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Research Article | Open Access

Deep panoramic depth prediction and completion for indoor scenes

Giovanni Pintore1,*( ), Eva Almansa1,*( ), Armando Sanchez2, Giorgio Vassena2,3, Enrico Gobbetti1( )
Visual and Data-intensive Computing, CRS4, Cagliari 09134, Italy
Gexcel srl, Elmas (CA) 09097, Italy
Department of Civil, Environment, Architectural Engineering, and Mathematics (DICATAM), Università degli Studi di Brescia (UNIBS), Brescia (BS) 25123, Italy

* Giovanni Pintore and Eva Almansa contributed equally to this work.

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Graphical Abstract

Abstract

We introduce a novel end-to-end deep-learning solution for rapidly estimating a dense spherical depth map of an indoor environment. Our input is a single equirectangular image registered with a sparse depth map, as provided by a variety of common capture setups. Depth is inferred by an efficient and lightweight single-branch network, which employs a dynamic gating system to process together dense visual data and sparse geometric data. We exploit the characteristics of typical man-made environments to efficiently compress multi-resolution features and find short- and long-range relations among scene parts. Furthermore, we introduce a new augmentation strategy to make the model robust to different types of sparsity, including those generated by various structured light sensors and LiDAR setups. The experimental results demonstrate that our method provides interactive performance and outperforms state-of-the-art solutions in computational efficiency, adaptivity to variable depth sparsity patterns, and prediction accuracy for challenging indoor data, even when trained solely on synthetic data without any fine tuning.

Keywords

machine learningimage processing and computer visionvision and scene understanding3D stereo scene analysis

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Computational Visual Media
Volume 10 Issue 5,
October 2024
Pages 903-922
DOI: 10.1007/s41095-023-0358-0
Cite this article:
Pintore G, Almansa E, Sanchez A, et al. Deep panoramic depth prediction and completion for indoor scenes. Computational Visual Media, 2024, 10(5): 903-922. https://doi.org/10.1007/s41095-023-0358-0

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Received: 07 March 2023
Accepted: 03 June 2023
Published: 08 February 2024
© The Author(s) 2024.

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