CoGeo-GS: Concept-Driven and Geometry-Aware Multi-Object Removal in 3D Scenes

2026-08-27

AI Summary

针对3D场景中多物体移除难题，提出CoGeo-GS框架，通过概念驱动和几何感知方法优化物体选择与几何恢复，提升视觉质量和重建保真度。

Abstract

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.

Problem

• Multi-object removal
• 3D scenes
• Occlusions
• Semantic entanglement
• Geometric consistency

Innovation

• Concept-Driven
• Geometry-Aware
• Multi-Object Removal
• Gaussian Splatting
• Semantic Tags