GaussianSpa: An "Optimizing-Sparsifying" Simplification Framework for Compact and High-Quality 3D Gaussian Splatting
3D Gaussian Splatting (3DGS) has emerged as a mainstream for novel view synthesis, leveraging continuous aggregations of Gaussian functions to model scene geometry. However, 3DGS suffers from substantial memory requirements to store the multitude of Gaussians, hindering its practicality. To address this challenge, we introduce GaussianSpa, an optimization-based simplification framework for compact and high-quality 3DGS. Specifically, we formulate the simplification as an optimization problem associated with the 3DGS training. Correspondingly, we propose an efficient "optimizing-sparsifying" solution that alternately solves two independent sub-problems, gradually imposing strong sparsity onto the Gaussians in the training process. Our comprehensive evaluations on various datasets show the superiority of GaussianSpa over existing state-of-the-art approaches. Notably, GaussianSpa achieves an average PSNR improvement of 0.9 dB on the real-world Deep Blending dataset with 10× fewer Gaussians compared to the vanilla 3DGS.
3D Gaussian Splatting (3DGS) 已成为新视图合成的主流方法，通过连续聚合高斯函数来建模场景几何。然而，3DGS 需要大量内存存储大量的高斯基元，限制了其实用性。为了解决这一问题，我们提出了 GaussianSpa，一种基于优化的简化框架，用于实现紧凑且高质量的 3DGS。具体而言，我们将简化问题表述为与 3DGS 训练相关的优化问题。为此，我们提出了一种高效的“优化-稀疏化”解决方案，通过交替解决两个独立的子问题，在训练过程中逐步对高斯基元施加强稀疏性。我们在多个数据集上的综合评估表明，GaussianSpa 相较于现有最先进方法表现出显著优势。尤其是在真实世界的 Deep Blending 数据集上，GaussianSpa 在使用 10 倍更少的高斯基元的情况下，平均 PSNR 提升了 0.9 dB，相较于标准 3DGS 展现了卓越的效果。