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This paper introduces Segment-Snap, a method that combines geometric and semantic cues to improve interaction understanding in 3D scenes, achieving significant gains in motion-gated AP and handle detection metrics.
This paper develops a geometric framework to measure semantic content of texts using sentence embeddings, proposing a three-coordinate semantic profile (novelty, breadth, integration) and a scalar trade-off triangle, validated across synthetic categories and novels.