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Computer Science > Computer Vision and Pattern Recognition

arXiv:2309.00610 (cs)
[Submitted on 1 Sep 2023]

Title:CityDreamer: Compositional Generative Model of Unbounded 3D
Cities

Authors:Haozhe Xie, Zhaoxi Chen, Fangzhou Hong, Ziwei Liu
Download a PDF of the paper titled CityDreamer: Compositional
Generative Model of Unbounded 3D Cities, by Haozhe Xie and 3 other
authors
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    Abstract: In recent years, extensive research has focused on 3D
    natural scene generation, but the domain of 3D city generation
    has not received as much exploration. This is due to the greater
    challenges posed by 3D city generation, mainly because humans are
    more sensitive to structural distortions in urban environments.
    Additionally, generating 3D cities is more complex than 3D
    natural scenes since buildings, as objects of the same class,
    exhibit a wider range of appearances compared to the relatively
    consistent appearance of objects like trees in natural scenes. To
    address these challenges, we propose CityDreamer, a compositional
    generative model designed specifically for unbounded 3D cities,
    which separates the generation of building instances from other
    background objects, such as roads, green lands, and water areas,
    into distinct modules. Furthermore, we construct two datasets,
    OSM and GoogleEarth, containing a vast amount of real-world city
    imagery to enhance the realism of the generated 3D cities both in
    their layouts and appearances. Through extensive experiments,
    CityDreamer has proven its superiority over state-of-the-art
    methods in generating a wide range of lifelike 3D cities.

Comments: Project page: this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2309.00610 [cs.CV]
          (or arXiv:2309.00610v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2309.00610
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Haozhe Xie [view email]
[v1] Fri, 1 Sep 2023 17:57:02 UTC (16,013 KB)
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