Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with
  Regularized Score Distillation Sampling
Authors: name: Hayeon Kim, name: Ji Ha Jang, name: Se Young Chun

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.