We present PRISM, a novel color-guided stratified sampling method for RGB-LiDAR point clouds. Our approach is motivated by the observation that unique scene features often exhibit chromatic diversity while repetitive, redundant features are homogeneous in color. Conventional downsampling methods (Random Sampling, Voxel Grid, Normal Space Sampling) enforce spatial uniformity while ignoring this photometric content. In contrast, PRISM allocates sampling density proportional to chormatic diversity. By treating RGB color space as the stratification domain and imposing a maximum capacity k per color bin, the method preserves texture-rich regions with high color variation while substantially reducing visually homogeneous surfaces. This shifts the sampling space from spatial coverage to visual complexity to produce sparser point clouds that retain essential features for 3D reconstruction tasks.

翻译：本文提出PRISM，一种面向RGB-LiDAR点云的颜色引导分层采样新方法。该方法的提出基于以下观察：独特的场景特征通常呈现色彩多样性，而重复冗余的特征则具有颜色同质性。传统下采样方法（随机采样、体素网格法、法向量空间采样）强制实现空间均匀性，却忽略了光度信息。与之相反，PRISM根据色彩多样性按比例分配采样密度。该方法将RGB色彩空间作为分层域，并对每个颜色区间设置最大容量k，从而在显著减少视觉同质区域的同时，保留具有高色彩变化的纹理丰富区域。这种策略将采样空间从空间覆盖转向视觉复杂度，最终生成能保留三维重建任务关键特征的更稀疏点云。