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Semantically Derived Geometric Constraints for MVS Reconstruction of Textureless Areas
2021
Remote Sensing
Conventional multi-view stereo (MVS) approaches based on photo-consistency measures are generally robust, yet often fail in calculating valid depth pixel estimates in low textured areas of the scene. In this study, a novel approach is proposed to tackle this challenge by leveraging semantic priors into a PatchMatch-based MVS in order to increase confidence and support depth and normal map estimation. Semantic class labels on image pixels are used to impose class-specific geometric constraints
doi:10.3390/rs13061053
fatcat:bz6w3femwbgedma2fzpj54346a