NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from
  Multiview Images
Yuan Liu Peng Wang Cheng Lin Xiaoxiao Long Jiepeng Wang Lingjie Liu Taku Komura Wenping Wang
We present a neural rendering-based method called NeRO for reconstructing the
geometry and the BRDF of reflective objects from multiview images captured in
an unknown environment. Multiview reconstruction of reflective objects is
extremely challenging because specular reflections are view-dependent and thus
violate the multiview consistency, which is the cornerstone for most multiview
reconstruction methods. Recent neural rendering techniques can model the
interaction between environment lights and the object surfaces to fit the
view-dependent reflections, thus making it possible to reconstruct reflective
objects from multiview images. However, accurately modeling environment lights
in the neural rendering is intractable, especially when the geometry is
unknown. Most existing neural rendering methods, which can model environment
lights, only consider direct lights and rely on object masks to reconstruct
objects with weak specular reflections. Therefore, these methods fail to
reconstruct reflective objects, especially when the object mask is not
available and the object is illuminated by indirect lights. We propose a
two-step approach to tackle this problem. First, by applying the split-sum
approximation and the integrated directional encoding to approximate the
shading effects of both direct and indirect lights, we are able to accurately
reconstruct the geometry of reflective objects without any object masks. Then,
with the object geometry fixed, we use more accurate sampling to recover the
environment lights and the BRDF of the object. Extensive experiments
demonstrate that our method is capable of accurately reconstructing the
geometry and the BRDF of reflective objects from only posed RGB images without
knowing the environment lights and the object masks. Codes and datasets are
available at https://github.com/liuyuan-pal/NeRO.