SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections

• URL: http://arxiv.org/abs/2302.01330v3
• Date: Thu, 7 Dec 2023 18:58:30 GMT
• Title: SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections
• Authors: Zhaoxi Chen, Guangcong Wang, Ziwei Liu
• Abstract summary: We present SceneDreamer, an unconditional generative model for unbounded 3D scenes. Our framework is learned from in-the-wild 2D image collections only, without any 3D annotations.
• Score: 49.802462165826554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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