Recent methods for neural surface representation and rendering, for example
NeuS, have demonstrated 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 integrate multi-resolution hash encodings into a neural
surface representation and implement our whole algorithm in CUDA. We also
present a lightweight calculation of second-order derivatives tailored to our
networks (i.e., ReLU-based MLPs), which achieves a factor two speed up. To
further stabilize training, a progressive learning strategy is proposed to
optimize multi-resolution hash encodings from coarse to fine. In addition, we
extend our method for reconstructing dynamic scenes with an incremental
training strategy. Our experiments on various datasets demonstrate that NeuS2
significantly outperforms the state-of-the-arts in both surface reconstruction
accuracy and training speed. The video is available at
this https URL .

提出了一种快速的神经表面重建方法，称为 NeuS2，通过将多分辨率哈希编码集成到神经表面表示中，实现了两个数量级的加速，并通过 CUDA 实现了整个算法。该方法不仅提高了表面重建的准确性，还能有效地解决神经表面表示的训练时间长的问题，特别是在动态场景下。