用马尔可夫链蒙特卡洛方法优化3D高斯点云，减少冗余计算。
Metropolis-Hastings Sampling for 3D Gaussian Reconstruction
- 将3D高斯点云的增删视为概率采样过程，基于多视角误差动态调整
- 在多个基准数据集上减少高斯点数量，收敛更快且图像质量相当或更好
- 无需预设场景复杂度，适合需要自适应重建的3D视觉任务
我们提出一种针对3D高斯溅射（3DGS）的自适应采样框架，利用统一的马尔可夫链蒙特卡洛（Metropolis-Hastings）方法整合多视角光度误差信号。传统3DGS依赖启发式密度控制机制（如克隆、分裂、剪枝），易导致冗余计算或过早移除有益高斯点。本框架将稠密化与剪枝重构成概率采样过程，根据聚合的多视角误差和不透明度评分动态插入与重定位高斯点。基于误差驱动的重要性评分进行贝叶斯接受测试，显著降低对启发式规则的依赖，提升灵活性，并自适应推断高斯分布而无需预设场景复杂度。在Mip-NeRF360、Tanks and Temples和Deep Blending等基准数据集上的实验表明，该方法减少了所需高斯点数量，在保持或略微超越当前最先进模型视图合成质量的同时实现更快收敛。
原文摘要 · Abstract (English)
We propose an adaptive sampling framework for 3D Gaussian Splatting (3DGS) that leverages comprehensive multi-view photometric error signals within a unified Metropolis-Hastings approach. Vanilla 3DGS heavily relies on heuristic-based density-control mechanisms (e.g., cloning, splitting, and pruning), which can lead to redundant computations or premature removal of beneficial Gaussians. Our framework overcomes these limitations by reformulating densification and pruning as a probabilistic sampling process, dynamically inserting and relocating Gaussians based on aggregated multi-view errors and opacity scores. Guided by Bayesian acceptance tests derived from these error-based importance scores, our method substantially reduces reliance on heuristics, offers greater flexibility, and adaptively infers Gaussian distributions without requiring predefined scene complexity. Experiments on benchmark datasets, including Mip-NeRF360, Tanks and Temples and Deep Blending, show that our approach reduces the number of Gaussians needed, achieving faster convergence while matching or modestly surpassing the view-synthesis quality of state-of-the-art models.
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