Title: Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency,
Abstract: We study the notion of consistency between a 3D shape and a 2D observation
and propose a differentiable formulation which allows computing gradients of
the 3D shape given an observation from an arbitrary view. We do so by
reformulating view consistency using a differentiable ray consistency (DRC)
term. We show that this formulation can be incorporated in a learning framework
to leverage different types of multi-view observations e.g. foreground masks,
depth, color images, semantics etc. as supervision for learning single-view 3D
prediction. We present empirical analysis of our technique in a controlled
setting. We also show that this approach allows us to improve over existing
techniques for single-view reconstruction of objects from the PASCAL VOC
dataset.