Point management is a critical component in optimizing 3D Gaussian Splatting (3DGS) models, as the point initiation (e.g., via structure from motion) is distributionally inappropriate. Typically, the Adaptive Density Control (ADC) algorithm is applied, leveraging view-averaged gradient magnitude thresholding for point densification, opacity thresholding for pruning, and regular all-points opacity reset. However, we reveal that this strategy is limited in tackling intricate/special image regions (e.g., transparent) as it is unable to identify all the 3D zones that require point densification, and lacking an appropriate mechanism to handle the ill-conditioned points with negative impacts (occlusion due to false high opacity). To address these limitations, we propose a Localized Point Management (LPM) strategy, capable of identifying those error-contributing zones in the highest demand for both point addition and geometry calibration. Zone identification is achieved by leveraging the underlying multiview geometry constraints, with the guidance of image rendering errors. We apply point densification in the identified zone, whilst resetting the opacity of those points residing in front of these regions so that a new opportunity is created to correct ill-conditioned points. Serving as a versatile plugin, LPM can be seamlessly integrated into existing 3D Gaussian Splatting models. Experimental evaluation across both static 3D and dynamic 4D scenes validate the efficacy of our LPM strategy in boosting a variety of existing 3DGS models both quantitatively and qualitatively. Notably, LPM improves both vanilla 3DGS and SpaceTimeGS to achieve state-of-the-art rendering quality while retaining real-time speeds, outperforming on challenging datasets such as Tanks & Temples and the Neural 3D Video Dataset.
点管理是优化3D高斯涂抹（3DGS）模型的一个关键组成部分，因为点初始化（例如通过运动结构）在分布上是不适当的。通常，会应用自适应密度控制（ADC）算法，利用视图平均梯度幅度阈值进行点密化，透明度阈值进行修剪，以及定期的所有点透明度重置。然而，我们发现这种策略在处理复杂/特殊图像区域（例如透明区域）时存在局限，因为它无法识别所有需要点密化的3D区域，并且缺乏适当的机制来处理带有负面影响的病态点（由于错误高透明度导致的遮挡）。为了解决这些限制，我们提出了一种局部点管理（LPM）策略，能够识别对点增加和几何校正需求最高的那些错误贡献区域。通过利用底层的多视图几何约束和图像渲染错误的指导来实现区域识别。我们在识别的区域内应用点密化，同时重置这些区域前方点的透明度，从而创造了纠正病态点的新机会。作为一个多功能插件，LPM可以无缝集成到现有的3D高斯涂抹模型中。在静态3D和动态4D场景中的实验评估验证了我们的LPM策略在定量和定性上提升各种现有3DGS模型的有效性。值得注意的是，LPM改进了普通3DGS和SpaceTimeGS，实现了业界领先的渲染质量，同时保持了实时速度，在挑战性数据集如Tanks & Temples和Neural 3D Video Dataset上表现优异。