  We tackle the challenge of learning a distribution over complex, realistic,
indoor scenes. In this paper, we introduce Generative Scene Networks (GSN),
which learns to decompose scenes into a collection of many local radiance
fields that can be rendered from a free moving camera. Our model can be used as
a prior to generate new scenes, or to complete a scene given only sparse 2D
observations. Recent work has shown that generative models of radiance fields
can capture properties such as multi-view consistency and view-dependent
lighting. However, these models are specialized for constrained viewing of
single objects, such as cars or faces. Due to the size and complexity of
realistic indoor environments, existing models lack the representational
capacity to adequately capture them. Our decomposition scheme scales to larger
and more complex scenes while preserving details and diversity, and the learned
prior enables high-quality rendering from viewpoints that are significantly
different from observed viewpoints. When compared to existing models, GSN
produces quantitatively higher-quality scene renderings across several
different scene datasets.
