用优化与稀疏化结合的方法，让3D高斯点云更小更清晰。
GaussianSpa: An "Optimizing-Sparsifying" Simplification Framework for Compact and High-Quality 3D Gaussian Splatting
- 通过交替优化与稀疏化，逐步压缩高斯点数量。
- 真实场景下用10倍少的点，图像质量提升0.9dB。
- 适合需要轻量化3D重建的应用，如移动设备部署。
3D高斯点阵（3DGS）已成为新视角合成的主流方法，利用连续高斯函数叠加建模场景几何。然而，3DGS需存储大量高斯点，内存占用大，限制实际应用。为此，我们提出GaussianSpa，一种基于优化的简化框架，实现紧凑且高质量的3DGS。具体地，将简化过程建模为与3DGS训练相关的优化问题，并设计高效“优化-稀疏化”解法，交替求解两个独立子问题，在训练中逐步施加强稀疏性。在多个数据集上的综合评估表明，GaussianSpa优于现有最优方法。尤其在真实世界Deep Blending数据集上，以10倍更少的高斯点，平均PSNR提升0.9 dB。
原文摘要 · Abstract (English)
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$\times$ fewer Gaussians compared to the vanilla 3DGS. Our project page is available at https://noodle-lab.github.io/gaussianspa/.
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