  3D reconstruction is a fundamental problem in computer vision, and the task
is especially challenging when the object to reconstruct is partially or fully
occluded. We introduce a method that uses the shadows cast by an unobserved
object in order to infer the possible 3D volumes behind the occlusion. We
create a differentiable image formation model that allows us to jointly infer
the 3D shape of an object, its pose, and the position of a light source. Since
the approach is end-to-end differentiable, we are able to integrate learned
priors of object geometry in order to generate realistic 3D shapes of different
object categories. Experiments and visualizations show that the method is able
to generate multiple possible solutions that are consistent with the
observation of the shadow. Our approach works even when the position of the
light source and object pose are both unknown. Our approach is also robust to
real-world images where ground-truth shadow mask is unknown.
