EfficientGS: Streamlining Gaussian Splatting for Large-Scale High-Resolution Scene Representation
<|reference_start|>EfficientGS: Streamlining Gaussian Splatting for Large-Scale High-Resolution Scene Representation: In the domain of 3D scene representation, 3D Gaussian Splatting (3DGS) has emerged as a pivotal technology. However, its application to large-scale, high-resolution scenes (exceeding 4k$\times$4k pixels) is hindered by the excessive computational requirements for managing a large number of Gaussians. Addressing this, we introduce 'EfficientGS', an advanced approach that optimizes 3DGS for high-resolution, large-scale scenes. We analyze the densification process in 3DGS and identify areas of Gaussian over-proliferation. We propose a selective strategy, limiting Gaussian increase to key primitives, thereby enhancing the representational efficiency. Additionally, we develop a pruning mechanism to remove redundant Gaussians, those that are merely auxiliary to adjacent ones. For further enhancement, we integrate a sparse order increment for Spherical Harmonics (SH), designed to alleviate storage constraints and reduce training overhead. Our empirical evaluations, conducted on a range of datasets including extensive 4K+ aerial images, demonstrate that 'EfficientGS' not only expedites training and rendering times but also achieves this with a model size approximately tenfold smaller than conventional 3DGS while maintaining high rendering fidelity.<|reference_end|>
@article{liu2024efficientgs:,
    title={EfficientGS: Streamlining Gaussian Splatting for Large-Scale
  High-Resolution Scene Representation},
    author={Wenkai Liu, Tao Guan, Bin Zhu, Lili Ju, Zikai Song, Dan Li, Yuesong
  Wang, Wei Yang},
    journal={arXiv preprint arXiv:2404.12777},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12777},
    primaryClass={cs.CV}
}