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
\textbf{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.

基于给定有限数量的多视角图像，实现可推广的神经隐式表面重建并使用视角组合得分判断输入视角组合的有效性。提出了 UFORecon，一个鲁棒的视角组合可推广表面重建框架，通过交叉视图匹配变换和建立相关视锥体来捕捉全局关联，显式编码成对特征相似性作为视角一致性先验。与先前方法相比，我们的提出的框架在视角组合可推广性和传统的训练有利视角组合基准上均表现出显著优势。