Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with
  Regularized Score Distillation Sampling
Recent advances in 3D neural representations and instance-level editing
models have enabled the efficient creation of high-quality 3D content. However,
achieving precise local 3D edits remains challenging, especially for Gaussian
Splatting, due to inconsistent multi-view 2D part segmentations and inherently
ambiguous nature of Score Distillation Sampling (SDS) loss. To address these
limitations, we propose RoMaP, a novel local 3D Gaussian editing framework that
enables precise and drastic part-level modifications. First, we introduce a
robust 3D mask generation module with our 3D-Geometry Aware Label Prediction
(3D-GALP), which uses spherical harmonics (SH) coefficients to model
view-dependent label variations and soft-label property, yielding accurate and
consistent part segmentations across viewpoints. Second, we propose a
regularized SDS loss that combines the standard SDS loss with additional
regularizers. In particular, an L1 anchor loss is introduced via our Scheduled
Latent Mixing and Part (SLaMP) editing method, which generates high-quality
part-edited 2D images and confines modifications only to the target region
while preserving contextual coherence. Additional regularizers, such as
Gaussian prior removal, further improve flexibility by allowing changes beyond
the existing context, and robust 3D masking prevents unintended edits.
Experimental results demonstrate that our RoMaP achieves state-of-the-art local
3D editing on both reconstructed and generated Gaussian scenes and objects
qualitatively and quantitatively, making it possible for more robust and
flexible part-level 3D Gaussian editing. Code is available at
https://janeyeon.github.io/romap.
687f16ce33947f780d9b4b3d
Hayeon Kim
claimed_verified
2025-07-22T07:47:42.011Z
63b3fea57af2e415f258a0ea
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HayeonKim
user
yeonE
687f16ce33947f780d9b4b3e
Ji Ha Jang
claimed_verified
2025-07-22T07:47:44.006Z
64c77dc26a26cddbec858bcb
/avatars/635e64f8205c194e63876cba9d8c5449.svg
jihajang
user
jeeit17
687f16ce33947f780d9b4b3f
Se Young Chun
Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with
  Regularized Score Distillation Sampling
Recent advances in 3D neural representations and instance-level editing
models have enabled the efficient creation of high-quality 3D content. However,
achieving precise local 3D edits remains challenging, especially for Gaussian
Splatting, due to inconsistent multi-view 2D part segmentations and inherently
ambiguous nature of Score Distillation Sampling (SDS) loss. To address these
limitations, we propose RoMaP, a novel local 3D Gaussian editing framework that
enables precise and drastic part-level modifications. First, we introduce a
robust 3D mask generation module with our 3D-Geometry Aware Label Prediction
(3D-GALP), which uses spherical harmonics (SH) coefficients to model
view-dependent label variations and soft-label property, yielding accurate and
consistent part segmentations across viewpoints. Second, we propose a
regularized SDS loss that combines the standard SDS loss with additional
regularizers. In particular, an L1 anchor loss is introduced via our Scheduled
Latent Mixing and Part (SLaMP) editing method, which generates high-quality
part-edited 2D images and confines modifications only to the target region
while preserving contextual coherence. Additional regularizers, such as
Gaussian prior removal, further improve flexibility by allowing changes beyond
the existing context, and robust 3D masking prevents unintended edits.
Experimental results demonstrate that our RoMaP achieves state-of-the-art local
3D editing on both reconstructed and generated Gaussian scenes and objects
qualitatively and quantitatively, making it possible for more robust and
flexible part-level 3D Gaussian editing. Code is available at
https://janeyeon.github.io/romap.
RoMaP, a novel framework, enhances precise local 3D Gaussian editing through robust 3D mask generation and regularized Score Distillation Sampling loss, achieving state-of-the-art results.
Gaussian Splatting Score Distillation Sampling 3D-GALP spherical harmonics L1 anchor loss Scheduled Latent Mixing and Part Gaussian prior removal 3D mask generation local 3D editing Gaussian scenes Gaussian objects