## Aether: An Embedded Domain Specific Sampling Language for Monte Carlo Rendering

Luke Anderson, MIT CSAIL

Tzu-Mao Li, MIT CSAIL

Jaakko Lehtinen, Aalto University and NVIDIA

Frédo Durand, MIT CSAIL and Inria, Université Côte dâ€™Azur

## Abstract

Implementing Monte Carlo integration requires
significant domain expertise. While simple samplers, such as unidirectional path
tracing, are relatively forgiving, more complex algorithms, such
as bidirectional path tracing or Metropolis methods, are notoriously difficult to
implement correctly.
We propose Aether, an embedded domain specific language for Monte Carlo integration, which offers primitives for writing
concise and correct-by-construction sampling and probability code. The user is tasked with writing sampling code, while our compiler
automatically generates the code necessary for evaluating PDFs as well as the book keeping and combination of multiple sampling strategies.
Our language focuses on ease of implementation for rapid exploration, at the cost of run time performance.
We demonstrate the effectiveness of the language by implementing several
challenging rendering algorithms as well as
a new algorithm, which would
otherwise be prohibitively difficult.

## Publication

Aether: An Embedded Domain Specific Sampling Language for Monte Carlo
Rendering

Luke Anderson, Tzu-Mao Li, Jaakko Lehtinen, Frédo Durand

*ACM Transactions on Graphics 36(4) (In Proceedings of SIGGRAPH
2017)*
## Files

SIGGRAPH 2017 Paper (9MB)

## Errata

Equation 5 should read:

`p(\omega) = \sqrt{| \det J^\text{T} J |}^{\color{red}{-1}} p(u_1,u_2)`

instead of:

`p(\omega) = \sqrt{| \det J^\text{T} J |} p(u_1,u_2)`

## Acknowledgments

Jonathan Ragan-Kelley provided valuable feedback and helped scope the
project from its inception. Matt Pharr and Wenzel Jakob gave us useful
insights and encouragement. The door scene was modeled by Miika Aittala,
Samuli Laine, and Jaakko Lehtinen. The Sponza scene was modeled by Marko
Dabrovic. The Cornell box scene with glass lamps was modeled by Toshiya
Hachisuka. This work was partially funded by DARPA REVEAL and Toyota.