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
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
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
-
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.
-
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.
-
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.