H2G distills 2D foundation-model affinities into a Lorentz hyperbolic feature field that represents hierarchical 3D groupings at multiple granularities.
A cost function for similarity-based hierarchical clustering
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Classification fields are infinite recursive hierarchical cluster structures generated by a local refinement rule, and a ReLU network predictor learned from finite prefixes can approximate the generator and extend it to deeper levels with exponential convergence in the completed cell metric.
citing papers explorer
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H2G: Hierarchy-Aware Hyperbolic Grouping for 3D Scenes
H2G distills 2D foundation-model affinities into a Lorentz hyperbolic feature field that represents hierarchical 3D groupings at multiple granularities.
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Classification Fields: Arbitrarily Fine Recursive Hierarchical Clustering From Few Examples
Classification fields are infinite recursive hierarchical cluster structures generated by a local refinement rule, and a ReLU network predictor learned from finite prefixes can approximate the generator and extend it to deeper levels with exponential convergence in the completed cell metric.