LEXI-SG：基于房间引导前馈重建的单目3D场景图映射
LEXI-SG: Monocular 3D Scene Graph Mapping with Room-Guided Feed-Forward Reconstruction
AI总结：
LEXI-SG是首个仅使用RGB相机输入的开放词汇3D场景图密集映射系统，通过语义先验分割场景为房间，实现可扩展的密集映射。
摘要：
场景图正成为机器人导航的标准表示，提供层次化的几何和语义场景理解。然而，大多数场景图映射方法依赖深度相机或LiDAR传感器。在本文中，我们提出了LEXI-SG，首个基于RGB相机输入的开放词汇3D场景图密集映射系统。我们的方法利用开放词汇基础模型的语义先验，将场景分割为房间，并在每个房间完全观测后延迟前馈重建，从而实现可扩展的密集映射，避免滑动窗口尺度不一致。我们提出基于房间的因素图公式，以全局对齐房间重建，同时保持局部地图一致性，并自然地施加语义场景图层次结构。在每个房间内，我们进一步支持开放词汇物体分割和跟踪。我们在Habitat-Matterport 3D和自采的egocentric办公室序列上验证了LEXI-SG。我们评估其性能，对比现有前馈SLAM方法以及已建立的场景图基线。我们展示了改进的轨迹估计和密集重建，以及在开放词汇分割中的竞争性表现。LEXI-SG表明，仅使用单目RGB即可实现准确、可扩展的开放词汇3D场景图。我们的项目页面和办公室序列可在此获取：https://ori-drs.github.io/lexisg-web/。
英文摘要：
Scene graphs are becoming a standard representation for robot navigation, providing hierarchical geometric and semantic scene understanding. However, most scene graph mapping methods rely on depth cameras or LiDAR sensors. In this work, we present LEXI-SG, the first dense monocular visual mapping system for open-vocabulary 3D scene graphs using only RGB camera input. Our approach exploits the semantic priors of open-vocabulary foundation models to partition the scene into rooms, deferring feed-forward reconstruction to when each room is fully observed -- enabling scalable dense mapping without sliding-window scale inconsistencies. We propose a room-based factor graph formulation to globally align room reconstructions while preserving local map consistency and naturally imposing the semantic scene graph hierarchy. Within each room, we further support open-vocabulary object segmentation and tracking. We validate LEXI-SG on indoor scenes from the Habitat-Matterport 3D and self-collected egocentric office sequences. We evaluate its performance against existing feed-forward SLAM methods, as well as established scene graphs baselines. We demonstrate improved trajectory estimation and dense reconstruction, as well as, competitive performance in open-vocabulary segmentation. LEXI-SG shows that accurate, scalable, open-vocabulary 3D scene graphs can be achieved from monocular RGB alone. Our project page and office sequences are available here: https://ori-drs.github.io/lexisg-web/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
