The recent neural surface reconstruction by volume rendering approaches have made much progress by achieving impressive surface reconstruction quality, but are still limited to dense and highly accurate posed views. To overcome such drawbacks, this paper pays special attention on the consistent surface reconstruction from sparse views with noisy camera poses. Unlike previous approaches, the key difference of this paper is to exploit the multi-view constraints directly from the explicit geometry of the neural surface, which can be used as effective regularization to jointly learn the neural surface and refine the camera poses. To build effective multi-view constraints, we introduce a fast differentiable on-surface intersection to generate on-surface points, and propose view-consistent losses based on such differentiable points to regularize the neural surface learning. Based on this point, we propose a jointly learning strategy for neural surface and camera poses, named SC-NeuS, to perform geometry-consistent surface reconstruction in an end-to-end manner. With extensive evaluation on public datasets, our SC-NeuS can achieve consistently better surface reconstruction results with fine-grained details than previous state-of-the-art neural surface reconstruction approaches, especially from sparse and noisy camera views.

翻译：近年来基于体积渲染的神经表面重建方法通过实现令人印象深刻的表面重建质量取得了显著进展，但依然受限于密集且高精度的位姿观测。为克服上述缺陷，本文特别关注从稀疏视角和含噪相机位姿中进行一致性表面重建。与以往方法不同，本文的关键区别在于直接利用神经表面显式几何结构构建多视角约束，这种约束可作为有效正则化手段，联合学习神经表面并优化相机位姿。为建立有效的多视角约束，我们提出一种快速的表面可微求交方法生成表面点，并基于此类可微点设计视图一致性损失以正则化神经表面学习。基于此，我们提出名为SC-NeuS的神经表面与相机位姿联合学习策略，以端到端方式实现几何一致性表面重建。在公开数据集上的大量评估表明，我们的SC-NeuS能够持续获得优于现有最先进神经表面重建方法的表面重建结果，尤其在稀疏含噪相机视角下能重建出更精细的几何细节。