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CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussians

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arxiv 2404.01133 v3 pith:XHWPB4DG submitted 2024-04-01 cs.CV

Yang Liu , He Guan , Chuanchen Luo , Lue Fan , Naiyan Wang , Junran Peng , Zhaoxiang Zhang This is my paper
classification cs.CV
keywords renderingtraininglarge-scalereal-timeacrossscalessceneapproach
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The advancement of real-time 3D scene reconstruction and novel view synthesis has been significantly propelled by 3D Gaussian Splatting (3DGS). However, effectively training large-scale 3DGS and rendering it in real-time across various scales remains challenging. This paper introduces CityGaussian (CityGS), which employs a novel divide-and-conquer training approach and Level-of-Detail (LoD) strategy for efficient large-scale 3DGS training and rendering. Specifically, the global scene prior and adaptive training data selection enables efficient training and seamless fusion. Based on fused Gaussian primitives, we generate different detail levels through compression, and realize fast rendering across various scales through the proposed block-wise detail levels selection and aggregation strategy. Extensive experimental results on large-scale scenes demonstrate that our approach attains state-of-theart rendering quality, enabling consistent real-time rendering of largescale scenes across vastly different scales. Our project page is available at https://dekuliutesla.github.io/citygs/.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Splats in Splats++: Robust and Generalizable 3D Gaussian Splatting Steganography

    cs.CV 2026-04 conditional novelty 7.0 of 10

    Splats in Splats++ embeds messages into 3DGS via importance-graded SH encryption, hash-grid opacity mapping, and a gradient-gated consistency loss, achieving higher fidelity and robustness than prior methods.

  2. Beyond a Single Light: A Large-Scale Aerial Dataset for Urban Scene Reconstruction Under Varying Illumination

    cs.CV 2025-12 conditional novelty 7.0 of 10

    SkyLume contributes 10 real-world UAV urban regions captured at morning, noon, and afternoon with LiDAR-based ground truth, plus the Temporal Consistency Coefficient metric for cross-time albedo stability.

  3. SharpSplat: Edge-Regularized 3D Gaussian Splatting for High Fidelity Urban Building Reconstruction from UAV images

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Aligning rendered Sobel edges to SAM3-masked building edges during 3DGS training modestly improves facade sharpness on UAV urban scenes without changing the Gaussian architecture.

  4. DiskChunGS: Large-Scale 3D Gaussian SLAM Through Chunk-Based Memory Management

    cs.RO 2025-11 conditional novelty 5.0 of 10

    Storing inactive spatial chunks of a 3D Gaussian map on disk and loading only camera-visible chunks into GPU memory lets DiskChunGS map all 11 KITTI sequences on a 24 GB GPU without memory failures.

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