用少量语义掩码清除3D高斯点云中的冗余噪点，压缩模型体积。
Clean-GS: Semantic Mask-Guided Pruning for 3D Gaussian Splatting
- 通过投影到语义掩码区域实现白名单筛选
- 压缩率达60%-80%，文件大小从125MB降至47MB
- 仅需3个分割掩码，适合网页与AR/VR部署
3D高斯点绘生成高质量场景重建，但会产生数十万条散乱的冗余高斯点（浮点），遮挡目标物体并增大模型体积，不利于带宽受限场景的应用。本文提出Clean-GS，利用稀疏语义掩码去除3DGS重建中的背景杂波和浮点。该方法结合白名单空间过滤、颜色引导验证与异常值移除，实现60%-80%的模型压缩，同时保持目标物体质量。不同于依赖全局重要性度量的现有剪枝方法，Clean-GS仅需3个分割掩码（占视图总数1%）即可识别并剔除非目标物体的高斯点。其多阶段流程包括：(1) 将高斯点投影至掩码区域进行白名单过滤；(2) 通过深度缓冲的颜色验证；(3) 基于邻域的异常值移除，成功从复杂户外场景中分离出建筑与物体。在Tanks and Temples数据集上的实验表明，模型文件大小由125MB降至47MB，渲染质量保持不变，使3DGS模型适用于网页及AR/VR应用。代码已开源。
原文摘要 · Abstract (English)
3D Gaussian Splatting produces high-quality scene reconstructions but generates hundreds of thousands of spurious Gaussians (floaters) scattered throughout the environment. These artifacts obscure objects of interest and inflate model sizes, hindering deployment in bandwidth-constrained applications. We present Clean-GS, a method for removing background clutter and floaters from 3DGS reconstructions using sparse semantic masks. Our approach combines whitelist-based spatial filtering with color-guided validation and outlier removal to achieve 60-80\% model compression while preserving object quality. Unlike existing 3DGS pruning methods that rely on global importance metrics, Clean-GS uses semantic information from as few as 3 segmentation masks (1\% of views) to identify and remove Gaussians not belonging to the target object. Our multi-stage approach consisting of (1) whitelist filtering via projection to masked regions, (2) depth-buffered color validation, and (3) neighbor-based outlier removal isolates monuments and objects from complex outdoor scenes. Experiments on Tanks and Temples show that Clean-GS reduces file sizes from 125MB to 47MB while maintaining rendering quality, making 3DGS models practical for web deployment and AR/VR applications. Our code is available at https://github.com/smlab-niser/clean-gs
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