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. Our project page is
https://fcyycf.github.io/RobustSplat/.
68425f88fa50fdb6ce3674b5
Chuanyu Fu
claimed_verified
2025-06-06T07:40:53.825Z
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fcyycf
user
fcy99
68425f88fa50fdb6ce3674b6
Yuqi Zhang
68425f88fa50fdb6ce3674b7
Kunbin Yao
68425f88fa50fdb6ce3674b8
Guanying Chen
68425f88fa50fdb6ce3674b9
Yuan Xiong
68425f88fa50fdb6ce3674ba
Chuan Huang
68425f88fa50fdb6ce3674bb
Shuguang Cui
68425f88fa50fdb6ce3674bc
Xiaochun Cao
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. Our project page is
https://fcyycf.github.io/RobustSplat/.
RobustSplat addresses artifacts in 3D Gaussian Splatting caused by transient objects through delayed Gaussian growth and scale-cascaded mask bootstrapping.
Gaussian Splatting novel-view synthesis 3D modeling Gaussian densification transient objects Gaussian growth delayed Gaussian growth scale-cascaded mask bootstrapping feature similarity mask prediction