DSM: Building A Diverse Semantic Map for 3D Visual Grounding

• URL: http://arxiv.org/abs/2504.08307v1
• Date: Fri, 11 Apr 2025 07:18:42 GMT
• Title: DSM: Building A Diverse Semantic Map for 3D Visual Grounding
• Authors: Qinghongbing Xie, Zijian Liang, Long Zeng,
• Abstract summary: We propose a diverse semantic map construction method specifically designed for robotic agents performing 3D Visual Grounding tasks.<n>This method leverages multimodal large language models (VLMs) to capture the latent semantic attributes and relations of objects within the scene and creates a Diverse Semantic Map (DSM) through a geometry sliding-window map construction strategy.<n> Experimental results show that our method outperforms current approaches in tasks like semantic segmentation and 3D Visual Grounding, particularly excelling in overall metrics compared to the state-of-the-art.
• Score: 4.89669292144966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, with the growing research and application of multimodal large language models (VLMs) in robotics, there has been an increasing trend of utilizing VLMs for robotic scene understanding tasks. Existing approaches that use VLMs for 3D Visual Grounding tasks often focus on obtaining scene information through geometric and visual information, overlooking the extraction of diverse semantic information from the scene and the understanding of rich implicit semantic attributes, such as appearance, physics, and affordance. The 3D scene graph, which combines geometry and language, is an ideal representation method for environmental perception and is an effective carrier for language models in 3D Visual Grounding tasks. To address these issues, we propose a diverse semantic map construction method specifically designed for robotic agents performing 3D Visual Grounding tasks. This method leverages VLMs to capture the latent semantic attributes and relations of objects within the scene and creates a Diverse Semantic Map (DSM) through a geometry sliding-window map construction strategy. We enhance the understanding of grounding information based on DSM and introduce a novel approach named DSM-Grounding. Experimental results show that our method outperforms current approaches in tasks like semantic segmentation and 3D Visual Grounding, particularly excelling in overall metrics compared to the state-of-the-art. In addition, we have deployed this method on robots to validate its effectiveness in navigation and grasping tasks.

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