Abstract
Semantic segmentation on point clouds is critical for 3D scene understanding. However, sparse and irregular point distributions provide limited appearance evidence, making geometry-only features insufficient to distinguish objects with similar shapes but distinct appearances (e.g., color, texture, material). We propose Gaussian-to-Point (G2P), which transfers appearance-aware attributes from 3D Gaussian Splatting to point clouds for more discriminative and appearance-consistent segmentation. Our G2P address the misalignment between optimized Gaussians and original point geometry by establishing point-wise correspondences. By leveraging Gaussian opacity attributes, we resolve the geometric ambiguity that limits existing models. Additionally, Gaussian scale attributes enable precise boundary localization in complex 3D scenes. Extensive experiments demonstrate that our approach achieves superior performance on standard benchmarks and shows significant improvements on geometrically challenging classes, all without any 2D or language supervision.
Abstract (translated)
点云的语义分割对于三维场景理解至关重要。然而，稀疏且不规则的点分布提供的外观证据有限，仅依靠几何特征不足以区分形状相似但外观不同（如颜色、纹理、材质）的对象。为此，我们提出了Gaussian-to-Point (G2P) 方法，该方法将基于3D高斯散布的感知属性转移到点云中，以实现更具判别力和外观一致性的分割。 我们的G2P通过建立逐点对应关系解决了优化后的高斯分布与原始点几何之间的对齐问题。利用高斯不透明度属性，我们解决了限制现有模型的几何模糊性。此外，高斯尺度属性使得在复杂的三维场景中能够精确定位边界。 大量的实验表明，我们的方法在标准基准测试中的表现优于其他方法，并且对于几何结构复杂度较高的类别也有显著改进，而无需任何二维或语言监督。
URL
https://arxiv.org/abs/2601.03510
