SoundSpaces is a realistic acoustic simulation platform for audio-visual embodied AI research. From audio-visual navigation and audio-visual exploration to echolocation, this platform expands embodied vision research to a broader scope of topics.

Introduction Video

Presentation videos can be found at our project page.

Motivation

Moving around in the world is naturally a multisensory experience, but today's embodied agents are deaf---restricted to solely their visual perception of the environment. We introduce audio-visual navigation for complex, acoustically and visually realistic 3D environments. We further build SoundSpaces: a first-of-its-kind dataset of audio renderings based on geometrical acoustic simulations for two sets of publicly available 3D environments (Matterport3D and Replica), and we instrument Habitat to support the new sensor, making it possible to insert arbitrary sound sources in an array of real-world scanned environments.

Citing SoundSpaces

If you use the Habitat platform in your research, please cite the following paper:

@inproceedings{chen20soundspaces,
  title     =     {SoundSpaces: Audio-Visual Navigaton in 3D Environments,
  author    =     {Changan Chen and Unnat Jain and Carl Schissler and Sebastia Vicenc Amengual Gari and Ziad Al-Halah and Vamsi Krishna Ithapu and Philip Robinson and Kristen Grauman},
  booktitle =     {ECCV},
  year      =     {2020}
}

Installation

1. Install habitat-api and habitat-sim
2. Install this repo into pip

pip install -e .

3. Following instructions on the dataset page to download the rendered audio data and datasets

Usage

This repo supports benchmarking PointGoal, AudioGoal and AudioPointGoal with different sensors on two datasets. Below we show the commands for training and evaluating AudioGoal with Depth sensor on Replica, but it applies to the other two tasks, other sensors and Matterport dataset as well.

1. Training

python av_nav/run.py --exp-config av_nav/config/replica/train_telephone/audiogoal_depth.yaml --model-dir data/models/replica/audiogoal_depth

2. Validation (evaluate each checkpoint and generate a validation curve)

python av_nav/run.py --run-type eval --exp-config av_nav/config/replica/val_telephone/audiogoal_depth.yaml --model-dir data/models/replica/audiogoal_depth

3. Test the best validation checkpoint based on validation curve

python av_nav/run.py --run-type eval --exp-config av_nav/config/replica/test_telephone/audiogoal_depth.yaml --model-dir data/models/replica/audiogoal_depth EVAL_CKPT_PATH_DIR data/models/replica/audiogoal_depth/data/ckpt.XXX.pth

4. Generate demo video with audio

python av_nav/run.py --run-type eval --exp-config av_nav/config/replica/test_telephone/audiogoal_depth.yaml --model-dir data/models/replica/audiogoal_depth EVAL_CKPT_PATH_DIR data/models/replica/audiogoal_depth/data/ckpt.220.pth VIDEO_OPTION [\"disk\"] TASK_CONFIG.SIMULATOR.USE_RENDERED_OBSERVATIONS False TASK_CONFIG.TASK.SENSORS [\"POINTGOAL_WITH_GPS_COMPASS_SENSOR\",\"SPECTROGRAM_SENSOR\",\"AUDIOGOAL_SENSOR\"] SENSORS [\"RGB_SENSOR\",\"DEPTH_SENSOR\"] EXTRA_RGB True TASK_CONFIG.SIMULATOR.CONTINUOUS_VIEW_CHANGE True DISPLAY_RESOLUTION 512 TEST_EPISODE_COUNT 1

5. Interactive demo

python scripts/interactive_demo.py

Contributing

See the CONTRIBUTING file for how to help out.

License

SoundSpaces is CC-BY-4.0 licensed, as found in the LICENSE file.

The trained models and the task datasets are considered data derived from the correspondent scene datasets.

• Matterport3D based task datasets and trained models are distributed with [Matterport3D Terms of Usehttp://kaldir.vc.in.tum.de/matterport/MP_TOS.pdf) and under CC BY-NC-SA 3.0 US license.
• Replica based task datasets, the code for generating such datasets, and trained models are under Replica license.