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。

本地高斯点管理

Localized Gaussian Point Management

3D Gaussian Splatting (3D-GS) technique couples 3D Gaussian primitives with
differentiable rasterization to achieve high-quality novel view synthesis
results while providing advanced real-time rendering performance. However, due
to the flaw of its adaptive density control strategy in 3D-GS, it frequently
suffers from over-reconstruction issue in intricate scenes containing
high-frequency details, leading to blurry rendered images. The underlying
reason for the flaw has still been under-explored. In this work, we present a
comprehensive analysis of the cause of aforementioned artifacts, namely
gradient collision, which prevents large Gaussians in over-reconstructed
regions from splitting. To address this issue, we propose the novel
homodirectional view-space positional gradient as the criterion for
densification. Our strategy efficiently identifies large Gaussians in
over-reconstructed regions, and recovers fine details by splitting. We evaluate
our proposed method on various challenging datasets. The experimental results
indicate that our approach achieves the best rendering quality with reduced or
similar memory consumption. Our method is easy to implement and can be
incorporated into a wide variety of most recent Gaussian Splatting-based
methods. We will open source our codes upon formal publication. Our project
page is available at: this https URL

本文研究了 3D 高斯喷射技术中的密度控制策略的不足之处，分析了导致过度重构和模糊渲染的原因，并提出了一种新的视图空间渐变方向梯度作为密度增加的判据，有效地解决了该问题，并在各种挑战性数据集上进行了评估，结果显示我们的方法在渲染质量上表现最佳，同时内存消耗降低或相似。

AbsGS：恢复三维高斯飞溅的细节

AbsGS: Recovering Fine Details for 3D Gaussian Splatting

In this paper, we address the limitations of Adaptive Density Control (ADC)
in 3D Gaussian Splatting (3DGS), a scene representation method achieving
high-quality, photorealistic results for novel view synthesis. ADC has been
introduced for automatic 3D point primitive management, controlling
densification and pruning, however, with certain limitations in the
densification logic. Our main contribution is a more principled, pixel-error
driven formulation for density control in 3DGS, leveraging an auxiliary,
per-pixel error function as the criterion for densification. We further
introduce a mechanism to control the total number of primitives generated per
scene and correct a bias in the current opacity handling strategy of ADC during
cloning operations. Our approach leads to consistent quality improvements
across a variety of benchmark scenes, without sacrificing the method's
efficiency.

本文提出了一种更加原则性、像素误差驱动的 3D 高斯喷洒（3DGS）中密度控制方法，解决了自适应密度控制（ADC）的局限性，并在新视角合成中实现高质量、逼真结果。