Learning surfaces from neural radiance field (NeRF) became a rising topic in
Multi-View Stereo (MVS). Recent Signed Distance Function (SDF)-based methods
demonstrated their ability to reconstruct accurate 3D shapes of Lambertian
scenes. However, their results on reflective scenes are unsatisfactory due to
the entanglement of specular radiance and complicated geometry. To address the
challenges, we propose a Gaussian-based representation of normals in SDF
fields. Supervised by polarization priors, this representation guides the
learning of geometry behind the specular reflection and captures more details
than existing methods. Moreover, we propose a reweighting strategy in the
optimization process to alleviate the noise issue of polarization priors. To
validate the effectiveness of our design, we capture polarimetric information,
and ground truth meshes in additional reflective scenes with various geometry.
We also evaluated our framework on the PANDORA dataset. Comparisons prove our
method outperforms existing neural 3D reconstruction methods in reflective
scenes by a large margin.

通过基于高斯的法线表示，本文提出了一种监督的方式来学习反射场景中几何形状的细节，通过极化先验引导几何学习，并通过优化过程中的重新权重策略来减轻极化先验的噪声问题，实验证明本文的方法在反射场景的神经三维重建中表现出色。