This paper addresses the challenge of reconstructing surfaces from sparse
view inputs, where ambiguity and occlusions due to missing information pose
significant hurdles. We present a novel approach, named EpiS, that incorporates
Epipolar information into the reconstruction process. Existing methods in
sparse-view neural surface learning have mainly focused on mean and variance
considerations using cost volumes for feature extraction. In contrast, our
method aggregates coarse information from the cost volume into Epipolar
features extracted from multiple source views, enabling the generation of
fine-grained Signal Distance Function (SDF)-aware features. Additionally, we
employ an attention mechanism along the line dimension to facilitate feature
fusion based on the SDF feature. Furthermore, to address the information gaps
in sparse conditions, we integrate depth information from monocular depth
estimation using global and local regularization techniques. The global
regularization utilizes a triplet loss function, while the local regularization
employs a derivative loss function. Extensive experiments demonstrate that our
approach outperforms state-of-the-art methods, especially in cases with sparse
and generalizable conditions.

稀疏视图输入中重建表面的挑战一直存在着，本文提出了一种名为 EpiS 的新方法，它将对极线信息纳入重建过程，聚合了来自多个源视图的线级信息，生成精细的信号距离函数 (SDF) 感知特征，并通过注意力机制实现了特征融合。此外，本文还采用全局和局部正则化技术，整合了从单目深度估计中得到的深度信息，用于解决稀疏条件下的信息缺失问题。广泛的实验证明，我们的方法在稀疏和可推广条件下优于现有的方法。