Indoor scene understanding remains a fundamental challenge in robotics, with direct implications for downstream tasks such as navigation and manipulation. Traditional approaches often rely on closed-set recognition or loop closure, limiting their adaptability in open-world environments. With the advent of visual foundation models (VFMs), open-vocabulary recognition and natural language querying have become feasible, unlocking new possibilities for 3D scene graph construction.In this paper, we propose a robust and efficient framework for instance-level 3D scene graph construction via LiDAR-camera fusion. Leveraging LiDAR's wide field of view (FOV) and long-range sensing capabilities, we rapidly acquire room-level geometric priors. Multi-level VFMs are employed to improve the accuracy and consistency of semantic extraction. During instance fusion, room-based segmentation enables parallel processing, while the integration of geometric and semantic cues significantly enhances fusion accuracy and robustness. Compared to state-of-the-art methods, our approach achieves up to an order-of-magnitude improvement in construction speed while maintaining high semantic precision.Extensive experiments in both simulated and real-world environments validate the effectiveness of our approach. We further demonstrate its practical value through a language-guided semantic navigation task, highlighting its potential for real-world robotic applications.
View on arXiv@article{chen2025_2506.06804,
title={ IRS: Instance-Level 3D Scene Graphs via Room Prior Guided LiDAR-Camera Fusion },
author={ Hongming Chen and Yiyang Lin and Ziliang Li and Biyu Ye and Yuying Zhang and Ximin Lyu },
journal={arXiv preprint arXiv:2506.06804},
year={ 2025 }
}