优化3D高斯点云渲染，提升画质与收敛性
Opt3DGS: Optimizing 3D Gaussian Splatting with Adaptive Exploration and Curvature-Aware Exploitation
- 分探索与利用两阶段优化，自适应调整搜索策略
- 在多个数据集上实现当前最优渲染质量
- 适合追求高质量3D重建的科研与工程人员
3D高斯点云（3DGS）已成为新视角合成的主流框架，但其核心优化问题仍缺乏深入研究。本文指出3DGS优化中的两大关键问题：陷入次优局部极小和收敛质量不足。为此，提出Opt3DGS框架，通过自适应探索与曲率引导利用的两阶段优化流程进行改进。探索阶段采用自适应加权随机梯度朗之万动力学（SGLD）方法增强全局搜索能力，以逃离局部极小；利用阶段则引入基于局部拟牛顿方向的Adam优化器，利用曲率信息实现精确高效收敛。在多个基准数据集上的大量实验表明，Opt3DGS在不改变3DGS底层表示的前提下，显著提升了渲染质量，达到当前最优水平。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has emerged as a leading framework for novel view synthesis, yet its core optimization challenges remain underexplored. We identify two key issues in 3DGS optimization: entrapment in suboptimal local optima and insufficient convergence quality. To address these, we propose Opt3DGS, a robust framework that enhances 3DGS through a two-stage optimization process of adaptive exploration and curvature-guided exploitation. In the exploration phase, an Adaptive Weighted Stochastic Gradient Langevin Dynamics (SGLD) method enhances global search to escape local optima. In the exploitation phase, a Local Quasi-Newton Direction-guided Adam optimizer leverages curvature information for precise and efficient convergence. Extensive experiments on diverse benchmark datasets demonstrate that Opt3DGS achieves state-of-the-art rendering quality by refining the 3DGS optimization process without modifying its underlying representation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。