基于正则化分数蒸馏采样的3D高斯溅射中鲁棒的三维掩码部件级编辑

Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with Regularized Score Distillation Sampling

July 15, 2025

作者: Hayeon Kim, Ji Ha Jang, Se Young Chun

cs.AI

摘要

近期，三维神经表示与实例级编辑模型的进展，已高效推动了高质量三维内容的生成。然而，实现精确的局部三维编辑仍面临挑战，特别是在高斯泼溅技术中，这源于多视角二维部件分割的不一致性及评分蒸馏采样（SDS）损失固有的模糊性。为克服这些局限，我们提出了RoMaP，一种创新的局部三维高斯编辑框架，支持精确且显著的部件级修改。首先，我们引入了具备三维几何感知标签预测（3D-GALP）的鲁棒三维掩码生成模块，该模块利用球谐函数（SH）系数建模视角依赖的标签变化与软标签特性，从而在多个视角下获得准确且一致的部件分割。其次，我们提出了一种正则化的SDS损失，将标准SDS损失与额外正则化项相结合。特别地，通过我们的计划潜在混合与部件（SLaMP）编辑方法引入了L1锚定损失，该方法生成高质量的部件编辑二维图像，并将修改严格限定于目标区域，同时保持上下文连贯性。其他正则化项，如高斯先验移除，通过允许超越现有上下文的改变，进一步提升了灵活性，而鲁棒的三维掩码则有效防止了非预期的编辑。实验结果表明，RoMaP在重建与生成的高斯场景及物体上，无论是定性还是定量分析，均实现了当前最优的局部三维编辑效果，为更稳健、灵活的部件级三维高斯编辑开辟了可能。代码发布于https://janeyeon.github.io/romap。

English

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.