We introduce SparseNeuS, a novel neural rendering based method for the task
of surface reconstruction from multi-view images. This task becomes more
difficult when only sparse images are provided as input, a scenario where
existing neural reconstruction approaches usually produce incomplete or
distorted results. Moreover, their inability of generalizing to unseen new
scenes impedes their application in practice. Contrarily, SparseNeuS can
generalize to new scenes and work well with sparse images (as few as 2 or 3).
SparseNeuS adopts signed distance function (SDF) as the surface representation,
and learns generalizable priors from image features by introducing geometry
encoding volumes for generic surface prediction. Moreover, several strategies
are introduced to effectively leverage sparse views for high-quality
reconstruction, including 1) a multi-level geometry reasoning framework to
recover the surfaces in a coarse-to-fine manner; 2) a multi-scale color
blending scheme for more reliable color prediction; 3) a consistency-aware
fine-tuning scheme to control the inconsistent regions caused by occlusion and
noise. Extensive experiments demonstrate that our approach not only outperforms
the state-of-the-art methods, but also exhibits good efficiency,
generalizability, and flexibility.

SparseNeuS 是一种基于神经渲染的表面重建方法，采用有符号距离函数（SDF）作为表面表示，通过几何编码体引入图像特征的通用先验知识进行预测，在利用稀疏视图进行高质量重建时采用了多级几何推理框架、多尺度颜色融合方案和一致性感知微调。实验证明了该方法在速度、泛化性能和灵活性方面均表现出优越性。