RobustSplat：解耦密度化与动态性，实现无瞬态干扰的3D高斯溅射

摘要

三维高斯溅射（3DGS）因其在新视角合成与三维建模中实现实时、照片级真实感渲染而备受瞩目。然而，现有方法在准确建模受瞬态物体影响的场景时面临挑战，导致渲染图像中出现伪影。我们发现，高斯密度化过程虽增强了场景细节捕捉，却无意中因生成额外高斯模型来模拟瞬态干扰而加剧了这些伪影。为此，我们提出了RobustSplat，一种基于两项关键设计的稳健解决方案。首先，我们引入了一种延迟高斯增长策略，优先优化静态场景结构，随后才允许高斯分裂/克隆，从而在早期优化中减少对瞬态物体的过拟合。其次，我们设计了一种尺度级联掩码引导方法，先利用低分辨率特征相似性监督进行可靠的初始瞬态掩码估计，得益于其更强的语义一致性和对噪声的鲁棒性，再逐步过渡到高分辨率监督，以实现更精确的掩码预测。在多个挑战性数据集上的广泛实验表明，我们的方法超越了现有技术，充分证明了其鲁棒性和有效性。项目页面请访问：https://fcyycf.github.io/RobustSplat/。

English

3D Gaussian Splatting (3DGS) has gained significant attention for its real-time, photo-realistic rendering in novel-view synthesis and 3D modeling. However, existing methods struggle with accurately modeling scenes affected by transient objects, leading to artifacts in the rendered images. We identify that the Gaussian densification process, while enhancing scene detail capture, unintentionally contributes to these artifacts by growing additional Gaussians that model transient disturbances. To address this, we propose RobustSplat, a robust solution based on two critical designs. First, we introduce a delayed Gaussian growth strategy that prioritizes optimizing static scene structure before allowing Gaussian splitting/cloning, mitigating overfitting to transient objects in early optimization. Second, we design a scale-cascaded mask bootstrapping approach that first leverages lower-resolution feature similarity supervision for reliable initial transient mask estimation, taking advantage of its stronger semantic consistency and robustness to noise, and then progresses to high-resolution supervision to achieve more precise mask prediction. Extensive experiments on multiple challenging datasets show that our method outperforms existing methods, clearly demonstrating the robustness and effectiveness of our method. Our project page is https://fcyycf.github.io/RobustSplat/.

RobustSplat：解耦密度化与动态性，实现无瞬态干扰的3D高斯溅射

RobustSplat: Decoupling Densification and Dynamics for Transient-Free 3DGS

摘要

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