VCS-SLAM: Geometry-Validated Semantic Evidence Fusion for 3D Gaussian SLAM

2026-06-28

AI Summary

Existing semantic 3D Gaussian SLAM methods are prone to persistent semantic artifacts in complex scenes due to unreliable 2D semantic priors, manifesting as mislabeled occlusions, boundary leakage, and premature labeling of ambiguous regions. This work proposes a geometry-validated semantic evidence fusion framework that introduces, for the first time, a geometric reliability assessment mechanism. By dynamically evaluating the credibility of semantic observations through visibility consistency, surface support constraints, and ray-wise conflict uncertainty, the method adaptively adjusts optimization weights during inference. This approach effectively suppresses erroneous updates in occluded regions, mitigates geometry-unsupported semantic diffusion, and defers label assignment in ambiguous areas. Experiments demonstrate significant improvements on Replica in semantic consistency, boundary fidelity, and reconstruction quality, while maintaining tracking accuracy on ScanNet comparable to state-of-the-art methods.

Abstract

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

Problem

• Semantic SLAM
• 3D Gaussian Splatting
• Semantic Artifacts
• Geometric Reliability
• RGB-D Mapping

Innovation

• Geometry-Validated Fusion
• Semantic SLAM
• 3D Gaussian Splatting
• Reliability-Aware Optimization
• RGB-D Mapping