  Recent works have shown exciting results in unsupervised image de-rendering
-- learning to decompose 3D shape, appearance, and lighting from single-image
collections without explicit supervision. However, many of these assume
simplistic material and lighting models. We propose a method, termed RADAR,
that can recover environment illumination and surface materials from real
single-image collections, relying neither on explicit 3D supervision, nor on
multi-view or multi-light images. Specifically, we focus on rotationally
symmetric artefacts that exhibit challenging surface properties including
specular reflections, such as vases. We introduce a novel self-supervised
albedo discriminator, which allows the model to recover plausible albedo
without requiring any ground-truth during training. In conjunction with a shape
reconstruction module exploiting rotational symmetry, we present an end-to-end
learning framework that is able to de-render the world's revolutionary
artefacts. We conduct experiments on a real vase dataset and demonstrate
compelling decomposition results, allowing for applications including
free-viewpoint rendering and relighting.
