解决3D高斯点云渲染中的动态干扰问题，提升真实场景重建质量。
3DGS-HPC: Distractor-free 3D Gaussian Splatting with Hybrid Patch-wise Classification
- 采用局部空间一致性进行分块分类，增强区域判断鲁棒性。
- 融合光度与感知特征的混合度量，更可靠地区分静态与动态内容。
- 适合需要高精度3D重建的真实世界应用，如自动驾驶、AR/VR。
3D高斯点云（3DGS）在新视角合成与三维场景重建中表现优异，但在真实环境中常因瞬时干扰（如移动物体、变化阴影）导致质量下降。现有方法多依赖预训练视觉模型的语义先验，或用于像素分组，或定义感知误差度量，但语义分组常与静态/动态区分不一致，感知特征在3DGS优化过程中易受外观扰动影响。本文提出3DGS-HPC，结合两种互补机制：基于局部空间一致性的分块分类策略，实现更稳健的区域级决策；以及自适应融合光度与感知线索的混合分类度量，提升分离可靠性。大量实验表明，该方法有效缓解干扰，显著提升3DGS的新视角合成性能。项目主页：https://cnhaox.github.io/3DGS-HPC/
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and 3D scene reconstruction, but its quality often degrades in real-world environments due to transient distractors, such as moving objects and varying shadows. Existing methods commonly introduce semantic priors from pre-trained vision models either to group pixels into coherent regions or to define perceptual error metrics. However, semantic grouping is often misaligned with the binary static/transient distinction, while perceptual features can be fragile under appearance perturbations introduced during 3DGS optimization. We propose 3DGS-HPC, a framework that addresses these issues by combining two complementary principles: a patch-wise classification strategy that leverages local spatial consistency for robust region-level decisions, and a hybrid classification metric that adaptively integrates photometric and perceptual cues for more reliable separation. Extensive experiments demonstrate the superiority and robustness of our method in mitigating distractors to improve 3DGS-based novel view synthesis. Our project page is https://cnhaox.github.io/3DGS-HPC/ .
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