  In this work, we present SceneDreamer, an unconditional generative model for
unbounded 3D scenes, which synthesizes large-scale 3D landscapes from random
noise. Our framework is learned from in-the-wild 2D image collections only,
without any 3D annotations. At the core of SceneDreamer is a principled
learning paradigm comprising 1) an efficient yet expressive 3D scene
representation, 2) a generative scene parameterization, and 3) an effective
renderer that can leverage the knowledge from 2D images. Our approach begins
with an efficient bird's-eye-view (BEV) representation generated from simplex
noise, which includes a height field for surface elevation and a semantic field
for detailed scene semantics. This BEV scene representation enables 1)
representing a 3D scene with quadratic complexity, 2) disentangled geometry and
semantics, and 3) efficient training. Moreover, we propose a novel generative
neural hash grid to parameterize the latent space based on 3D positions and
scene semantics, aiming to encode generalizable features across various scenes.
Lastly, a neural volumetric renderer, learned from 2D image collections through
adversarial training, is employed to produce photorealistic images. Extensive
experiments demonstrate the effectiveness of SceneDreamer and superiority over
state-of-the-art methods in generating vivid yet diverse unbounded 3D worlds.
