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×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.
在3D场景表现领域，3D高斯喷溅（3DGS）已成为一项关键技术。然而，其应用于大规模、高分辨率场景（超过4k×4k像素）时，由于管理大量高斯的计算需求过高而受到限制。针对这一问题，我们推出了“EfficientGS”，一种优化3DGS以适应高分辨率、大规模场景的先进方法。我们分析了3DGS中的密集化过程，并识别了高斯过度增殖的区域。我们提出了一种选择性策略，仅在关键原始体上限制高斯增加，从而提高了表现效率。此外，我们开发了一种修剪机制，用于移除多余的高斯，即那些仅作为邻近高斯的辅助存在的高斯。为了进一步提升，我们整合了一个稀疏阶数增加的球谐（SH），旨在减轻存储限制并降低训练开销。我们在包括大量4K+航拍图像的多个数据集上进行的实证评估表明，“EfficientGS”不仅加速了训练和渲染时间，而且还以大约是传统3DGS十分之一的模型大小实现了高保真度渲染。