MAGS-SLAM is the first RGB-only multi-agent 3D Gaussian Splatting SLAM framework that matches RGB-D performance via compact submap sharing, geometry-appearance loop verification, and occupancy-aware fusion.
Gaussian-slam: Photo-realistic dense slam with gaussian splatting
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
WaterSplat-SLAM achieves robust camera tracking and high-fidelity rendering in underwater environments by coupling semantic medium filtering into two-view reconstruction and using an online medium-aware Gaussian map.
NG-GS uses NeRF guidance and RBF interpolation on 3DGS to produce smoother, higher-quality object segmentation boundaries.
citing papers explorer
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MAGS-SLAM: Monocular Multi-Agent Gaussian Splatting SLAM for Geometrically and Photometrically Consistent Reconstruction
MAGS-SLAM is the first RGB-only multi-agent 3D Gaussian Splatting SLAM framework that matches RGB-D performance via compact submap sharing, geometry-appearance loop verification, and occupancy-aware fusion.
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WaterSplat-SLAM: Photorealistic Monocular SLAM in Underwater Environment
WaterSplat-SLAM achieves robust camera tracking and high-fidelity rendering in underwater environments by coupling semantic medium filtering into two-view reconstruction and using an online medium-aware Gaussian map.
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NG-GS: NeRF-Guided 3D Gaussian Splatting Segmentation
NG-GS uses NeRF guidance and RBF interpolation on 3DGS to produce smoother, higher-quality object segmentation boundaries.