  Neural surfaces learning has shown impressive performance in multi-view
surface reconstruction. However, most existing methods use large multilayer
perceptrons (MLPs) to train their models from scratch, resulting in hours of
training for a single scene. Recently, how to accelerate the neural surfaces
learning has received a lot of attention and remains an open problem. In this
work, we propose a prior-based residual learning paradigm for fast multi-view
neural surface reconstruction. This paradigm consists of two optimization
stages. In the first stage, we propose to leverage generalization models to
generate a basis signed distance function (SDF) field. This initial field can
be quickly obtained by fusing multiple local SDF fields produced by
generalization models. This provides a coarse global geometry prior. Based on
this prior, in the second stage, a fast residual learning strategy based on
hash-encoding networks is proposed to encode an offset SDF field for the basis
SDF field. Moreover, we introduce a prior-guided sampling scheme to help the
residual learning stage converge better, and thus recover finer structures.
With our designed paradigm, experimental results show that our method only
takes about 3 minutes to reconstruct the surface of a single scene, while
achieving competitive surface quality. Our code will be released upon
publication.
