  Neural rendering can be used to reconstruct implicit representations of
shapes without 3D supervision. However, current neural surface reconstruction
methods have difficulty learning high-frequency geometry details, so the
reconstructed shapes are often over-smoothed. We develop HF-NeuS, a novel
method to improve the quality of surface reconstruction in neural rendering. We
follow recent work to model surfaces as signed distance functions (SDFs).
First, we offer a derivation to analyze the relationship between the SDF, the
volume density, the transparency function, and the weighting function used in
the volume rendering equation and propose to model transparency as transformed
SDF. Second, we observe that attempting to jointly encode high-frequency and
low-frequency components in a single SDF leads to unstable optimization. We
propose to decompose the SDF into a base function and a displacement function
with a coarse-to-fine strategy to gradually increase the high-frequency
details. Finally, we design an adaptive optimization strategy that makes the
training process focus on improving those regions near the surface where the
SDFs have artifacts. Our qualitative and quantitative results show that our
method can reconstruct fine-grained surface details and obtain better surface
reconstruction quality than the current state of the art. Code available at
https://github.com/yiqun-wang/HFS.
