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。

SparseNeuS: 基於稀疏視角的快速通用神經表面重建