRAPH 2020.

The implementation is based on the Mitsuba 2 Renderer, see the lower part of the README.

Compilation

The normal compilation instructions for Mitsuba 2 apply. See the "Getting started" sections in the documentation. For this project, only the scalar_{rgb,spectral} variants are tested. The paper shows results generated with scalar_rgb.

Overview

Various versions of the SMS technique are implemented:

Single-scattering caustic SMS

Sampling technique for diffuse-specular-light connections with a single reflection or refraction event.

  • Implemented in
    include/mitsuba/render/manifold_ss.h
    and
    src/librender/manifold_ss.cpp
  • Augmented path tracer in
    src/integrators/path_sms_ss.cpp
  • Reference path tracer (that renders the same subset of light paths for comparisons) in
    src/integrators/path_filtered_ss.cpp
  • Used to render Figures 4, 6, 8, 9, 14, 15, 16, 17 in the paper.

Multi-scattering caustic SMS

Sampling technique for diffuse-specular*-light connections with a fixed number of reflection or refraction events.

  • Implemented in
    include/mitsuba/render/manifold_ms.h
    and
    src/librender/manifold_ms.cpp
  • Augmented path tracer in
    src/integrators/path_sms_ms.cpp
  • Reference path tracer (that renders the same subset of light paths for comparisons) in
    src/integrators/path_filtered_ms.cpp
  • Used to render Figure 18 in the paper.

Glint SMS

Sampling technique for glints from specular (normal-mapped) microstructures.

  • Implemented in
    include/mitsuba/render/manifold_glints.h
    src/librender/manifold_glints.cpp
  • Augmented path tracer in
    src/integrators/path_sms_ms.cpp
  • Used to render Figures 12 and 19 in the paper.

Vectorized Glint SMS

Since the submission, we also implemented a version of the glints that use of SIMD vectorization.

  • Implemented in
    include/mitsuba/render/manifold_glints_vectorized.h
    src/librender/manifold_glints_vectorized.cpp
  • Uses the same integrator as the scalar glints, but with the xml flag
     in the scene description
  • Does not support surface roughness
  • Only supports the biased inverse PDF estimate

Combined caustic and glint integrators

We also combined the previous single/multi-scattering caustics and the glint method into a single integrator that was used for the teaser image.

  • Augmented path tracer in
    src/integrators/path_sms_teaser.cpp
  • Reference path tracer (that renders the same subset of light paths for comparisons) in
    src/integrators/path_filtered_teaser.cpp
  • Used to render Figures 1 and 13 in the paper.

Results

The directory

results
contains a set of folders for the different figures in the paper, e.g.
results/Figure__
. They contain Python scripts to generate plots or render the included Mitsuba 2 scenes.
  • All of these scripts need to be run from the respective subfolder to ensure that files are written to existing directories.
  • Most scripts assume that Mitsuba was added to the path either manually or by running
    source setpath.sh
    . See the "Running Mitsuba" section in the documentation.
  • Note that the output for the various equal time comparisons will be more or less converged depending on your hardware, and will likely not match the exact renderings from the paper.

Here is a list of available results:

results/Figure_4_5_RingSolutions/

  • Run
    mitsuba ring.xml
    to render Figure 4a.
  • Run Python script
    generate_plots_simple.py
    to create the two plots in Figure b,d.
  • Run
    mitsuba ring_paths.xml
    to render Figure 4c.
  • Run Python script
    generate_fractal.py
    to create Figure 5a.
  • Run Python script
    generate_plots_normalmapped.py
    to create Figure 5b.

results/Figure_6_Sequence/

  • Run Python script
    render.py
    that renders the scene with Mitsuba after setting the right method parameters.
  • Run Python script
    render_references.py
    to render references with path tracing and SMS. This will take a long time!

results/Figure_8_Constraints/

  • Run Python script
    render.py
    that renders the scene using the two approaches.

results/Figure_9_Twostage/

  • Run Python script
    render.py
    that renders the two scenes with both approaches.

results/Figure_10_TwostageSolutions/

  • Run Python script
    generate_plots.py
    to create the two subplots.

results/Figure_11_GlintsZoom/

  • Run Python script
    generate_plots.py
    to create plots of the footprint and the convergence basins inside.

results/Figure_12_GlintsMIS/

  • Run Python script
    render.py
    that renders the three images with Mitsuba after setting the right method parameters.

results/Figure_14_15_MainComparison/

  • Run Python scripts
    render_{plane,sphere,pool}.py
    to create renderings for all methods at equal time.
  • Run Python scripts
    render_references_{plane,sphere,pool}.py
    to render references with path tracing and SMS. This will take a long time!

results/Figure_16_Displacement/

  • Run Python script
    render.py
    to render both versions of the scene.

results/Figure_17_Roughness/

  • Run Python script
    render.py
    to render the scenes with varying roughness using both approaches.

results/Figure_18_DoubleRefraction/

  • Run Python script
    render.py
    to create renderings for all methods at equal time.
  • Run Python script
    render_references.py
    to render references with path tracing and SMS. This will take a long time!

results/Figure_19_GlintsComparison/

  • Run Python script
    generate_normalmaps.py
    that will create the high-resolution normal maps used in the two scenes.
  • In order to run prior work "Position-Normal Distributions for Efficient Rendering of Specular Microstructure" by Yan et al. 2016, convert the normal maps to the
    .flakes
    format used by their method by running these two commands:
    • .//dist/normalmap_to_flakes textures/normalmap_gaussian_yan.exr gaussian.flakes 4
    • .//dist/normalmap_to_flakes textures/normalmap_brushed_yan.exr brushed.flakes 2
  • Render sequences of renderings with increasing time by running the
    render_{shoes,kettle}.py
    scripts. These run for a long time! Specify the method to use by providing one of the following command line arguments:
    • pt
      for path tracer reference
    • sms_ub
      for unbiased SMS
    • sms_b
      for biased SMS
    • sms_bv
      for biased + vectorized SMS
    • yan
      for the method of Yan et al. 2016
  • Render path traced reference insets with Python script
    render_references.py
    .
  • Process the renderings and log files to generate the convergence plots with Python script
    generate_plots.py
    .

Mitsuba logo

Mitsuba Renderer 2

| Documentation | | :---: | | docs |

Mitsuba 2 is a research-oriented rendering system written in portable C++17. It consists of a small set of core libraries and a wide variety of plugins that implement functionality ranging from materials and light sources to complete rendering algorithms. Mitsuba 2 strives to retain scene compatibility with its predecessor Mitsuba 0.6. However, in most other respects, it is a completely new system following a different set of goals.

The most significant change of Mitsuba 2 is that it is a retargetable renderer: this means that the underlying implementations and data structures are specified in a generic fashion that can be transformed to accomplish a number of different tasks. For example:

  1. In the simplest case, Mitsuba 2 is an ordinary CPU-based RGB renderer that processes one ray at a time similar to its predecessor Mitsuba 0.6.

  2. Alternatively, Mitsuba 2 can be transformed into a differentiable renderer that runs on NVIDIA RTX GPUs. A differentiable rendering algorithm is able to compute derivatives of the entire simulation with respect to input parameters such as camera pose, geometry, BSDFs, textures, and volumes. In conjunction with gradient-based optimization, this opens door to challenging inverse problems including computational material design and scene reconstruction.

  3. Another type of transformation turns Mitsuba 2 into a vectorized CPU renderer that leverages Single Instruction/Multiple Data (SIMD) instruction sets such as AVX512 on modern CPUs to efficiently sample many light paths in parallel.

  4. Yet another type of transformation rewrites physical aspects of the simulation: Mitsuba can be used as a monochromatic renderer, RGB-based renderer, or spectral renderer. Each variant can optionally account for the effects of polarization if desired.

In addition to the above transformations, there are several other noteworthy changes:

  1. Mitsuba 2 provides very fine-grained Python bindings to essentially every function using pybind11. This makes it possible to import the renderer into a Jupyter notebook and develop new algorithms interactively while visualizing their behavior using plots.

  2. The renderer includes a large automated test suite written in Python, and its development relies on several continuous integration servers that compile and test new commits on different operating systems using various compilation settings (e.g. debug/release builds, single/double precision, etc). Manually checking that external contributions don't break existing functionality had become a severe bottleneck in the previous Mitsuba 0.6 codebase, hence the goal of this infrastructure is to avoid such manual checks and streamline interactions with the community (Pull Requests, etc.) in the future.

  3. An all-new cross-platform user interface is currently being developed using the NanoGUI library. Note that this is not yet complete.

Compiling and using Mitsuba 2

Please see the documentation for details on how to compile, use, and extend Mitsuba 2.

About

This project was created by Wenzel Jakob. Significant features and/or improvements to the code were contributed by Merlin Nimier-David, Guillaume Loubet, Sébastien Speierer, Delio Vicini, and Tizian Zeltner.

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