Universal Differentiable Renderer for Implicit Neural Representations

The goal of this work is to learn implicit 3D shape representation with 2D supervision (i.e., a collection of images). To that end we introduce the Universal Differentiable Renderer (UDR) a neural network architecture that can provably approximate reflected light from an implicit neural representation of a 3D surface, under a wide set of reflectance properties and lighting conditions. Experimenting with the task of multiview 3D reconstruction, we find our model to improve upon the baselines in the accuracy of the reconstructed 3D geometry and rendering from unseen viewing directions.