Abstract
Recent advances in 3D Gaussian Splatting (3DGS) enable real-time, high-fidelity novel view synthesis (NVS) with explicit 3D representations. However, performance degradation and instability remain significant under sparse-view conditions. In this work, we identify two key failure modes under sparse-view conditions: overfitting in regions with excessive Gaussian density near the camera, and underfitting in distant areas with insufficient Gaussian coverage. To address these challenges, we propose a unified framework D$^2$GS, comprising two key components: a Depth-and-Density Guided Dropout strategy that suppresses overfitting by adaptively masking redundant Gaussians based on density and depth, and a Distance-Aware Fidelity Enhancement module that improves reconstruction quality in under-fitted far-field areas through targeted supervision. Moreover, we introduce a new evaluation metric to quantify the stability of learned Gaussian distributions, providing insights into the robustness of the sparse-view 3DGS. Extensive experiments on multiple datasets demonstrate that our method significantly improves both visual quality and robustness under sparse view conditions. The project page can be found at: this https URL.
Abstract (translated)
最近在3D高斯点置法（3D Gaussian Splatting，简称3DGS）方面取得的进展使得能够实现实时、高质量的新视角合成（NVS），并使用明确的三维表示。然而，在稀疏视图条件下，性能下降和不稳定性仍然是主要问题。在这项工作中，我们识别出两种关键的失败模式：在相机附近高密度区域过度拟合以及远距离区域由于高斯分布不足导致欠拟合。 为了解决这些问题，我们提出了一种统一框架D²GS，该框架包含两个关键组件： 1. 深度和密度引导的Dropout策略（Depth-and-Density Guided Dropout）：通过自适应地基于密度和深度屏蔽冗余高斯点来抑制过度拟合。 2. 距离感知保真度增强模块（Distance-Aware Fidelity Enhancement module）：通过有针对性的监督提高远距离欠拟合区域的重建质量。 此外，我们还引入了一个新的评估指标，用于量化学习到的高斯分布的稳定性，并为稀疏视图下的3DGS提供了关于其鲁棒性的见解。在多个数据集上的广泛实验表明，我们的方法显著提高了稀疏视角条件下的视觉质量和稳健性。项目的网页可以在以下网址找到：this https URL.
URL
https://arxiv.org/abs/2510.08566
