FUS3DMaps: ボクセル層とインスタンス層の3D融合によるスケーラブルで高精度なオープンボキャブラリ意味マッピング
FUS3DMaps: Scalable and Accurate Open-Vocabulary Semantic Mapping by 3D Fusion of Voxel- and Instance-Level Layers
共有ボクセルマップ内で密な層とインスタンス層の両方を維持し、層間の意味融合を行うことで、大規模シーンでも正確なオープンボキャブラリ意味マッピングを実現する手法を提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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6. 次に読むべき論文は？
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著者: Timon Homberger, Finn Lukas Busch, Jesús Gerardo Ortega Peimbert, Quantao Yang, Olov Andersson
分類: cs.RO, cs.AI
原文アブストラクト
Open-vocabulary semantic mapping enables robots to spatially ground previously unseen concepts without requiring predefined class sets. Current training-free methods commonly rely on multi-view fusion of semantic embeddings into a 3D map, either at the instance-level via segmenting views and encoding image crops of segments, or by projecting image patch embeddings directly into a dense semantic map. The latter approach sidesteps segmentation and 2D-to-3D instance association by operating on full uncropped image frames, but existing methods remain limited in scalability. We present FUS3DMaps, an online dual-layer semantic mapping method that jointly maintains both dense and instance-level open-vocabulary layers within a shared voxel map. This design enables further voxel-level semantic fusion of the layer embeddings, combining the complementary strengths of both semantic mapping approaches. We find that our proposed semantic cross-layer fusion approach improves the quality of both the instance-level and dense layers, while also enabling a scalable and highly accurate instance-level map where the dense layer and cross-layer fusion are restricted to a spatial sliding window. Experiments on established 3D semantic segmentation benchmarks as well as a selection of large-scale scenes show that FUS3DMaps achieves accurate open-vocabulary semantic mapping at multi-story building scales. Additional material and code will be made available: https://githanonymous.github.io/FUS3DMaps/.
