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European Conference on Computer Vision

ECCV 2022: Computer Vision – ECCV 2022 pp 257–274Cite as

CostDCNet: Cost Volume Based Depth Completion for a Single RGB-D Image

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13662))

Abstract

Successful depth completion from a single RGB-D image requires both extracting plentiful 2D and 3D features and merging these heterogeneous features appropriately. We propose a novel depth completion framework, CostDCNet, based on the cost volume-based depth estimation approach that has been successfully employed for multi-view stereo (MVS). The key to high-quality depth map estimation in the approach is constructing an accurate cost volume. To produce a quality cost volume tailored to single-view depth completion, we present a simple but effective architecture that can fully exploit the 3D information, three options to make an RGB-D feature volume, and per-plane pixel shuffle for efficient volume upsampling. Our CostDCNet framework consists of lightweight deep neural networks (\(\sim \)1.8M parameters), running in real time (\(\sim \)30 ms). Nevertheless, thanks to our simple but effective design, CostDCNet demonstrates depth completion results comparable to or better than the state-of-the-art methods.

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Notes

  1. 1.

    https://github.com/kamse/CostDCNet.

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Acknowledgment

This work was supported by the Ministry of Science and ICT, Korea, through IITP grants (SW Star Lab, 2015-0-00174; AI Innovation Hub, 2021-0-02068; Artificial Intelligence Graduate School Program (POSTECH), 2019-0-01906).

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Authors and Affiliations

  1. POSTECH, Pohang-si, South Korea

    Jaewon Kam, Jungeon Kim, Soongjin Kim, Jaesik Park & Seungyong Lee

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  1. Jaewon Kam
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  2. Jungeon Kim
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  3. Soongjin Kim
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  4. Jaesik Park
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  5. Seungyong Lee
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Correspondence to Seungyong Lee .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Kam, J., Kim, J., Kim, S., Park, J., Lee, S. (2022). CostDCNet: Cost Volume Based Depth Completion for a Single RGB-D Image. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13662. Springer, Cham. https://doi.org/10.1007/978-3-031-20086-1_15

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  • DOI: https://doi.org/10.1007/978-3-031-20086-1_15

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