Inverse Rendering of Translucent Objects using Physical and Neural
  Renderers
Chenhao Li Trung Thanh Ngo Hajime Nagahara
In this work, we propose an inverse rendering model that estimates 3D shape,
spatially-varying reflectance, homogeneous subsurface scattering parameters,
and an environment illumination jointly from only a pair of captured images of
a translucent object. In order to solve the ambiguity problem of inverse
rendering, we use a physically-based renderer and a neural renderer for scene
reconstruction and material editing. Because two renderers are differentiable,
we can compute a reconstruction loss to assist parameter estimation. To enhance
the supervision of the proposed neural renderer, we also propose an augmented
loss. In addition, we use a flash and no-flash image pair as the input. To
supervise the training, we constructed a large-scale synthetic dataset of
translucent objects, which consists of 117K scenes. Qualitative and
quantitative results on both synthetic and real-world datasets demonstrated the
effectiveness of the proposed model.