Title: Laser: Latent Set Representations for 3D Generative Modeling. Abstract: NeRF provides unparalleled fidelity of novel view synthesis---rendering a 3D scene from an arbitrary viewpoint. NeRF requires training on a large number of views that fully cover a scene, which limits its applicability.
While these issues can be addressed by learning a prior over scenes in various forms, previous approaches have been either applied to overly simple scenes or struggling to render unobserved parts.
We introduce Laser-NV---a generative model which achieves high modelling capacity, and which is based on a set-valued latent representation modelled by normalizing flows.
Similarly to previous amortized approaches, Laser-NV learns structure from multiple scenes and is capable of fast, feed-forward inference from few views. 
To encourage higher rendering fidelity and consistency with observed views, Laser-NV further incorporates a geometry-informed attention mechanism over the observed views.
Laser-NV further produces diverse and plausible completions of occluded parts of a scene while remaining consistent with observations.
Laser-NV shows state-of-the-art novel-view synthesis quality when evaluated on ShapeNet and on a novel simulated City dataset, which features high uncertainty in the unobserved regions of the scene.