A framework for robust 3D segmentation in editable Gaussian Splatting that combines SAM-HQ masks with prior-guided multiview-consistent label assignment to 3D Gaussians.
Emerging properties in self-supervised vision transformers
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MapSR achieves 59.64% mIoU on land cover super-resolution from low-resolution labels alone by prompting frozen vision foundation models and applying training-free inference plus graph refinement.
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Robust Prior-Guided Segmentation for Editable 3D Gaussian Splatting
A framework for robust 3D segmentation in editable Gaussian Splatting that combines SAM-HQ masks with prior-guided multiview-consistent label assignment to 3D Gaussians.
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MapSR: Prompt-Driven Land Cover Map Super-Resolution via Vision Foundation Models
MapSR achieves 59.64% mIoU on land cover super-resolution from low-resolution labels alone by prompting frozen vision foundation models and applying training-free inference plus graph refinement.