3DGSNav: Enhancing Vision-Language Model Reasoning for Object Navigation via Active 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2602.12159v1
• Date: Thu, 12 Feb 2026 16:41:26 GMT
• Title: 3DGSNav: Enhancing Vision-Language Model Reasoning for Object Navigation via Active 3D Gaussian Splatting
• Authors: Wancai Zheng, Hao Chen, Xianlong Lu, Linlin Ou, Xinyi Yu,
• Abstract summary: 3DGSNav is a novel framework that embeds 3D Gaussian Splatting (3DGS) as persistent memory for vision-language models (VLMs) to enhance spatial reasoning.<n>3DGSNav incrementally constructs a 3DGS representation of the environment, enabling trajectory-guided free-viewpoint rendering of frontier-aware first-person views.<n>During navigation, a real-time object detector filters potential targets, while VLM-driven active viewpoint switching performs target re-verification.
• Score: 12.057873540714098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object navigation is a core capability of embodied intelligence, enabling an agent to locate target objects in unknown environments. Recent advances in vision-language models (VLMs) have facilitated zero-shot object navigation (ZSON). However, existing methods often rely on scene abstractions that convert environments into semantic maps or textual representations, causing high-level decision making to be constrained by the accuracy of low-level perception. In this work, we present 3DGSNav, a novel ZSON framework that embeds 3D Gaussian Splatting (3DGS) as persistent memory for VLMs to enhance spatial reasoning. Through active perception, 3DGSNav incrementally constructs a 3DGS representation of the environment, enabling trajectory-guided free-viewpoint rendering of frontier-aware first-person views. Moreover, we design structured visual prompts and integrate them with Chain-of-Thought (CoT) prompting to further improve VLM reasoning. During navigation, a real-time object detector filters potential targets, while VLM-driven active viewpoint switching performs target re-verification, ensuring efficient and reliable recognition. Extensive evaluations across multiple benchmarks and real-world experiments on a quadruped robot demonstrate that our method achieves robust and competitive performance against state-of-the-art approaches.The Project Page:https://aczheng-cai.github.io/3dgsnav.github.io/

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