根据颜色多样性采样点云，让重要纹理更完整。
PRISM: Color-Stratified Point Cloud Sampling
- 以颜色空间为分层依据，按色差分配采样密度
- 相同颜色区域采样少，高色差区保留更多点
- 适合需要保留细节的3D重建任务
我们提出PRISM，一种针对RGB-LiDAR点云的颜色引导分层采样方法。观察发现，独特场景特征常具有丰富的色彩差异，而重复冗余特征则颜色单一。传统下采样方法（随机采样、体素网格、法向空间采样）仅保证空间均匀性，忽略颜色信息。PRISM将RGB颜色空间作为分层域，每个颜色区间最大容量为k，使采样密度与色差成正比。该方法能有效保留高色差纹理区域，显著减少颜色均质表面的点数，将采样重点从空间覆盖转向视觉复杂度，生成更稀疏但关键特征完整的点云，适用于3D重建任务。
原文摘要 · Abstract (English)
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 chromatic 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。