scene-dreamer.github.io - SceneDreamer

Description: Project page for SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections

Example domain paragraphs

In this work, we present SceneDreamer , an unconditional generative model for unbounded 3D scenes, which synthesizes large-scale 3D landscapes from random noises. 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 fr

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Given a simplex noise and a style code as input, our model is capable of synthesizing large-scale 3D scenes where the camera can move freely and get realistic renderings. We first derive our BEV scene representation which consists of a height field and a semantic field. Then, we use a generative neural hash grid to parameterize the hyperspace of space-varied and scene-varied latent features given scene semantics and 3D position. Finally, a style-modulated renderer is employed to blend latent features and re

Links to scene-dreamer.github.io (5)

frozenburning.github.io Zhaoxi Chen
wanggcong.github.io Guangcong Wang - Homepage
sparsenerf.github.io SparseNeRF
perf-project.github.io PERF
open3dvr.github.io Open3DVR