Acoustic Reliefs

We present a framework to optimize and generate Acoustic Reliefs : acoustic diffusers that not only perform well acoustically in scattering sound uniformly in all directions, but are also visually interesting and can approximate user-provided images. To this end, we develop a differentiable acoustics simulator based on the boundary element method, and integrate it with a differentiable renderer coupled with a vision model to jointly optimize for acoustics, appearance, and fabrication constraints at the same time. We generate various examples and fabricate two room-scale reliefs. The result is a validated simulation and optimization scheme for generating acoustic reliefs whose appearances can be guided by a provided image.

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Publication Details

Journal
ACM Transactions on Graphics
Published
2025-12-01
DOI
https://doi.org/10.1145/3763287
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
0.00
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article

Acoustic Reliefs

Bernd Bickel, Doug L. James, Michal Piovarči, Jeremy Chew et al.
ACM Transactions on Graphics
Generative Adversarial Networks and Image Synthesis
article

Acoustic Reliefs

Bernd Bickel, Doug L. James, Michal Piovarči, Jeremy Chew, Kangrui Xue
article en

Abstract

We present a framework to optimize and generate Acoustic Reliefs : acoustic diffusers that not only perform well acoustically in scattering sound uniformly in all directions, but are also visually interesting and can approximate user-provided images. To this end, we develop a differentiable acoustics simulator based on the boundary element method, and integrate it with a differentiable renderer coupled with a vision model to jointly optimize for acoustics, appearance, and fabrication constraints at the same time. We generate various examples and fabricate two room-scale reliefs. The result is a validated simulation and optimization scheme for generating acoustic reliefs whose appearances can be guided by a provided image.

ACM Transactions on GraphicsVol. 44(6)
ETH Zurich (CH), Stanford University (US)
Openalex Percentile: Top 60%
Generative Adversarial Networks and Image Synthesis
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