  Learning-based 3D reconstruction methods have shown impressive results.
However, most methods require 3D supervision which is often hard to obtain for
real-world datasets. Recently, several works have proposed differentiable
rendering techniques to train reconstruction models from RGB images.
Unfortunately, these approaches are currently restricted to voxel- and
mesh-based representations, suffering from discretization or low resolution. In
this work, we propose a differentiable rendering formulation for implicit shape
and texture representations. Implicit representations have recently gained
popularity as they represent shape and texture continuously. Our key insight is
that depth gradients can be derived analytically using the concept of implicit
differentiation. This allows us to learn implicit shape and texture
representations directly from RGB images. We experimentally show that our
single-view reconstructions rival those learned with full 3D supervision.
Moreover, we find that our method can be used for multi-view 3D reconstruction,
directly resulting in watertight meshes.
