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European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 pp 17–36Cite as

SoundSpaces: Audio-Visual Navigation in 3D Environments

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12351))

Abstract

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. By both seeing and hearing, the agent must learn to navigate to a sounding object. We propose a multi-modal deep reinforcement learning approach to train navigation policies end-to-end from a stream of egocentric audio-visual observations, allowing the agent to (1) discover elements of the geometry of the physical space indicated by the reverberating audio and (2) detect and follow sound-emitting targets. We further introduce 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. Our results show that audio greatly benefits embodied visual navigation in 3D spaces, and our work lays groundwork for new research in embodied AI with audio-visual perception. Project: http://vision.cs.utexas.edu/projects/audio_visual_navigation.

C. Chen and U. Jain—Contributed equally.

U. Jain—Work done as an intern at Facebook AI Research.

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Notes

  1. 1.

    While algorithms could also run with ambisonic inputs, using binaural sound has the advantage of allowing human listeners to interpret our video results (see Supp video).

  2. 2.

    Replica has more multi-room trajectories, where audio gives clear cues of room entrances/exits (vs. open floor plans in Matterport). This may be why AG is better than PG and APG on Replica.

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Acknowledgements

UT Austin is supported in part by DARPA Lifelong Learning Machines. We thank Alexander Schwing, Dhruv Batra, Erik Wijmans, Oleksandr Maksymets, Ruohan Gao, and Svetlana Lazebnik for valuable discussions and support with the AI-Habitat platform.

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Authors and Affiliations

  1. UT Austin, Austin, USA

    Changan Chen, Ziad Al-Halah & Kristen Grauman

  2. UIUC, Champaign, USA

    Unnat Jain

  3. Facebook Reality Labs, Pittsburgh, USA

    Carl Schissler, Sebastia Vicenc Amengual Gari, Vamsi Krishna Ithapu & Philip Robinson

  4. Facebook AI Research, Pittsburgh, USA

    Changan Chen, Unnat Jain & Kristen Grauman

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    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Chen, C. et al. (2020). SoundSpaces: Audio-Visual Navigation in 3D Environments. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_2

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