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.

使用本地化的点管理策略（LPM）解决现有 3D 高斯平滑模型中点扩增和几何校准的需求，并通过借鉴图像渲染错误和多视角几何约束来识别出错误贡献区域，在保持实时速度的同时，在诸如 Tanks＆Temples 和 Neural 3D 视频数据集等具有挑战性的数据集上改善了渲染质量，实现了令人满意的定量和定性效果。