Novel-View Acoustic Synthesis from 3D Reconstructed Rooms

[Paper] [Docs] [Demo docs] [Video1] [Video2]

Click on the thumbnail image below to watch a video showcasing our Novel-View Acoustic Synthesis.

🎧 For the optimal experience, using a headset is recommended.

Welcome to the official code repository for "Novel-View Acoustic Synthesis from 3D Reconstructed Rooms". This project estimates the sound anywhere in a scene containing multiple unknown sound sources, hence resulting in novel-view acoustic synthesis, given audio recordings from multiple microphones and the 3D geometry and material of a scene.

"Novel-View Acoustic Synthesis from 3D Reconstructed Rooms"
Byeongjoo Ahn, Karren Yang, Brian Hamilton, Jonathan Sheaffer, Anurag Ranjan, Miguel Sarabia, Oncel Tuzel, Jen-Hao Rick Chang

Directory Structure

.
├── demo/                  # Quickstart and demo
│   ├── ...                
├── nvas3d/                # Implementation of our model
│   ├── ...                
└── soundspaces_nvas3d/    # SoundSpaces integration for NVAS3D
    ├── ...                

Installation: SoundSpaces

Follow our Step-by-Step Installation Guide for rendering room impulse responses (RIRs) and images in Matterport3D rooms using SoundSpaces.

Quickstart: Demo

Refer to the Demo Guide for instructions on data generation, dry sound estimation using our model, and novel-view acoustic rendering.

Download the Pretrained Model

Download our pretrained model and place it in the nvas3d/assets/saved_models/default/checkpoints/ directory.

Launch the Demo

To get started with the full pipeline quickly:

bash demo/run_demo.sh

Training

After Training Data Generation, start the training process with:

python main.py --config ./nvas3d/config/default_config.yaml --exp default_exp

Acknowledgements

We thank Dirk Schroeder and David Romblom for insightful discussions and feedback, Changan Chen for the assistance with SoundSpaces.