3D audio-visual indoor scene reconstruction and semantics completion for virtual reality from a single 360◦ RGB-D image

Abstract

We introduce a new approach for constructing immersive virtual spaces by generating comprehensive 3D voxelised models that encompass both geometric and semantic scene representations from a single 360° RGB-D input. The proposed approach utilises a deep convolutional neural network for semantic scene completion (SSC), allowing the estimation of complete semantics and geometries of the scene. We design MDBNet a dual head model that simultaneously processes RGB and depth data using a perspective camera. Depth information is encoded using a flipped transcribed signed distance function (F-TSDF), capturing essential geometric shape characteristics. We extend the inference capabilities of MDBNet on RGB-D input of the perspective camera to accommodate 360° RGB-D by proposing MDBNet360. We employ RGB spherical-to-cubic projection and 3D rotation for depth point clouds, allowing for virtual reality (VR) space design with comprehensive spatial coverage. To our knowledge, this is the first work to extend a pre-trained SSC model, originally using perspective camera RGB-D input, to infer a 3D model from 360º RGB-D input. To assess acoustic properties, we measure parameters such as early decay time (EDT) and reverberation time (RT60) using the exponential sine sweep method (ESS). We used Unity with the Steam Audio plug-in for conducting simulations in virtual space. The proposed framework demonstrates better virtual space reconstruction and immersive sound generation, advancing semantically rich and spatially accurate virtual environments compared to the state-of-the-art (SOTA). Code and rendered sounds are available on GitHub: https://github.com/MonaIA1/Repo360.

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Identifiers

ORCID for Mona Alawadh: orcid.org/0000-0001-5354-7681

ORCID for Mahesan Niranjan: orcid.org/0000-0001-7021-140X

ORCID for Hansung Kim: orcid.org/0000-0003-4907-0491

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