DISC: 大規模オープンセット意味マッピングのための高密度統合意味コンテキスト
DISC: Dense Integrated Semantic Context for Large-Scale Open-Set Semantic Mapping
画像クロップに頼らず、Vision Transformerの中間層から直接CLIP特徴を抽出する単一パス方式で、大規模環境向けのオープンセット意味マッピングを高速化・高精度化するフレームワークを提案した。
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著者: Felix Igelbrink, Lennart Niecksch, Martin Atzmueller, Joachim Hertzberg
分類: cs.CV, cs.RO
原文アブストラクト
Open-set semantic mapping enables language-driven robotic perception, but current instance-centric approaches are bottlenecked by context-depriving and computationally expensive crop-based feature extraction. To overcome this fundamental limitation, we introduce DISC (Dense Integrated Semantic Context), featuring a novel single-pass, distance-weighted extraction mechanism. By deriving high-fidelity CLIP embeddings directly from the vision transformer's intermediate layers, our approach eliminates the latency and domain-shift artifacts of traditional image cropping, yielding pure, mask-aligned semantic representations. To fully leverage these features in large-scale continuous mapping, DISC is built upon a fully GPU-accelerated architecture that replaces periodic offline processing with precise, on-the-fly voxel-level instance refinement. We evaluate our approach on standard benchmarks (Replica, ScanNet) and a newly generated large-scale-mapping dataset based on Habitat-Matterport 3D (HM3DSEM) to assess scalability across complex scenes in multi-story buildings. Extensive evaluations demonstrate that DISC significantly surpasses current state-of-the-art zero-shot methods in both semantic accuracy and query retrieval, providing a robust, real-time capable framework for robotic deployment. The full source code, data generation and evaluation pipelines will be made available at https://github.com/DFKI-NI/DISC.
