MSGNav: Unleashing the Power of Multi-modal 3D Scene Graph for Zero-Shot Embodied Navigation

• URL: http://arxiv.org/abs/2511.10376v2
• Date: Fri, 14 Nov 2025 12:20:45 GMT
• Title: MSGNav: Unleashing the Power of Multi-modal 3D Scene Graph for Zero-Shot Embodied Navigation
• Abstract summary: Real-world deployment requires open vocabulary generalization and low training overhead.<n>Existing zero-shot methods that build explicit 3D scene graphs compress rich visual observations into text-only relations.<n>We introduce the Multi-modal 3D Scene Graph (M3DSG), which preserves visual cues by replacing textual relation.
• Score: 42.4892002514681
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Embodied navigation is a fundamental capability for robotic agents operating. Real-world deployment requires open vocabulary generalization and low training overhead, motivating zero-shot methods rather than task-specific RL training. However, existing zero-shot methods that build explicit 3D scene graphs often compress rich visual observations into text-only relations, leading to high construction cost, irreversible loss of visual evidence, and constrained vocabularies. To address these limitations, we introduce the Multi-modal 3D Scene Graph (M3DSG), which preserves visual cues by replacing textual relation

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