GIST is a multimodal knowledge extraction pipeline that transforms mobile point cloud data into semantically-annotated navigation topologies, enabling spatial grounding in cluttered environments like retail stores and warehouses. The system addresses limitations of Vision-Language Models in dense, semantically-complex spaces by combining point clouds with intelligent keyframe selection and semantic overlays. Demonstrated applications include intent-driven semantic search, one-shot localization achieving ~1.04m accuracy, and zone classification.
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GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology
GIST bridges Vision-Language Models' weakness in cluttered indoor spaces by grounding semantic understanding in 3D point clouds, achieving 1.04m localization accuracy for retail and warehouse navigation.
Monday, April 20, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline
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