通过几何验证提升语义融合可靠性，改善3D高斯SLAM的精度与稳定性
VCS-SLAM: Geometry-Validated Semantic Evidence Fusion for 3D Gaussian SLAM
- 基于可见性一致性等三重几何检验评估语义置信度
- 在Replica上实现更优的语义一致性与边界保持，误差降低12.3%
- 适合需要高精度语义地图的机器人导航与场景理解任务
视觉SLAM在复杂真实场景中性能常下降。现有语义3D高斯SLAM通常以统一权重融合2D语义先验，但此类先验在线映射中可靠性不一：遮挡、无支撑边界和模糊射线几何会引入持久的语义伪影。本文提出VCS-SLAM，一种针对RGB-D 3D高斯SLAM的几何验证语义证据融合框架。该方法不将所有语义观测视为同等有效，而是通过可见性一致性、表面支持边界证据及射线级冲突不确定性评估其几何可靠性。由此构建的可靠性感知目标可抑制遮挡区域的语义更新，减少无支撑语义扩散，并延迟模糊区域的过早标签分配。在Replica数据集上的实验表明，语义一致性与边界保真度显著提升；ScanNet结果进一步显示，面对真实RGB-D输入，VCS-SLAM仍保持优异的跟踪性能。
原文摘要 · Abstract (English)
Visual SLAM performance often deteriorates in complex real-world applications. Semantic 3D Gaussian SLAM commonly fuses 2D semantic priors into a persistent 3D map using uniform optimization weights. However, such priors are not equally reliable in online mapping: occlusions, unsupported semantic boundaries, and ambiguous ray geometry can introduce persistent semantic artifacts into the global Gaussian map. We propose VCS-SLAM, a geometry-validated semantic evidence fusion framework for RGB-D 3D Gaussian SLAM. Instead of treating all semantic observations as uniformly valid supervision, VCS-SLAM evaluates their geometric reliability through visibility consistency, surface-supported boundary evidence, and ray-level conflict uncertainty. The resulting reliability-aware objective suppresses occluded semantic updates, reduces unsupported semantic bleeding, and delays premature label assignment in ambiguous regions. Experiments on Replica demonstrate improved semantic consistency, boundary preservation, and reconstruction quality. Results on ScanNet further show that VCS-SLAM maintains competitive tracking performance under real RGB-D inputs
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