title: RobustSplat: Decoupling Densification and Dynamics for Transient-Free
    3DGS
summary: 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/.
paper_authors._id: 68425f88fa50fdb6ce3674b5
paper_authors.name: Chuanyu Fu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:40:53.825Z
paper_authors._id: 67e6679e4b036872ccb9448d
paper_authors.avatarUrl: /avatars/04dff7f816046f6e82910c894db10ab1.svg
paper_authors.fullname: fcyycf
paper_authors.type: user
paper_authors.user: fcy99
paper_authors._id: 68425f88fa50fdb6ce3674b6
paper_authors.name: Yuqi Zhang
paper_authors._id: 68425f88fa50fdb6ce3674b7
paper_authors.name: Kunbin Yao
paper_authors._id: 68425f88fa50fdb6ce3674b8
paper_authors.name: Guanying Chen
paper_authors._id: 68425f88fa50fdb6ce3674b9
paper_authors.name: Yuan Xiong
paper_authors._id: 68425f88fa50fdb6ce3674ba
paper_authors.name: Chuan Huang
paper_authors._id: 68425f88fa50fdb6ce3674bb
paper_authors.name: Shuguang Cui
paper_authors._id: 68425f88fa50fdb6ce3674bc
paper_authors.name: Xiaochun Cao
paper_title: RobustSplat: Decoupling Densification and Dynamics for Transient-Free
    3DGS
paper_summary: 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/.
paper_ai_summary: RobustSplat addresses artifacts in 3D Gaussian Splatting caused by transient objects through delayed Gaussian growth and scale-cascaded mask bootstrapping.
paper_ai_keywords: Gaussian Splatting novel-view synthesis 3D modeling Gaussian densification transient objects Gaussian growth delayed Gaussian growth scale-cascaded mask bootstrapping feature similarity mask prediction