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

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.

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.

As training progresses, vanilla 3DGS suffers from performance degradation and exhibits artifacts due to the increasing number of Gaussians. Disabling Gaussian densification notably improves the results, even achieving performance comparable to the recent robust method SpotLessSplats. Despite producing transient-free rendering, 3DGS w/o densification struggles to recover fine details in regions with sparse Gaussian initialization (highlighted by red arrows).

We introduce RobustSplat, a robust framework for transient-free 3D Gaussian Splatting, effectively mitigating artifacts caused by transient objects in dynamic scenes. Built on our analysis on the relation between Gaussians densification and artifacts caused by transient objects, our approach integrates a delayed Gaussian growth strategy to prioritize static scene optimization and a scale-cascaded mask bootstrapping method for reliable transient object suppression.