G2P: Gaussian-to-Point Attribute Alignment for Boundary-Aware 3D Segmentation

Point cloud segmentation is critical for 3D scene understand-ing. However, sparse and irregular point distributions provide limitedappearance evidence, making geometry-only features insufficient to dis-tinguish objects with similar shapes but distinct appearances (e.g., color,texture, and material). We propose Gaussian-to-Point (G2P), which trans-fers Gaussian attributes from 3D Gaussian Splatting to point clouds formore discriminative and appearance-consistent segmentation. Our G2Paddresses the misalignment between optimized Gaussians and originalpoint geometry by establishing point-wise correspondences. By distillingopacity-derived visibility cues, we mitigate the geometric ambiguity thatlimits existing models. Additionally, Gaussian scale attributes enableprecise boundary localization in complex 3D scenes. Extensive experi-ments demonstrate that our approach achieves competitive performanceon standard benchmarks and shows notable improvements on geomet-rically challenging classes, without pretrained 2D features or languagesupervision in our segmentation pipeline.