  In this work we address the challenging problem of multiview 3D surface
reconstruction. We introduce a neural network architecture that simultaneously
learns the unknown geometry, camera parameters, and a neural renderer that
approximates the light reflected from the surface towards the camera. The
geometry is represented as a zero level-set of a neural network, while the
neural renderer, derived from the rendering equation, is capable of
(implicitly) modeling a wide set of lighting conditions and materials. We
trained our network on real world 2D images of objects with different material
properties, lighting conditions, and noisy camera initializations from the DTU
MVS dataset. We found our model to produce state of the art 3D surface
reconstructions with high fidelity, resolution and detail.
