  Recent methods for neural surface representation and rendering, for example
NeuS, have demonstrated the remarkably high-quality reconstruction of static
scenes. However, the training of NeuS takes an extremely long time (8 hours),
which makes it almost impossible to apply them to dynamic scenes with thousands
of frames. We propose a fast neural surface reconstruction approach, called
NeuS2, which achieves two orders of magnitude improvement in terms of
acceleration without compromising reconstruction quality. To accelerate the
training process, we parameterize a neural surface representation by
multi-resolution hash encodings and present a novel lightweight calculation of
second-order derivatives tailored to our networks to leverage CUDA parallelism,
achieving a factor two speed up. To further stabilize and expedite training, a
progressive learning strategy is proposed to optimize multi-resolution hash
encodings from coarse to fine. We extend our method for fast training of
dynamic scenes, with a proposed incremental training strategy and a novel
global transformation prediction component, which allow our method to handle
challenging long sequences with large movements and deformations. Our
experiments on various datasets demonstrate that NeuS2 significantly
outperforms the state-of-the-arts in both surface reconstruction accuracy and
training speed for both static and dynamic scenes. The code is available at our
website: https://vcai.mpi-inf.mpg.de/projects/NeuS2/ .
