RobustSplat: Decoupling Densification and Dynamics for Transient-Free
  3DGS
Authors: name: Chuanyu Fu, name: Yuqi Zhang, name: Kunbin Yao, name: Guanying Chen, name: Yuan Xiong, name: Chuan Huang, name: Shuguang Cui, name: Xiaochun Cao

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/.