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

Date: unknown

Location: dekuliutesla.github.io

He Guan1,2, Chuanchen Luo1, Lue Fan1,2, Junran Peng1, Zhaoxiang Zhang1,2,3,4,

1Institute of Automation, Chinese Academy of Sciences 2University of Chinese Academy of Sciences (UCAS) 3Centre for Artificial Intelligence and Robotics (HKISI, CAS) 4State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS)

Abstract

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-the-art rendering quality, enabling consistent real-time rendering of large-scale scenes across vastly different scales.

CityGS: No LoD

Without our proposed LoD technique, the MatrixCity is depicted by 25 million Gaussians. The consequent speed of 18 FPS (tested on A100) leads to unpleasant roaming experience.

CityGS

With the support of LoD, our CityGS can be rendered in real-time under vastly different scales. The average speed is 36 FPS (tested on A100).

BibTeX

@misc{liu2024citygaussian,
      title={CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussians}, 
      author={Yang Liu and He Guan and Chuanchen Luo and Lue Fan and Junran Peng and Zhaoxiang Zhang},
      year={2024},
      eprint={2404.01133},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

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