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• W4283159207 abstract "PatchMatch-based multi-view stereo (MVS) algorithms have achieved great success in large-scale scene reconstruction tasks. However, reconstruction of textureless planes often fails, as similarity measurement methods may become ineffective in these regions. Thus, a potential depth hypothesis inference strategy is proposed to make the reconstruction result more complete. The strategy consists of two steps. First, for pixels that are not successfully reconstructed, multiple potential depth hypotheses are generated using neighboring reconstructed accurate depth values. Second, depth hypotheses are selected using the Markov random field (MRF). The strategy can significantly increase the reconstruction completeness. In addition, a new acceleration scheme similar to dilated convolution is proposed to speed up the depth map estimation process when using larger matching windows. We integrated the above ideas into a new MVS pipeline, potential hypothesis inference multi-view stereo (PHI-MVS). The result of PHI-MVS is validated on ETH3D and BlendedMVS benchmarks, and it demonstrates competing performance against the state-of-the-art." @default.
• W4283159207 created "2022-06-21" @default.
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• W4283159207 date "2022-08-01" @default.
• W4283159207 modified "2023-10-06" @default.
• W4283159207 title "Multi-view stereo for large-scale scene reconstruction with MRF-based depth inference" @default.
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• W4283159207 doi "https://doi.org/10.1016/j.cag.2022.06.009" @default.
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