  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.
