Sur2f: A Hybrid Representation for High-Quality and Efficient Surface
  Reconstruction from Multi-view Images
Zhangjin Huang Zhihao Liang Haojie Zhang Yangkai Lin Kui Jia
Multi-view surface reconstruction is an ill-posed, inverse problem in 3D
vision research. It involves modeling the geometry and appearance with
appropriate surface representations. Most of the existing methods rely either
on explicit meshes, using surface rendering of meshes for reconstruction, or on
implicit field functions, using volume rendering of the fields for
reconstruction. The two types of representations in fact have their respective
merits. In this work, we propose a new hybrid representation, termed Sur2f,
aiming to better benefit from both representations in a complementary manner.
Technically, we learn two parallel streams of an implicit signed distance field
and an explicit surrogate surface Sur2f mesh, and unify volume rendering of the
implicit signed distance function (SDF) and surface rendering of the surrogate
mesh with a shared, neural shader; the unified shading promotes their
convergence to the same, underlying surface. We synchronize learning of the
surrogate mesh by driving its deformation with functions induced from the
implicit SDF. In addition, the synchronized surrogate mesh enables
surface-guided volume sampling, which greatly improves the sampling efficiency
per ray in volume rendering. We conduct thorough experiments showing that
Sur$^2$f outperforms existing reconstruction methods and surface
representations, including hybrid ones, in terms of both recovery quality and
recovery efficiency.