GaussianGraph: 3D Gaussian-based Scene Graph Generation for Open-world Scene Understanding
This addresses scene understanding for robotics or AR/VR applications, but appears incremental as it builds on existing 3DGS methods.
The paper tackles the problem of low object segmentation accuracy and lack of spatial reasoning in 3D Gaussian Splatting-based scene understanding by proposing GaussianGraph, which integrates adaptive semantic clustering and scene graph generation, outperforming state-of-the-art methods on three datasets.
Recent advancements in 3D Gaussian Splatting(3DGS) have significantly improved semantic scene understanding, enabling natural language queries to localize objects within a scene. However, existing methods primarily focus on embedding compressed CLIP features to 3D Gaussians, suffering from low object segmentation accuracy and lack spatial reasoning capabilities. To address these limitations, we propose GaussianGraph, a novel framework that enhances 3DGS-based scene understanding by integrating adaptive semantic clustering and scene graph generation. We introduce a "Control-Follow" clustering strategy, which dynamically adapts to scene scale and feature distribution, avoiding feature compression and significantly improving segmentation accuracy. Additionally, we enrich scene representation by integrating object attributes and spatial relations extracted from 2D foundation models. To address inaccuracies in spatial relationships, we propose 3D correction modules that filter implausible relations through spatial consistency verification, ensuring reliable scene graph construction. Extensive experiments on three datasets demonstrate that GaussianGraph outperforms state-of-the-art methods in both semantic segmentation and object grounding tasks, providing a robust solution for complex scene understanding and interaction.