UFORecon: Generalizable Sparse-View Surface Reconstruction from Arbitrary and Unfavorable Sets

Abstract

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 rel-evant views using predefined scores for training and testing phases. This constraint makes the model impractical because we cannot always ensure the availability of favorable combinations in real-world scenarios. We observe that previous methods output degenerate solutions under arbi-trary and unfavorable sets. Building upon this finding, we propose UFORecon, a robust view-combination general-izable surface reconstruction framework. To this end, we apply cross-view matching transformers to model interactions between source images and build correlation frustums to capture global correlations. In addition, we explicitly encode pairwise feature similarities as view-consistent pri-ors. Our proposed framework largely outperforms previous methods not only in view-combination generalizability but also in the existing generalizable protocol trained with favorable view-combinations. The code is available at https://github.com/Youngju-NaIUFORecon.