IRS: 部屋事前分布に導かれたLiDAR-カメラ融合によるインスタンスレベル3Dシーングラフ構築
IRS: Instance-Level 3D Scene Graphs via Room Prior Guided LiDAR-Camera Fusion
LiDARとカメラを融合し、視覚基盤モデルを活用して部屋単位の幾何事前情報からインスタンスレベルの3Dシーングラフを高速かつ高精度に構築するフレームワークを提案。言語誘導型ナビゲーションで実用性を示す。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Hongming Chen, Yiyang Lin, Ziliang Li, Biyu Ye, Yuying Zhang, Ximin Lyu
分類: cs.RO
原文アブストラクト
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.
