{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gaussiancity-generative-gaussian-splatting","title":"GaussianCity: Generative Gaussian Splatting for Unbounded 3D City Generation","arxiv_id":"2406.06526","date":"2024-06-10","proceeding":null,"authors":["Haozhe Xie","Zhaoxi Chen","Fangzhou Hong","Ziwei Liu"],"abstract":"3D city generation with NeRF-based methods shows promising generation results but is computationally inefficient. Recently 3D Gaussian Splatting (3D-GS) has emerged as a highly efficient alternative for object-level 3D generation. However, adapting 3D-GS from finite-scale 3D objects and humans to infinite-scale 3D cities is non-trivial. Unbounded 3D city generation entails significant storage overhead (out-of-memory issues), arising from the need to expand points to billions, often demanding hundreds of Gigabytes of VRAM for a city scene spanning 10km^2. In this paper, we propose GaussianCity, a generative Gaussian Splatting framework dedicated to efficiently synthesizing unbounded 3D cities with a single feed-forward pass. Our key insights are two-fold: 1) Compact 3D Scene Representation: We introduce BEV-Point as a highly compact intermediate representation, ensuring that the growth in VRAM usage for unbounded scenes remains constant, thus enabling unbounded city generation. 2) Spatial-aware Gaussian Attribute Decoder: We present spatial-aware BEV-Point decoder to produce 3D Gaussian attributes, which leverages Point Serializer to integrate the structural and contextual characteristics of BEV points. Extensive experiments demonstrate that GaussianCity achieves state-of-the-art results in both drone-view and street-view 3D city generation. Notably, compared to CityDreamer, GaussianCity exhibits superior performance with a speedup of 60 times (10.72 FPS v.s. 0.18 FPS).","url_abs":"https://arxiv.org/abs/2406.06526v2","url_pdf":"https://arxiv.org/pdf/2406.06526v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gaussiancity-generative-gaussian-splatting","repo_url":"https://github.com/hzxie/GaussianCity","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"3d-generation","task_name":"3D Generation"},{"task_slug":"nerf","task_name":"NeRF"},{"task_slug":"scene-generation","task_name":"Scene Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/scene-generation-on-googleearth","task":"Scene Generation","dataset":"GoogleEarth","model":"GaussianCity","rank_in_archive_order":1,"of":5,"metrics":{"Camera Error":"0.057","Depth Error":"0.136","FID":"86.94","KID":"0.09"},"uses_additional_data":false},{"leaderboard":"/sota/scene-generation-on-kitti","task":"Scene Generation","dataset":"KITTI","model":"GaussianCity","rank_in_archive_order":1,"of":1,"metrics":{"FID":"29.5","KID":"0.017"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2406.06526","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.06526"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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