RADSeg adapts the RADIO model with targeted enhancements to deliver 6-30% higher mIoU in zero-shot OVSS while using 2.5x fewer parameters and running 3.95x faster than prior large-model combinations.
Rayfronts: Open-set semantic ray frontiers for online scene understanding and ex- ploration
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
By detecting trajectory disturbances, attributing them to visual causes with a VLM, and fitting a few-shot spatial disturbance model, robots build personalized danger libraries that improve later navigation.
G-DRAGON framework maps language commands to OSM coordinates via lightweight LLM for global planning and uses frontier exploration for local targets, outperforming baselines in simulation and completing real UGV person-search missions up to 500m.
FUS3DMaps fuses voxel- and instance-level open-vocabulary layers inside a shared 3D voxel map to improve both layers and enable scalable accurate semantic mapping.
PLAF introduces a 2D pixel-wise language-aligned feature extractor paired with a redundancy-reducing storage scheme that supports accurate open-vocabulary 3D scene understanding.
citing papers explorer
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RADSeg: Unleashing Parameter and Compute Efficient Zero-Shot Open-Vocabulary Segmentation Using Agglomerative Models
RADSeg adapts the RADIO model with targeted enhancements to deliver 6-30% higher mIoU in zero-shot OVSS while using 2.5x fewer parameters and running 3.95x faster than prior large-model combinations.
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Don't Fool Me Twice: Adapting to Adversity in the Wild with Experience-Driven Reasoning
By detecting trajectory disturbances, attributing them to visual causes with a VLM, and fitting a few-shot spatial disturbance model, robots build personalized danger libraries that improve later navigation.
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G-DRAGON: Geospatial Reasoning and Dynamic Planning for Retrieval-Augmented Outdoor Navigation
G-DRAGON framework maps language commands to OSM coordinates via lightweight LLM for global planning and uses frontier exploration for local targets, outperforming baselines in simulation and completing real UGV person-search missions up to 500m.
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FUS3DMaps: Scalable and Accurate Open-Vocabulary Semantic Mapping by 3D Fusion of Voxel- and Instance-Level Layers
FUS3DMaps fuses voxel- and instance-level open-vocabulary layers inside a shared 3D voxel map to improve both layers and enable scalable accurate semantic mapping.
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PLAF: Pixel-wise Language-Aligned Feature Extraction for Efficient 3D Scene Understanding
PLAF introduces a 2D pixel-wise language-aligned feature extractor paired with a redundancy-reducing storage scheme that supports accurate open-vocabulary 3D scene understanding.