  Recovery of an underlying scene geometry from multiview images stands as a
long-time challenge in computer vision research. The recent promise leverages
neural implicit surface learning and differentiable volume rendering, and
achieves both the recovery of scene geometry and synthesis of novel views,
where deep priors of neural models are used as an inductive smoothness bias.
While promising for object-level surfaces, these methods suffer when coping
with complex scene surfaces. In the meanwhile, traditional multi-view stereo
can recover the geometry of scenes with rich textures, by globally optimizing
the local, pixel-wise correspondences across multiple views. We are thus
motivated to make use of the complementary benefits from the two strategies,
and propose a method termed Helix-shaped neural implicit Surface learning or
HelixSurf; HelixSurf uses the intermediate prediction from one strategy as the
guidance to regularize the learning of the other one, and conducts such
intertwined regularization iteratively during the learning process. We also
propose an efficient scheme for differentiable volume rendering in HelixSurf.
Experiments on surface reconstruction of indoor scenes show that our method
compares favorably with existing methods and is orders of magnitude faster,
even when some of existing methods are assisted with auxiliary training data.
The source code is available at https://github.com/Gorilla-Lab-SCUT/HelixSurf.
