  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. This constraint
renders the model impractical in real-world scenarios, where the availability
of favorable combinations cannot always be ensured. We introduce and validate a
view-combination score to indicate the effectiveness of the input view
combination. We observe that previous methods output degenerate solutions under
arbitrary and unfavorable sets. Building upon this finding, we propose
UFORecon, a robust view-combination generalizable surface reconstruction
framework. To achieve this, we apply cross-view matching transformers to model
interactions between source images and build correlation frustums to capture
global correlations. Additionally, we explicitly encode pairwise feature
similarities as view-consistent priors. Our proposed framework significantly
outperforms previous methods in terms of view-combination generalizability and
also in the conventional generalizable protocol trained with favorable
view-combinations. The code is available at
https://github.com/Youngju-Na/UFORecon.
