GaussianGraph: 3D Gaussian-based Scene Graph Generation for Open-world Scene Understanding

• URL: http://arxiv.org/abs/2503.04034v1
• Date: Thu, 06 Mar 2025 02:36:59 GMT
• Title: GaussianGraph: 3D Gaussian-based Scene Graph Generation for Open-world Scene Understanding
• Authors: Xihan Wang, Dianyi Yang, Yu Gao, Yufeng Yue, Yi Yang, Mengyin Fu,
• Abstract summary: We propose a novel framework that enhances 3DGS-based scene understanding by integrating semantic clustering and scene graph generation.<n>We introduce a "Control-Follow" clustering strategy, which dynamically adapts to scene scale and feature distribution, avoiding feature compression.<n>We enrich scene representation by integrating object attributes and spatial relations extracted from 2D foundation models.
• Score: 20.578106363482018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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