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https://github.com/mitsuba-renderer/mitsuba2

Mitsuba 2: A Retargetable Forward and Inverse Renderer
https://github.com/mitsuba-renderer/mitsuba2

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Mitsuba 2: A Retargetable Forward and Inverse Renderer

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README 

        
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# Mitsuba Renderer 2



| Documentation   |

|      :---:      |

| [![docs][1]][2] |



[1]: https://readthedocs.org/projects/mitsuba2/badge/?version=latest

[2]: https://mitsuba2.readthedocs.io/en/latest/src/getting_started/intro.html



## This repository is deprecated



*NOTE*: [Mitsuba 3](https://github.com/mitsuba-renderer/mitsuba3) has recently

been released, which addresses many long-standing limitations of Mitsuba 2.

This repository is therefore deprecated: it will not receive updates or

bugfixes, and we recommend that you migrate to Mitsuba 3.



## Introduction



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](https://github.com/mitsuba-renderer/mitsuba).

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](https://github.com/mitsuba-renderer/mitsuba).



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](https://github.com/pybind/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](https://github.com/mitsuba-renderer/nanogui) library. *Note

   that this is not yet complete.*



## Compiling and using Mitsuba 2



Please see the [documentation](http://mitsuba2.readthedocs.org/en/latest) for

details on how to compile, use, and extend Mitsuba 2.



## About



This project was created by [Wenzel Jakob](http://rgl.epfl.ch/people/wjakob).

Significant features and/or improvements to the code were contributed by

[Merlin Nimier-David](https://merlin.nimierdavid.fr/),

[Guillaume Loubet](https://maverick.inria.fr/Membres/Guillaume.Loubet/),

[Benoît Ruiz](https://github.com/4str0m),

[Sébastien Speierer](https://github.com/Speierers),

[Delio Vicini](https://dvicini.github.io/),

and [Tizian Zeltner](https://tizianzeltner.com/).