3D场景中可控制地移除多个物体，保持几何一致性和视觉质量。
CoGeo-GS: Concept-Driven and Geometry-Aware Multi-Object Removal in 3D Scenes
- 用语义标签标记高斯点，单次优化实现多物体灵活选择
- 结合单目深度先验与扩散模型修复，恢复合理几何结构
- 适合需要精准编辑3D场景的科研与工业应用
3D场景中的多物体移除因严重遮挡、语义纠缠以及保持几何与多视角一致性困难而具有挑战性。现有3D高斯溅射（3DGS）方法在单物体编辑上表现良好，但在多物体场景下扩展性差，常需重复优化且移除区域几何不稳定。我们提出CoGeo-GS，一种概念驱动的可控多物体移除框架。CoGeo-GS为高斯点分配概念感知的语义标签，实现在单次优化阶段灵活选择物体并减少前景与背景间的干扰。为恢复合理几何，引入几何感知重建流程，结合单目深度先验、基于扩散模型的精修及边界对齐融合。几何正则化精修策略进一步稳定重建并保持多视角一致性。实验表明，CoGeo-GS在视觉质量与重建保真度上优于现有方法。
原文摘要 · Abstract (English)
Multi-object removal in 3D scenes is challenging due to severe occlusions, semantic entanglement, and the difficulty of maintaining geometric and multi-view consistency. Existing 3D Gaussian Splatting (3DGS) methods perform well for single-object editing but scale poorly to multi-object scenarios, often requiring repetitive optimization and yielding unstable geometry in removed regions. We propose CoGeo-GS, a concept-driven framework for controllable multi-object removal in 3D scenes. CoGeo-GS assigns concept-aware semantic tags to Gaussians, enabling flexible object selection and reducing interference between foreground objects and background structures within a single optimization stage. To recover plausible geometry, we introduce a geometry-aware completion pipeline that combines monocular depth priors with diffusion-based refinement and boundary-aligned blending. A geometry-regularized refinement strategy further stabilizes reconstruction and preserves multi-view consistency. Experiments demonstrate that CoGeo-GS outperforms existing methods in visual quality and reconstruction fidelity.
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