  Holistic 3D scene understanding entails estimation of both layout
configuration and object geometry in a 3D environment. Recent works have shown
advances in 3D scene estimation from various input modalities (e.g., images, 3D
scans), by leveraging 3D supervision (e.g., 3D bounding boxes or CAD models),
for which collection at scale is expensive and often intractable. To address
this shortcoming, we propose a new method to learn 3D scene priors of layout
and shape without requiring any 3D ground truth. Instead, we rely on 2D
supervision from multi-view RGB images. Our method represents a 3D scene as a
latent vector, from which we can progressively decode to a sequence of objects
characterized by their class categories, 3D bounding boxes, and meshes. With
our trained autoregressive decoder representing the scene prior, our method
facilitates many downstream applications, including scene synthesis,
interpolation, and single-view reconstruction. Experiments on 3D-FRONT and
ScanNet show that our method outperforms state of the art in single-view
reconstruction, and achieves state-of-the-art results in scene synthesis
against baselines which require for 3D supervision.
