PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces
Yiqun Wang Ivan Skorokhodov Peter Wonka
A signed distance function (SDF) parametrized by an MLP is a common
ingredient of neural surface reconstruction. We build on the successful recent
method NeuS to extend it by three new components. The first component is to
borrow the tri-plane representation from EG3D and represent signed distance
fields as a mixture of tri-planes and MLPs instead of representing it with MLPs
only. Using tri-planes leads to a more expressive data structure but will also
introduce noise in the reconstructed surface. The second component is to use a
new type of positional encoding with learnable weights to combat noise in the
reconstruction process. We divide the features in the tri-plane into multiple
frequency scales and modulate them with sin and cos functions of different
frequencies. The third component is to use learnable convolution operations on
the tri-plane features using self-attention convolution to produce features
with different frequency bands. The experiments show that PET-NeuS achieves
high-fidelity surface reconstruction on standard datasets. Following previous
work and using the Chamfer metric as the most important way to measure surface
reconstruction quality, we are able to improve upon the NeuS baseline by 57% on
Nerf-synthetic (0.84 compared to 1.97) and by 15.5% on DTU (0.71 compared to
0.84). The qualitative evaluation reveals how our method can better control the
interference of high-frequency noise. Code available at
\url{https://github.com/yiqun-wang/PET-NeuS}.