We present NeRSP, a Neural 3D reconstruction technique for Reflective
surfaces with Sparse Polarized images. Reflective surface reconstruction is
extremely challenging as specular reflections are view-dependent and thus
violate the multiview consistency for multiview stereo. On the other hand,
sparse image inputs, as a practical capture setting, commonly cause incomplete
or distorted results due to the lack of correspondence matching. This paper
jointly handles the challenges from sparse inputs and reflective surfaces by
leveraging polarized images. We derive photometric and geometric cues from the
polarimetric image formation model and multiview azimuth consistency, which
jointly optimize the surface geometry modeled via implicit neural
representation. Based on the experiments on our synthetic and real datasets, we
achieve the state-of-the-art surface reconstruction results with only 6 views
as input.

我们提出的 NeRSP 是一种用于具有稀疏偏振图像的反射表面的神经 3D 重建技术。通过利用偏振图像，我们共同处理了稀疏输入和反射表面所带来的挑战，并通过多视角一致性优化了表面几何模型。在合成和真实数据集上的实验证明，我们仅使用 6 个视角的输入就实现了最先进的表面重建结果。