Get 3D object shape when poor views using feature similarity learn view interactions with UFORecon
Get 3D object shape when poor views using feature similarity learn view interactions with UFORecon
UFORecon: Generalizable Sparse-View Surface Reconstruction from Arbitrary and UnFavOrable Data Sets
arXiv paper abstract https://arxiv.org/abs/2403.05086
arXiv PDF paper https://arxiv.org/pdf/2403.05086.pdf
Generalizable neural implicit surface reconstruction aims to obtain an accurate underlying geometry given a limited number of multi-view images from unseen scenes.
However, existing methods select only informative and relevant views using predefined scores for training and testing phases.
... introduce and validate a view-combination score to indicate the effectiveness of the input view combination.
... propose UFORecon, a robust view-combination generalizable surface reconstruction framework.
... apply cross-view matching transformers to model interactions between source images and build correlation frustums to capture global correlations.
... framework .. outperforms previous methods in terms of view-combination generalizability and ... conventional generalizable protocol trained with favorable view-combinations ...
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