Robotics and Sensor-Based Localization
Open access
Abstract
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
Direct answer
What can I do from this paper page?
Use this page to scan "VCS-SLAM: Geometry-Validated Semantic Evidence Fusion for 3D Gaussian SLAM" quickly: start with the summary and abstract, then check the authors, source, topics, and related papers. From here, open Scollr to follow Robotics and Sensor-Based Localization research, save the paper, or map adjacent work.
Research areas
Follow related topics
Citation
BibTeX
@article{Jha2026SLAM,
title = {VCS-SLAM: Geometry-Validated Semantic Evidence Fusion for 3D Gaussian SLAM},
author = {Rahul Jha and Shuaihang Yuan and Y D Fang},
journal = {arXiv (Cornell University)},
year = {2026},
doi = {10.48550/arxiv.2606.29494},
url = {https://doi.org/10.48550/arxiv.2606.29494}
}FAQ
Using this paper in a discovery workflow
How do I find related work for this paper?
Use the related papers and topic links on this page as starting points. In Scollr, you can also open the paper and build a literature map around its references, citing papers, and related work.
How can I keep up with new Robotics and Sensor-Based Localization research papers?
Follow Robotics and Sensor-Based Localization research in Scollr. New papers from the topic flow into a personalized feed, and you can save useful studies to revisit later.
Can I cite this paper from this page?
This page includes a static BibTeX block for VCS-SLAM: Geometry-Validated Semantic Evidence Fusion for 3D Gaussian SLAM. Always verify the DOI, source, and publication details against the publisher record before submitting a manuscript.
Follow this research in Scollr
Follow the topics and authors behind this paper, save useful studies, and build a literature map when you are ready to go deeper.
Get the app