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: https://insta360-research-team.github.io/DDGS-website/.

翻译：近年来，3D高斯溅射（3DGS）技术凭借其显式的三维表示，实现了实时、高保真的新视角合成。然而，在稀疏视图条件下，其性能下降与不稳定性问题依然显著。本研究识别了稀疏视图条件下的两种关键失效模式：在相机附近高斯密度过高的区域出现过拟合，以及在远处高斯覆盖不足的区域出现欠拟合。为应对这些挑战，我们提出了一个统一框架D$^2$GS，它包含两个核心组件：一种基于深度与密度引导的丢弃策略，通过根据密度和深度自适应地屏蔽冗余高斯点来抑制过拟合；以及一个距离感知的保真度增强模块，通过有针对性的监督来改善欠拟合远场区域的重建质量。此外，我们引入了一种新的评估指标，用于量化所学高斯分布的稳定性，从而深入理解稀疏视图3DGS的鲁棒性。在多个数据集上的大量实验表明，我们的方法在稀疏视图条件下显著提升了视觉质量和鲁棒性。项目页面位于：https://insta360-research-team.github.io/DDGS-website/。