  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.
