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LGP-MVS: combined local and global planar priors guidance for indoor multi-view stereo

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Abstract

Multi-view stereo (MVS) has long been a subject for researchers in the computer vision field. Due to the unreliable photometric consistency in low-textured areas, the existing PatchMatch MVS methods have low accuracy and completeness when recovering the depth information of low-textured areas in indoor environments. To solve the above problem that PatchMatch methods always fail in the textureless regions, we propose a global-local planar priors jointly optimized PatchMatch MVS method. The algorithm constructs global-local planar priors and uses a dynamic texture-related multi-view aggregation cost to balance photometric consistency and planar priors. The validity of the algorithm is verified by quantitative and qualitative analysis of depth maps and 3D reconstruction on multiple real-world scenes from ScanNet Dataset and ETH3D benchmark, also synthetic indoor scenes from ICL-NUIM Dataset. Our method can effectively recover the depth information in the textureless regions, so as to obtain the 3D model with high precision.

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Funding

This work was supported by the National Natural Science Foundation of China under Grants 62176096 and 61991412.

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Authors and Affiliations

  1. National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Weihang Kong, Qingshan Xu, Wanjuan Su, Siyuan Xu & Wenbing Tao

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  1. Weihang Kong
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  2. Qingshan Xu
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  3. Wanjuan Su
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  4. Siyuan Xu
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  5. Wenbing Tao
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Correspondence to Wenbing Tao.

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Kong, W., Xu, Q., Su, W. et al. LGP-MVS: combined local and global planar priors guidance for indoor multi-view stereo. Vis Comput 39, 6421–6433 (2023). https://doi.org/10.1007/s00371-022-02737-2

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