  In this paper, we define a new problem of recovering the 3D geometry of an
object confined in a transparent enclosure. We also propose a novel method for
solving this challenging problem. Transparent enclosures pose challenges of
multiple light reflections and refractions at the interface between different
propagation media e.g. air or glass. These multiple reflections and refractions
cause serious image distortions which invalidate the single viewpoint
assumption. Hence the 3D geometry of such objects cannot be reliably
reconstructed using existing methods, such as traditional structure from motion
or modern neural reconstruction methods. We solve this problem by explicitly
modeling the scene as two distinct sub-spaces, inside and outside the
transparent enclosure. We use an existing neural reconstruction method (NeuS)
that implicitly represents the geometry and appearance of the inner subspace.
In order to account for complex light interactions, we develop a hybrid
rendering strategy that combines volume rendering with ray tracing. We then
recover the underlying geometry and appearance of the model by minimizing the
difference between the real and hybrid rendered images. We evaluate our method
on both synthetic and real data. Experiment results show that our method
outperforms the state-of-the-art (SOTA) methods. Codes and data will be
available at https://github.com/hirotong/ReNeuS
