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MVSTER: Epipolar Transformer for Efficient Multi-view Stereo

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13691))

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Abstract

Learning-based Multi-View Stereo (MVS) methods warp source images into the reference camera frustum to form 3D volumes, which are fused as a cost volume to be regularized by subsequent networks. The fusing step plays a vital role in bridging 2D semantics and 3D spatial associations. However, previous methods utilize extra networks to learn 2D information as fusing cues, underusing 3D spatial correlations and bringing additional computation costs. Therefore, we present MVSTER, which leverages the proposed epipolar Transformer to learn both 2D semantics and 3D spatial associations efficiently. Specifically, the epipolar Transformer utilizes a detachable monocular depth estimator to enhance 2D semantics and uses cross-attention to construct data-dependent 3D associations along epipolar line. Additionally, MVSTER is built in a cascade structure, where entropy-regularized optimal transport is leveraged to propagate finer depth estimations in each stage. Extensive experiments show MVSTER achieves state-of-the-art reconstruction performance with significantly higher efficiency: Compared with MVSNet and CasMVSNet, our MVSTER achieves 34% and 14% relative improvements on the DTU benchmark, with 80% and 51% relative reductions in running time. MVSTER also ranks first on Tanks &Temples-Advanced among all published works. Code is available at https://github.com/JeffWang987/MVSTER.

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Notes

  1. 1.

    Three hypothesized depth number: (\(i\)) D : 192 used by one-stage methods [57, 65, 66], (\(ii\)) D : 48, 32, 8 used by three-stage methods [14, 23], and (\(iii\)) D : 8, 8, 4, 4 used by MVSTER. All of these conditions follow implementation details described in Sect. 4.2.

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Acknowledgments

This project was supported by the National Natural Science Foundation of China (No. 62073317).

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Xiaofeng Wang, Fangbo Qin & Xingang Wang

  2. School of Artificial Intelligence, University of Chinese Academy of Science, Beijing, China

    Xiaofeng Wang

  3. PhiGent Robotics, Beijing, China

    Zheng Zhu, Guan Huang, Yun Ye & Xu Chi

  4. Kwai Inc., Beijing, China

    Yijia He

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  1. Xiaofeng Wang
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  2. Zheng Zhu
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Correspondence to Xingang Wang .

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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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Wang, X. et al. (2022). MVSTER: Epipolar Transformer for Efficient Multi-view Stereo. 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 13691. Springer, Cham. https://doi.org/10.1007/978-3-031-19821-2_33

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